Decision Support Laboratory

This is compiled information on Decision Support Systems (DSS) in conjunction with DSL research.

Books

1. Turban, E. and Aronson, J. (2001) Decision Support Systems and Intelligent Systems, 6th edn, Prentice Hall.

Journal Articles

1. Poh, K. (2000) An Intelligent Decision Support System for Invetment Analysis, Knowledge and Information Systems, 2, pp. 340-358.

Conference Papers

1. Colantonio, S., Martinelli, M., Moroni, D., Salvetti, O., Chiarugi, F. and Emmanouilidou, D. (2009) A Decision Support System for Aiding Heart Failure Management, in Proceedings of 2009 Ninth International Conference on Intelligent Systems Design and Applications, 30 November – 2 December, Pisa, Italy, pp. 351-356.
2. Koppar, A. R. and Sridhar, V. (2009) A Workflow Solution for Electronic Health Records to Improve Healthcare Delivery Efficiency in Rural India, in Proceedings of 2009 International Conference on eHealth, Telemedicine, and Social Medicine, 1 – 7 February, Cancun, Mexico, pp. 227-232.
3. Tsumoto, S., Hirano, S., Abe, h., Nakakuni, H. and Hanada, E. (2005) Clinical Decision Support based on Mobile Telecommunication Systems, in Proceedings of The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI’05), 19 – 22 September, Compiegne University of Technology, France.

Electronic Resources

1. Power, D. J. (2007) A Brief History of Decision Support Systems, DSSResources.COM, http://DSSResources.COM/history/dsshistory.html Accessed: 24 December 2009.

This is compiled information on e-Tourism in conjunction with DSL research.

For sustainable tourism development, there is a need for present tourists and making regions accessible, and also a need for all other factors such as political stability, healthy economics, social cohesion, cultural integrity and heritages. We look at the relationship between tourism, culture and arts in the direction of sustainable e-tourism development. Figure 1 shows our viewpoint of e-tourism with its related value chains. E-tourism works as a gateway to interconnect arts, culture and tourism. In our multi-disciplinary approach, by applying Information and Communication Technology (ICT), Knowledge Management (KM) and Corporate Governance (CG) to e-tourism, the expected results will demonstrate improved opportunities for strengthening social cohesion and mutual understanding of local arts and cultural inheritances. In turn, the socially cohesive community offers members better opportunities to participate in the political, social and economic development of sustainable tourism. As a result, they can earn the benefits from it. Moreover, the strengthened community brings positive ripple effects on tourism such as gaining local expertise, sharing local knowledge, and promoting cultural inheritances towards sustainable tourism in the next decade.

Figure 1. Visualizing e-Tourism relationships of the value chain

In relation to our research interests of open innovation, to advance sustainable development in the long term, thus, positive ripple effects should not be under-estimated and but also should be taken into consideration along with transparent development activities. In this practice, open innovation may bring internal and external people to new opportunities and added-values to the market (Drucker 1985). It is a sound approach when there is an uncertain demand for tourism goods and services. Meantime, there is still need for participation of many different parties to enable leadership in open innovation communities (e.g. tourism industries, government sectors) to gain mutual benefits. Traditionally closed innovation involves only internal people within the business.

Books

1. Drucker, P. (1985) Innovation and Enterpreneurship: Practice and Principles, Butterworth-Heinemann.

Journal Articles

1. Isaac, A., Schlobach, S., Matthezing, H. and Zinn, C. (2008) Integrated access to cultural heritage resources through representation and alignment of controlled vocabularies, Library Review, 57(3), pp. 187-199.

Conference Papers

1. Egger, R., Hörl, J. Jellinek, B. and Jooss, M. (2007) Virtual Tourism Content Network TANDEM – A Prototype for the Austrian Tourism Industry, in Proceedings of the fourteenth International Conference on Information and Communication Technologies in Travel and Tourism, 24 – 26 January, Ljubljana, Slovenia, pp. 175-184.
2. Lo, M. and Law, R. (2007) Towards a Web-based Marketing Process Framework for City-based National Tourism Organizations, in Proceedings of the fourteenth International Conference on Information and Communication Technologies in Travel and Tourism, 24 – 26 January, Ljubljana, Slovenia, pp. 353-364.
3. Michopoulou, E., Buhalis, D., Michailidis, S. and Ambrose, I. (2007) Destination Management Systems: Technical Challenges in Developing an eTourism Platform for Accessible Tourism in Europe, in Proceedings of the fourteenth International Conference on Information and Communication Technologies in Travel and Tourism, 24 – 26 January, Ljubljana, Slovenia, pp. 301-310.
4. Waters, J. (2008) Social Network Behavior, Thought-Leaders and knowledge building In An Online Learning Community, in Proceedings of the 41st Hawaii International Conference on Systems Sciences (HICSS2008), 7 – 10 January, Hilton Waikoloa Village Resort Waikoloa, Big Island, Hawaii, pp. 1530-1605.

Report

1. GSTC Partnership, (2008) The Criteria, The Global Partnership for Sustainable Tourism Criteria, http://www.sustainabletourismcriteria.org/index.php?option=com_content&task=view&id=58&Itemid=188 Accessed: 10 November 2008.
2. The United National Department of Economic and Social Affairs (2002) GLOBAL CHALLENGE GLOBAL OPPORTUNITY TRENDS IN SUSTAINABLE DEVELOPMENT, The United National, 24 pages.
3. WTO (2004) Concepts & Definitions: Sustainable Development of Tourism, the World Tourism Organization, http://www.world-tourism.org/frameset/frame_sustainable.html Accessed: 5 October 2008.

This is compiled information on open innovation in conjunction with DSL research.

Information and Communication Technology (ICT) is able to support and automate more of the activities that go on in businesses. Some of the most important technology opportunities will not involve making new technologies (invention), but in figuring out new ways to use technologies (innovation). We believe that finding and exploiting the most promising of these new opportunities can give us significant advantages in many ways. That is, it will give noticeable impacts on how we live in a rapid changing world. In open innovation research, we focus on reshaping a working environment to generate added and new values by brainstorming new ideas and building knowledge with collaboration and cooperation.

What is Open Innovation?

• Peter Drucker, “In business, innovation rarely springs from a gash of inspiration. It arises from a cold-eyed analysis of seven kinds of opportunities.” – unexpected occurrences, incongruities, process needs, industry and market changes, dernographic changes, changes in perception, and new knowledge
• Henry Chesbrough, “…Companies can no longer keep their own innovations secret unto themselves; … the key to success is creating, in effect, an open platform around your innovations so your customers, your employees and even your competitors can build upon it, because only by that building will you create an ongoing, evolving community of users, doers and creators.”

Books

1. Chesbrough, H. (2006) Open Business Models: How to Thrive in the New Innovation Landscape, 1st edn, Harvard Business School Press.
2. Chesbrough, H. (2006) Open Innovation: The New Imperative for Creating And Profiting from Technology, 1st edn, Harvard Business School Press.
3. Drucker, P. (1985) Innovation and Enterpreneurship: Practice and Principles, Butterworth-Heinemann.
4. Goldman, R. and Gabriel, R. (2005) Innovation Happens Elsewhere: Open Source as Business Strategy, Morgan Kaufmann.
5. Harvard Business Review on Innovation (2001) Harvard Business School Press, Boston.
6. Herzog, P. (2008) Open and Closed Innovation: Different Cultures for Different Strategies, Gabler.
7. Rogers, E. (1995) Diffusion of Innovations, 4th edn, Free Press.
8. Yoshino, M. (1968) Japan’s Managerial System: Tradition and Innovation, The MIT Press, Cambridge, Massachusetts, and London.

Conference Papers

1. Potgieter, B. (2004) Change and Innovation We Expect of ICT Teaching Staff, in Proceedings of the Sixth Conference on Australasian Computing Education, 18 – 22 January, Dunedin, New Zealand, pp. 247-253.

Journal Articles

1. Chesbrough, H. (2003) The Era of Open Innovation, Sloan Management Review, 44(3), pp. 35-41.
2. Chesbrough, H. (2003) A Better Way to Innovate, Harvard Business Review, 81(7), pp. 12-14.
3. Chesbrough, H. (2003) Open Platform Innovation: Creating Value from Internal and External Innovation, Intel Technology Journal, 7( 3), pp. 5-9.
4. Chesbrough, H. (2004), Managing Open Innovation: Chess and Poker, Research-Technology Management, 47(1), pp. 23-26.
5. Chesbrough, H. and Appleyard, M. (2007) Open Innovation and Strategy, California Management Review, 50(1), pp. 57-76.
6. Chesbrough, Henry and Crowther, A. (2006) Beyond high tech: early adopters of open innovation in other industries, R&D Management, 36(3), pp. 229-236.
7. Christensen, J., Michael Olesen, H. and Kj?r, J. (2005) The Industrial Dynamics of Open Innovation – Evidence from the transformation of consumer electronics, Research Policy, 34(10), pp. 1533-1549.
8. Cooke, P. (2005) Regionally asymmetric knowledge capabilities and open innovation: Exploring ‘Globalisation 2′ – A new model of industry organisation, Research Policy, 34(8), pp. 1128-1149.
9. Drucker, P. (1988) The Coming of the New Organization, Harvard Business Review on Knowledge Management, January-February(88105).
10. Drucker, P. (2002) The Discipline of Innovation, Harvard Business Review, August (2002), pp. 95-103.
11. Gassmann, O. (2006) Opening up the innovation process: towards an agenda, R&D Management, 36(3), pp. 223-226.
12. Kirschbaum, R. (2005) Open Innovation in Practice, Research-Technology Management, 48(4), pp. 24-28.
13. Laursen, K., and Salter, A. (2006) Open for Innovation: The role of openness in explaining innovation performance among UK manufacturing firms, Strategic Management Journal, 27(2), pp. 131-150.

Report

1. Fredberg, T. Elmquist, M. and Ollila, S. (2008) Managing Open Innovation – Present Findings and Future Directions, VINNOVA Report VR 2008:02, VINNOVA – Verket för Innovationssystem/Swedish Governmental Agency for Innovation Systems, 64 pages.

This is compiled information on Case-Based Reasoning in conjunction with DSL research.

The research on CBR (Case-Based Reasoning) can be traced back to the work of Roger Schank’s (1982) dynamic memory and Memory Organisation Packet (MOP). According to Schank (1982, p.2), ‘a dynamic memory is one that can change its own organization when new experiences demand it. A dynamic memory can learn.’ It is the way we deal with new problem by observing new information to generalise new solution from past experiences. By understanding new problem, we can dynamically solve our new problem to reflect our experiences. A MOP is ‘information about how memory structures are ordinarily linked in frequently occurring combinations’ (Schank 1982, p.83). It is an important approach in which past experience is structured. It values past experience which is not often integrated in the computing systems. These past experiences are used to interpret new inputs using the most closely related past cases.

Using cases in CBR, it is possible to provide better knowledge sharing and reuse solutions because CBR cycle involves revision and refinement phases (See Figure 1). In general, there are two parts to a case (Kolodner 1993, p.18): the first part is the lesson(s) it teaches, and the second part is the context in which it can teach its lesson(s). According to Kolodner (1993, p.3), ‘a case is a conceptualised piece of knowledge representing an experience that teaches a lesson fundamental to achieving the goals of the reasoned.’ Therefore, a case or problem situation can be defined as a conceptualised part of knowledge representing past experience.

In general, CBR refers to a problem-solving paradigm that relies on case representation, instead of only relying on general knowledge of a problem domain. Case representation in a CBR system includes a detailed problem description and a detailed solution description. Within a case representation, most types of data can be stored in a case. For example, stored data in a relational database, photographs, sound, and video can be represented in a case. However it may be difficolt to represent large amount of inter-related data in a case. Therefore the functionality and acquisition of information need to be clarified first before deciding what shoold be represented in cases. Watson (1997, p.19) points out what information shoold be in a case using two pragmatic measures: the functionality of the information and the ease of acquisition of the information. In fact, CBR is dependent on the structure and collected case in case repository, so it is important to have a mechanism that organises information that can be retrieved when it needs. Case representation shoold have standardised mechanism that is supportable, suitable and appropriate to support case retrieval.

There are four phases in the CBR cycle: retrieve, reuse, review and retain as shown in Figure 1.

Figure 1 CBR Cycle, (Source: Watson 1997, p.17)

Books

1. Avramenko, Y. and Kraslawski, A. (2008) Case Based Design: Applications in Process Engineering, 1st edn, Springer.
2. Kolodner, J. (1993) Case-Based Reasoning, Morgan Kaufman Publisher, Inc, San Mateo, CA.
3. Leake, D. B. (1996) Case-Based Reasoning: Experiences, Lessons, and Future Directions, AAAI Press.
4. Leake, D. B. and Plaza, E. (eds.) (1997) Case-Based Reasoning Research and Development: Proceedings of the Second International Conference on Case-Based Reasoning, Springer Verlag, Berlin.
5. Schade, M. (2007) Using Case-based Reasoning to Control Traffic Consumption, VDM Verlag Dr. Mueller e.K.
6. Schank, R. C. (1982) Dynamic memory: a theory of remining and learning in computers and people, Combridge Univeristy Press, New York.
7. Shiu, S. and Pal, S. K. (2004) Foundations of Soft Case-Based Reasoning, 1st edn, Wiley-Interscience.
8. Sun, Z. and Finnie, G. (2004) Intelligent Techniques in E-Commerce: A Case Based Reasoning Perspective, 1st edn, Springer.
9. Watson, I. D. (1997) Applying Case-Based Reasoning: Techniques for Enterprise Systems, Morgan Kaufmann, San Francisco, California.

Conference Papers

1. Aha, D. (1999) The AAAI-99 KM/CBR Workshop: Summary of Contributions, in Proceedings of the Sixteenth National conference on Aritificial Intelligence Workshop on Exploring Synergies of Knowledge Management and Case-Baed Reasoning 18 – 22 July, Orland, Florida, USA.
2. Blythe, j. and Russ, t. (2008) Case-based reasoning for procedure learning by instruction, in Proceedings of the 13th international conference on Intelligent user interfaces, 13 – 16 January, Gran Canaria, Spain, pp. 301-304.
3. Dubitzky, W., Buchner, G. and Azuaje, J. (1999) Viewing Knowledge Management as a Case-Based Reasoning Application, in Proceedings of the AAAI-99 Workshop on Exploring Synergies of Knowlege Management & Case-Based Reasoning Munich, Germany.
4. Gary, P. and Mann, S. (2003) The Fraunce (1588) model of case-based reasoning, in Proceedings of the 9th international conference on Artificial intelligence and law, 24 – 28 June, Scotland, United Kingdom, pp. 89-90.
5. Kolodner, J. and Nagel, K. (1999) The design discussion area: a collaborative learning tool in support of learning from problem-solving and design activities, in Proceedings of the 1999 Conference on Computer Support for Collaborative Learning, 12 – 15 December, Palo Alto, California, U.S.A., No. 37.
6. Magaldi, R. (1999) A Case-Based Approach to the Management and Development of Knowledge Assests, In Proceedings of the International Conference on Case-Baed Reasoning, 27 – 30 Joly, Monastery Seeon, Munich, Germany.
7. Mendes, E., Mosley, N. and Watson, I. (2002) A comparison of case-based reasoning approaches, in Proceedings of the 11th international conference on World Wide Web, 7 – 11 May, Honolulu, Hawaii, USA, pp. 272-280.
8. Menkovski, V. and Metafas, D. (2008) AI Model for Computer games based on Case Based Reasoning and AI Planning, in Proceedings of the 3rd international conference on Digital Interactive Media in Entertainment and Arts, 10 – 12 September, Athens, Greece, pp. 295-302.
9. Mota, J., Câmara, G., Fonseca, L, Escada, M. and Bittencourt, O. (2008) Applying case-based reasoning in the evolution of deforestation patterns in the Brazilian Amazonia, in Proceedings of the 2008 ACM symposium on Applied computing, 16 – 20 March, Fortaleza, Ceara, Brazil, pp. 1683-1687.
10. Moussavi, M. (1999) A Case-Based Approach to Knowledge Management, In Proceedings of the International Conference on Case-Based Reasoning, 27 – 30 Joly, Monastery Seeon, Munich, Germany.
11. Watson, I. (1997) Applying Case-Based Reasoning: Techniques for Enterprise Systems, Morgan Kaufmann, San Francisco, California.
12. Vlado, M. and Dimitrios, M. (2008) AI Model for Computer games based on Case Based Reasoning and AI Planning, in Proceedings of the 3rd International Conference on Digital Interactive Media in Entertainment and Arts, September 10 – 12, Athens, Greece, pp. 295-302.

Journal Articles

1. Aamodt, A. and Plaza, E. (1994) Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches, Artificial Intelligence Communications, 7(1), pp. 39-59.
2. Jurisica, I., Mylopoulos, J., Glasgow, j., Shapiro, H. and Casper, R. (1998) Case-Based Reasoning in IVF: Prediction and Knowledge Mining, Journal of Artificial Intelligence in Medicine, 12(1), pp. 1-24.
3. Jurisica, I. and Nixon, B. (1998) Building Quality into Case-Based Reasoning Systems, in Proceedings of the 10th International Conference on Advanced Information Systems Engineering, 8 – 12 June, Pisa, Italy, pp. 363-380.
4. Kolodner, J. (1983) Reconstructive memory, a computer model, Congnitive Science, 7, pp. 281-328.
5. Roberson, S. (2002) A tale of two knowledge-sharing systems, Journal of Knowledge Management, 6(3), pp. 295-308.

This is compiled information on Belief Revision in conjunction with DSL research.

Belief revision is a process of dealing with changes in one state of belief to another state of belief when new information is acquired. It involves changing the state of existing beliefs to another as a result of external input of new information. There are two approaches to belief revision: foundation and coherence theories. The first approach of foundation theory focuses on a way of verification though justification for one’s beliefs when new information is accepted or added to existing beliefs. The second approach is coherence theory which focuses on assessing relationships between new and existing beliefs without the process of verification as found in the foundation theory. The concept of epistemic entrenchment is important. It refers to the degree of importance on some beliefs (Gärdenfors 1990). It means that certain belief can be considered more important than the others if additional evidence or information is available. This is an important concept for the coherence theory when evaluating relationships between new and existing beliefs to ensure minimum lost of information (Rott 2003).

Books

1. Ditmarsch, H., Hoek, W. and Kooi, B. (2007) Dynamic Epistemic Logic, 1st edn, Springer.
2. Gärdenfors, P. (1988) Knowledge in Flux: Modeling the Dynamics of Epistemic States, The MIT Press, London.
3. Gärdenfors, P. (1990) The Dynamics of Belief Systems: Foundations vs. Coherence Theories, in Revue Internationale de Philosophie, in Brennan, G. and Walsh, C., (eds.), Cambridge University Press, Cambridge, pp. 24-46.
4. Gärdenfors, P. (ed.) (2003) Belief Revision, Cambridge University Press.
5. Swain, M. (1970) Induction, Acceptance, and Rational Belief, D. Reidel Publishing Company, Dordrecht, Holland.
6. Williams, M. and Rott, H. (2001) Frontiers in Belief Revision, 1st edn, Springer.

Conference Papers

1. Crave, R. M., Cachopo, P. J. o., Cachopo, C. A. and Martins, P. J. o. (2001) Permissive Belief Revision, in Proceedings of the 10th Portuguese International Conference on Artificial Intelligence, 17 – 20 December, Porto, Portugal, pp. 335-348.
2. Doyle, J. (1979) A Glimpse of Truth Maintenance, in Proceedings of the Sixth International Joint Conference on Artificial Intelligence, 20 – 23 August, Tokyo, pp. 232-237.
3. Rao, A. S. and Foo, N. Y. (1989) Minimal Change and Maximal Coherence: A Basis for Belief Revision and Reasoning about Actions, in Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, 20 – 25 August, Detroit, MI, USA, pp. 966-971.
4. Nebel, B. (1989) A Knowledge Level Analysis of Belief Revision, in Proceedings of the 1st International Conference on Principles of Knowledge Representation and Reasoning, 15 – 18 May, Toronto, Canada, pp. 301-311.
5. Zhang, D., Foo, N., Meyer, T. and Kwok, R. (2004) Negotiation as Mutual Belief Revision, in Proceedings of the 19th National Conference on Artificial Intelligence, 25 – 29 July, San Jose, California, pp. 317-322.

Journal Articles

1. Benferhat, S. Dubois, P., Didier, H. and Williams, M. (2002) A Practical Approach to Revising Prioritized Knowledge Bases, Studia logica, 70(1), pp. 105-130.
2. Dragoni, A. and Giorgini, P. (2002) Distributed Belief Revision, Journal of Autonomous Agents and Multi-Agent Systems, 6(2), pp. 115-143.
3. Fuhrmann, A. and Hansson, O. (1994) A survey of moltiple contractions, Journal of Logic, Language and Information, 3(1), pp. 39-76.
4. Makinson, D. (1986) How to give it up: A survey of some formal aspects of the logic of theory change, Synthese, 62(1), pp. 185-186.
5. Peppas, P. and Williams, M. (1995) Constructive modelings for theory change, Notre Dame Journal of Formal Logic, 36(1), pp. 120-133.
6. Schulte, O. (2002) Minimal belief change, Pareto-optimality and logical consequence, Economic Theory, 19(1), pp. 105-144.
7. Tennant, N. (2006) New Foundations for a Relational Theory of Theory-revision, Journal of Philosophical Logic, 35(5), pp. 489-528.
8. Tohmé, F. and Sandholm, T. (1999) Coalition Formation Processes with Belief Revision among Bounded-Rational Self-Interested Agents, Journal of Logic Computation, 9(6), pp. 793-815.

Reports

1. McAllester, D. (1978) A Three Valued Truth Maintenance System, A.I. Memo 473, Massachusetts Institute of Technology AI Lab, Cambridge, MA., 31 pages.
2. Rott, H. (2003) Economics and Economy in the Theory of Belief Revision (Preliminary Report), Electronic Notes in Theoretical Computer Science, 84, 15 pages.
3. Segal, R. (1994) Belief Revision, Department of Computer Science and Engineering, FR-35. University of Washington, http://citeseer.nj.nec.com/segal94belief.html Accessed: 12 February 2003.

This is compiled information on Corporate Governance & Information and Communication Technology in conjunction with DSL research.

According to Shleifer and Vishny (1997, p. 737), Corporate Governance (CG) deals with the ways in which suppliers of finance to corporations assure themselves of getting a Return on Investment (ROI). It mainly focuses on maximizing benefits for shareholders and stakeholders. Thus conflicts of interests can be incurred among stakeholders, customers, employees and investors. For this reason, there are doubts against self-published social performance measurements in addressing corporate social responsibilities. The business may prioritize issues that are less costly and trivial due to operational difficolties or its little impact for the business. On the other hand, social responsibility can resolt in social opportunities and competitive advantages by prioritizing social issues (Porter and Kramer, 2006). Recently Information Communication and Technology (ICT) industries turn above challenges into advantages by promoting reputation and image of corporation. To advance sustainable development in the long term, thus, positive ripple effects shoold not be under-estimated and but also should be taken into consideration along with transparent development activities.

Books

1. Clarke, T. (2007) International Corporate Governance: A Comparative Perspective, 1st edn, Routledge.
2. Macey, J. (2008) Corporate Governance: Promises Kept, Promises Broken, Princeton University Press.
3. Monks, R. and Minow, N. (2008) Corporate Governance, 4th edn, Wiley-Blackwell.
4. Salmon, W., Lorsch, W., Donaldson, G. and Pound, J. (2000) Harvard Business Review on Corporate Governance, 1st edn, Harvard Business School Press.

Journal Articles

1. Bonsón, E., Cortijo, V. and Escobar, T. (2009) Towards the global adoption of XBRL using International Financial Reporting Standards (IFRS), International Journal of Accounting Information Systems, 10(1), pp. 46-60.
2. Cormier, D., Ledoux, M.-J. and Magnan, M. (2009) The use of Web sites as a disclosure platform for corporate performance, International Journal of Accounting Information Systems, 10(1), pp. 1-24.
3. Porter, M. and Kramer, M. (2006) Strategy and Society: The link Between Competitive Advantage and Corporate Social Responsibility, Harvard Business Review, 84(12), pp. 78-92.
4. Shleifer, A. and Vishny, R. (1997) A Survey of Corporate Governance, Journal of Finance, American Finance Association, vol. 52(2), pp. 737-783.
5. Hannola, M. and Pirttimaki, V. (2003) Business intelligence empirical study on the top 50 Finnish companies, Journal of American Academy of Business, 2(2), pp. 593-600.

This is compiled information on agent technology in conjunction with DSL research.

In computing, an agent can be a computer program that is capable of acting on your behalf intelligently. In fact, the agent’s levels of intelligence is various in the literature. Russell and Norving note (1995, p.33) that “an agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors.” According to McCarthy and Hayes (1995, p. 1), “the world must be have a general representation of the world in terms of its inputs and interpreted.” At the end, the agent must be able to take action according to the event. This action is not just pressing a “ENTER” key on the keyboard, it is a result from the event that the agent relies on and behaves rationally. Thus Agent Technology covers the discipline dealing with agents of applying knowledge to practical problems.

Books

1. Annicchiarico, R., Cortés, U. and Urdiales, C. (2008) Agent Technology and e-Health, 1st edn, Birkhäuser Basel.
2. Belifemine, F., Caire, G. and Greenwood, D. (2007) Developing Multi-Agent Systems with JADE, Wiley.
3. Brenner, W., Zarnekow, R. and Wittig, H. (1998) Intelligent Software Agents: Foundations and Applications, Springer.
4. Cervenka, R. and Trencansky, I. (2007) The Agent Modeling Language – AML: A Comprehensive Approach to Modeling Multi-Agent Systems, 1st edn, Birkhäuser Basel.
5. d’Inverno, M. and Luck, M. (2003) Understanding Agent Systems, 2nd edn, Springer.
6. Fasli, M. (2007) Agent Technology For E-Commerce, Wiley.
7. Jennings, N. and Wooldridge, M. (2002) Agent Technology: Foundations, Applications, and Markets, Springer.
8. Klügl, F., Bazzan, A. and Ossowski, S. (2005) Applications of Agent Technology in Traffic and Transportation, 1st edn, Birkhไuser Basel.
9. Padgham, L. and Winikoff, M. (2004) Developing Intelligent Agent Systems: A Practical Guide, Wiley.
10. Parsons, S., Gymtrasiewicz, P. and Wooldridge, M. (eds.) (2002) Game Theory and Decision Theory in Agent-Based Systems, 1st edn, Springer.
11. Protogeros, N. (2007) Agent and Web Service Technologies in Virtual Enterprises, IGI Global.
12. Rouff, C., Hinchey, M., Rash, J. Truszkowski, W. and Gordon-Spears, D. (eds.) (2005) Agent Technology from a Formal Perspective, 1st edn, Springer.
13. Russell, S. and Norvig, P. (2002) Artificial Intelligence: A Modern Approach, 2nd edn, Prentice-Hall.
14. Russell, S. and Norvig, P. (1995) Artificial Intelligence: A Modern Approach, 1st edn, Prentice-Hall.
15. Suqumaran, V. (2008) Distributed Artificial Intelligence, Agent Technology, and Collaborative Applications, Information Science Reference.
16. Tamma, V. Cranefield, S., Finin, T. and Willmott, S. (2005) Ontologies for Agents: Theory and Experiences, 1st edn, Birkhไuser Basel.
17. Unland, R., Klusch, M. and Calisti, M. (2005) Software Agent-Based Applications, Platforms and Development Kits, 1st edn, Birkhäuser Basel.
18. Weiss, G. (ed.) (2000) Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, The MIT Press.

Conference Papers

1. Aart, C., Pels, R., Caire, G. and Bergenti, F. (2002) Creating and Using Ontologies in Agent Communication, in Proceedings of the 1st International Joing Conference on Autonomous Agents and Multi-Agent Systems, 15 – 19 July, Bologna, Italy.
2. Broersen, J., Dastani, M., Julstijn, J., Huang, Z. and Torre, L. (2001) The BOID Architecture: Conflicts Between Beliefs, Obligations, Intentions and Desires, in Proceedings of the Fifth nternational Conference on Autonomous Agents, 28 May – 1 June, Montreal, Canada, pp. 9-16.
3. Dickinson, I. (2003) Towards Practical Reasoning Agents for the Semanitc Web, in Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems, 14 – 18 July, Melbourne, Australia, pp. 827-834.
4. Juan, T. and Sterling, L. (2004) Achieving Dynamic Interfaces with Agent Concepts, in Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 19 – 23 July, New York, pp. 19-23.
5. Luke, S., Spector, L., Ranger, D. and Hendler, J. (1997) Ontology-based Web Agents, in Proceedings of the first International Conference on Autonomous Agents, 05 – 08 February, Marina del Rey, CA, USA, pp. 59-66.
6. Morley, D. and Myers, K. (2004) The SPARK Agent Framework, in Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 19 – 23 July, New York, pp. 714 – 721.
7. Olivia, C., Chang, C.-F., Enguix, C. and Ghose, A. (1999) Case-Based BDI Agents: an Effective Approach for Intelligent Search on the World Wide Web, in Proceedings of the AAAI Spring Symposium on Intelligent Agents, 22 – 24 March, Standford University, USA.
8. Williams, A. (2001) Agents Teaching Agents to Share Meaning, in Proceedings of the Fifth International Conference on Autonomous Agents, 28 May – 1 June, Montreal, Canada, pp. 465-472.

Journal Articles

1. Albers, M., Jonker, C. and Karami, M. (2004) Agent Models and Different User Ontologies for an Electronic Market Place, Knowledge and Information Systems, 6, pp.1-41.
2. Bigus, P. J., Schlosnagle, A. D., Pilgrim, R. J., Mills III, N. W. and Diao, Y. (2002) ABLE: A toolkit for building multiagent autonomic systems, IBM Systems Journal 41(3), pp. 350-371.
3. Franklin, S. and Graesser, A. (1996) Is it an Agent, or just a Program?: A Taxonomy for Autonomous Agents, in Proceedings of the Third International Workshop on Agent Theories, Architectures, and Language 12 – 13 August, Budapest, Hungary, http://www.dfki.uni-sb.de/~jpm/atal96.html
4. Hayes-Roth, B. (1995) An Architecture for Adaptive Intelligent Systems, Artificial Intelligence: Special Issue on Agents and Interactivity, 72(1-2), pp. 329-365.
5. Hindriks, V. K., Boer, S. d. F., Hoek, v. d. W. and Meyer, C. J.-H. (1999) Agent Programming in 3APL, Autonomos Agents and Multi-Agent Systems 2(4), pp. 357-401.
6. Joen, H., Petrie, C. and Cutkosky, R. M. (2000) JATLite: A Java Agent Infrastructure with Message Routing, IEEE Internet Computing 4(2), pp. 87-96.
7. Maes, P. (1995) Artificial Life Meets Entertainment: Life Like Autonomous Agents, Communications of the ACM 38(11), pp. 108-114.
8. Shoham, Y. (1993) Agent Oriented Programming, Artificial Intelligence, 60(1), pp. 51-92.
9. Smith, D., Cypher, A. and Spohrer, J. (1994) KidSim: Programming Agents Without a Programming Language, Communications of the ACM, 37(7), pp. 55-67.
10. Virdbagriswaran, S., Osisek, D. and O’Connor, P. (1995) Standardizing Agent Technology, StandardView, 3(3), pp. 96-101.
11. Wooldridge, J. M. and Jennings, R. N. (1995) Intelligent Agents: Theory and Practice, The Knowledge Engineering Review, 10(2), pp. 115-152.

Online Resources: tools, framework, language

1. 3APL (triple-a-p-l) – a programming language for implementing cognitive agents, http://www.cs.uu.nl/3apl/ Accessed: 29 May 2009.
2. AgentBuilder – an integrated software toolkit to develop intelligent software agents and agent-based applications, http://www.agentbuilder.com/, Accessed: 29 May 2009.
3. JADE (Java Agent DEvelopment Framework) – a software Framework that allows the development of multi-agent systems, http://jade.tilab.com/ Accessed: 29 May 2009.
4. The Open Agent Architecture – a framework for integrating software agents in a distributed environment, http://www.ai.sri.com/~oaa/ Accessed: 29 May 2009.

This is compiled information on Knowledge Management in conjunction with DSL research.

The term knowledge management can be seen as management of knowledge related activities. These activities include broad and multi-dimensional aspects of the enterprise’s activities (Wiig 1997).

There are different approaches in which knowledge management is used in the organisations. A repository model approach focuses on managing information and reusing knowledge in tangible formats. A legal approach views knowledge management for making a direct connection between an organisation’s intellectual assets (Barclay and Murray 1997). It involves intellectual properties such as copyright, patents and trademarks. In a business intelligence approach, knowledge management is regarded as a process to produce valuable up-to-date information for operative and strategic decision-making (Hannola and Pirttimaki 2003). With the help of business intelligence, organisations achieve competitive advantages in a rapidly changing business environment by utilising intelligence. Other approaches include the cognitive and continuous learning approaches. These approaches involve an individual ability’s to acquire continuous and ongoing renewal of organisational information, and reuse it for problem solving and decision-making. In addition, cognitive approach of knowledge management focuses on learning within groups as well as individual’s learning level.

Books

1. Becerra-Fernandez, I., Gonzalez, A. and Sabherwal, R. (2003) Knowledge Management: Challenges, Solutions, and Technologies, Prentice Hall.
2. Cohen, D. and Prusak, L. (2001) In Good Company: how Social Captial Makes Organizations Work, Harvard Business School Press.
3. Dalkir, K.(2005) Knowledge Management in Theory and Practice, 1st edn, Butterworth-Heinemann.
4. Davenport, T. and Prusak, L. (1997) Working Knowledge: how Organizations Manage What They Know, Harvard Business School Press.
5. Dixon, N. (2000) Common Knowledge: How Companies Thrive by Sharing What They Know, Harvard Business School Press.
6. Drucker, P., Garvin, D. Leonard, D., Straus, S. and Brown, J. (1998) Harvard Business Review on Knowledge Management, 6th edn, Hardvard Business School Press.
7. Frappaolo, C. (2006) Knowledge Management, 2nd edn, Capstone.
8. Leonard-Barton, d. (1995) Wellsprings of Knowledge, Harvard Business School Press.
9. Nonaka, I. (1995) The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation, Oxford University Press.
10. Nonaka, I. (2008) The Knowledge-Creating Company, Harvard Business School Press.
11. Nonaka, I. and Nishiquchi, T. (2001) Knowledge Emergence: Social, Technical, and Evolutionary Dimensions of Knowledge Creation, 1st edn, Oxford University Press, USA.
12. Stewart, T. (1997) Intellectual Capital: The New Wealth of Organizations, Doubleday.
13. Tiwana, A. (2002) The Knowledge Management Toolkit: Orchestrating IT, Strategy, and Knowledge Platforms, 2nd edn, Prentice Hall.

Conference Papers

1. Whitley, E. (2000) Tacit and explicit knowledge: Conceptual confusion around the commodification of knowledge, in Proceedings of the Business Processes Resource Center Conference on Knowledge Management: Concepts and Controversies, 10 – 11 Feb., University of Warwick, Coventry, UK, pp.62-64.

Electronic Sources

1. Barclay, R. O. and Murray, P. C. (1997) What is knowledge management, Knowledge Management Associates, http://www.media-access.com/whatis.html Accessed: 05 March 2003.
2. Stewart, T. (2002) The Case Against Knowledge Management, Business 2.0, http://www.providersedge.com/docs/km_articles/The_Case_Against_KM.pdf Accessed: 21 March 2009.

Journal Articles

1. Alavi, M. and Leidner, D. (1999) Knowledge Management: Issues, Chalenges, and Benefits, Communications of the Association for Information Systems, Vol. 1, No. 2es.
2. Alavi, M. and Leidner, D. (2001) Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues, MIS Quartery, 25(1), pp. 107-136.
3. Barachini, F. (2009) Coltural and social issues for knowledge sharing, Journal of Knowledge Management, 13(1), pp. 98-110.
4. Bennet, A. and Bennet, D. (2008) The fallacy of knowledge reuse: building sustainable knowledge, Journal of Knowledge Management, 12(5), pp. 21-33.
5. Cantú, F., Bustani, A., Molina, A. and Moreira, H. (2009) A knowledge-based development model: the research chair strategy, Journal of Knowledge Management, 13(1), pp. 150-170.
6. Gao, F, Li, M. and Clarke, S. (2008) Knowledge, management, and knowledge management in business operations, Journal of Knowledge Management, 12(2), pp. 3-17.
7. Chou, T.-H., Vassar, J. A. and Lin, B. (2008) Knowledge management via ontology development in accounting, Kybernetes, 37(1), pp. 36-48.
8. Hannola, M. and Pirttimaki, V. (2003) Business intelligence empirical study on the top 50 Finnish companies, Journal of American Academy of Business, 2(2), pp. 593-600.
9. Levy, M. (2009) WEB 2.0 implications on knowledge management, Journal of Knowledge Management, 13(1), pp. 120-134.
10. Milne, P. (2007) Motivation, incentives and organisational colture, Journal of Knowledge Management, 11(6), pp. 28-38.
11. Rodney, M. and Sandra, M. (2000) A Critique of Knowledge Management: Using a Social Constructionist Approach, New Technology, Work and Employeement, 15(2), pp. 155-168.
12. Sharma, R. Ng, E., Dharmawirya, M. and Lee, C. (2008) Beyond the digital divide: a conceptual framework for analyzing knowledge societies, Journal of Knowledge Management, 12(5), pp. 151-164
13. Taminiau, Y., Smit, W. and Lange, A. (2009) Innovation in management consolting firms through informal knowledge sharing, Journal of Knowledge Management, 13(1), pp. 42-55.
14. Warren, P. (2006) Knowledge Management and the Semantic Web: from Scenario to Technology, 21(1), pp. 53-59.
15. Webb, C. (2008) Measuring social capital and knowledge networks, Journal of Knowledge Management, 12(5), pp. 65-78.
16. Wiig, K. M. (1997) Knowledge management: an introduction and perspective, Journal of Knowledge Management, 1(1), pp. 6-14.

This is compiled information on the Semantic Web in conjunction with DSL research. This is an emerging research area that attracts research interests among researchers.

Tim Berners-Lee defines the Semantic Web as an extension of the current web in which information is given well-defined meaning. The Semantic Web also better enables computers (e.g., intelligent agents) and people to work in cooperation. He presents the Semantic Web as a layer cake. This knowledge terms includes ontology vocabulary, logic, proof, roles, trust and others.

The Semantic Web as a “layer cake” (Source: James Hendler, 2001 p. 30)

Books

1. Allemang, D. and Hendler, J. (2008) Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL, Morgan Kaufmann.
2. Antoniou, G. and Harmelen, F. (2004) A Semantic Web Primer, 1st end, The MIT Press, Cambridge, London.
3. Antoniou, G. and Harmelen, F. (2008) A Semantic Web Primer, 2nd edn, The MIT Press, Cambridge, London.
4. Daconta, M., Obrst, L. and Smith, K. (2003) The Semantic Web: A Guide to the Future of XML, Web Services, and Knowledge Management. Wiley Publishing, Inc.
5. Davies, J., Bussler, C., Studer, R. (2004) The Semantic Web: Research and Applications, Springer.
6. Davies, J., Studer, R., and Warren, P. (eds.) Semantic Web Technologies: Trends and Research in Ontology-based Systems, Wiley
7. Fensel, D., Hendler, J., Lieberman, H. and Wahlster, W. (2003) Spining the Semantic Web: Bringing the World Wide Web to Its Foll Potential, The MIT Press.
8. Geroimenko, V. and Chen, C. (2003) Visualizing the Semantic Web: XML-based Internet and Information Visualization, Springer.
9. Handschuh, S. and Stabb, S. (2003) Annotation for the Semantic Web, IOS Press, Amsterdam, Berlin, Oxford, Tokyo, Washington, DC.
10. Hyvönen E. (2002) Semantic Web Kick-Off in Finland – Vision, Technologies, Research, and Applications, HIIT Publications, 2002-2001, Helsinki Institute for Information Technology (HIIT), Helsinki, Finland, 304 pages. http://www.cs.helsinki.fi/u/eahyvone/stes/semanticweb/kick-off/proceedings.html Accessed: 27 Nov. 2003.
11. Kashyap, V., Bussler, C., and Moran, M. (2008) The Semantic Web: Semantics for Data and Services on the Web (Data-Centric Systems and Applications), 1st edn, Springer.
12. Omelayenko, B. and Klein, M. (2003) Knowledge Transformation for the Semantic Web, IOS Press, Amsterdam, Berlin, Oxford, Tokyo, Washington, DC.
13. Stuckenschmidt, H. and Harmelen, F. (2005) Information Sharing on the Semantic Web, Springer.
14. Thuraisingham, B. (2002) XML Databases and The Semantic Web. CRC Press. Boca Raton, London, New York, Washinton, D.C.
15. Yu, L. (2007) Introduction to the Semantic Web and Semantic Web Services, Chapman & Hall/CRC.

Book Sections

1. Bryson, J. J., Martin, D., McIlraith, S. A. and Stein, L. A. (2003) Agent-Based Composite Services in DAML-S: the Behavior-Oriented Design of an Intelligent Semantic Web, in Zhong, N., Liu, J. and Yao, Y., (eds.) Web Intelligence, Springer, Berlin, pp. 37-58.
2. Sure, Y., Akkermans, H., Broekstra, J., Davies, J., Ding, Y., Duke, A., Engels, R., Fensel, D., Horrocks, I., Iosif, V., Kampman, A., Kiryakov, A., Klein, M., Lau, T., Ognyanov, D., Reimer, U., Simov, K., Studer, R., Meer, J. v. d. and Harmelen, F. v. (2003) On-To-Knowledge: Semantic Web-Enabled Knowledge Management, in Zhong, N., Liu, J. and Yao, Y., (eds.) Web Intelligence, Springer, Berlin, pp. 277-300.

Journal Articles

1. Artz, D. and Gil, Y. (2007) A survey of trust in computer science and the Semantic Web, Web Semantics: Science, Services and Agents on the World Wide Web, 5(2), pp. 58-71.
2. Bussler, C. (2008) In Semantic Web Technology Taking the Wrong Turn?, IEEE Internet Computing, 12(1), pp. 75-79.
3. Dori, D. (2004) ViSWeb – the Visual Semantic Web: unifying human and machine knowledge representations with Object-Process Methodology, The International Journal on Very Large Data Bases, 13(2), pp. 120-147.
4. Heflin, J. and Hendler, J. (2001) A Portrait of the Semantic Web in Action, IEEE Intelligent Systems, 16(2), pp. 54-59.
5. Hendler, J. (2008) Web 3.0: Chicken Farms on the Semantic Web, IEEE Computer, 41(1), pp. 106-108.
6. Hendler, J. (2008) A New Portrait of the Semantic Web in Action, IEEE Intelligent Systems, 23(3), pp. 2-3.
7. Hendler, J. (2007) The Dark Side of the Semantic Web, IEEE Intelligent Systems, 22(1), pp. 2-4.
8. Hendler, J. (2003) Science and the Semantic Web, Science, 299(5606), pp. 520-521.
9. Hendler, J. (2001) Agents and the Semantic Web, The IEEE Intelligent Systems, (Mar./Apr.), pp. 30-37.
10. Isaac, A., Schlobach, S., Matthezing, H. and Zinn, C. (2008) Integrated access to cultural heritage resources through representation and alignment of controlled vocabularies, Library Review, 57(3), pp.187-199.
11. Jacob, E. (2003) Ontologies and the Semantc Web, Bolletin of the American Society for Information Science and Technology, 29(4), pp. 19-22.
12. Korthaus, A., Schwind, M. and Seedorf, S. (2007) Leveraging Semantic Web technologies for business component specification, Web Semantics: Science, Services and Agents on the World Wide Web, 5(2), pp. 142-150.
13. Mcllraith, S., Son, T. and Zeng, H. (2001) Semantic Web Services, IEEE Intelligent Systems, Mar./Apr. (Special Issue on the Semantic Web), pp. 46-53.
14. Mika, P. and Tummarello, G. (2008) Web Semantics in the Clouds, IEEE Intelligent Systems, September/October(2008), pp. 82–87.
15. Samwald, M., Chen, H., Ruttenberg, A., Lim, E., Marenco, L., Miller, P., Shepherd, G. and Cheung, K.-H. (2010) Semantic SenseLab: Implementing the vision of the Semantic Web in neuroscience, Artificial Intelligence in Medicine, 48(2010), pp.21-28.
16. Sheth, A. and Perry, M. (2008) Traveling the Semantic Web through Space, Time, and Theme, IEEE Computer Society, 12(02), pp. 81-86.
17. Sizov, S. (2007) What Makes You Think That? The Semantic Web’s Proof Layer, IEEE Intelligent Systems, 22(06), pp. 94-99.
18. Tamma, V. and Payne, T. R. (2008) Is a Semantic Web Agent a Knowledge-Savvy Agent?, IEEE Intelligent Systems, 23(4), pp. 82-85.
19. Valle, E. D., Ceri, S., Harmelen, F. v. and Fensel, D. (2009) It’s a Streaming World! Reasoning upon Rapidly Changing Information, IEEE Intelligent Systems, November/December(2009), pp. 83–89.
20. Waard, A. d. (2010) From Proteins to Fairytales: Directions in Semantic Publishing, IEEE Intelligent Systems, 25(2), pp.83 – 88.

Conference Papers

1. Grau, B. (2004) A Possible Simplication of the Semantic Web Architecture, in Proceedings of the Thirteenth International World Wide Web Conference, 17 – 22 May, New York, pp. 704-713.
2. Heflin, J. and Hendler, J. (2000) Semanitc Interoperability on the Web, in Proceedings of Extreme Markup Languages 2000, pp. 111-120.
3. Lassila, O. (2001) Enabling Semantic Web Programming by Integrating RDF and Common Lisp, in Proceedings of the International Semantic Web Working Symposium, 30 July – 1 Aug, Stanford University, California, USA.
4. Marshall, C. and Shipman, F. (2003) Which Semantic Web?, in Proceedings of the Fourteenth ACM Conference on Hypertext and Hypermedia, 26 – 30 August, Nottingham, UK, pp. 57-66.
5. McDowell, L., Etzioni, O., Halevy, A. and Levy, H. (2004) Semantic Email, in Proceedings of The 13th International Conference on World Wide Web, 17 – 22 May, New York, USA, pp. 244-254.
6. Parlanti, D., Pettenati, M. C., Bussotti, P. and Giuli, D. (2008) Improving Information Systems Interoperability and Flexibility Using a Semantic Integration Approach, in Proceedings of 2008 International Conference on Automated solutions for Cross Media Content and Multi-channel Distribution, 17 – 19 November, Florence, Italy, pp.63-67.
7. Pastorello Jr, G. Z., Daltio, J. and Medeiros, C. B. (2008) Multimedia Semantic Annotation Propagation, in Proceedings of the IEEE International Symposium on Multimedia (ISM2008), 15 – 17 December, Berkeley, California, USA, pp. 509-514.
8. Patel-Schneider, P. F. and Horrocks, I. (2006) Position Paper: A Comparison of Two Modelling Paradigms in the Semantic Web, in Proceedings of the 15th International Conference on World Wide Web WWW ’06, 23 – 26 May, Edinburgh, Scotland, pp. 3-12.
9. Sure, Y., Erdmann, M., Angele, J., Staab, S., Studer, R. and Wenke, D. (2002) OntoEdit: Collaborative Ontology Development for the Semantic Web, in Proceedings of the fisrt International Semantic Web Conference (ISWC 2002), 9 – 12 June, Sardinia, Italia.
10. Trastour, D., Bartolini, C. and Preist, C. (2002) Semantic Web Support for the Business-to-Business E-Commerce Lifecycle, in Proceedings of Proceedings of the eleventh International Conference on World Wide Web Workshop on Semantic Web Services, 7 – 11 May, Honololu, Hawaii, USA, pp. 89-98.

Courses

1. Prof. Amit Sheth, Semantic Web in Spring 2002 at the University of Georgia. http://lsdis.cs.uga.edu/SemWebCourse/index.htm
2. Prof. Jeff Heflin, Semantic Web Topics in Spring 2008 at Lehigh University. ttp://www.eecs.lehigh.edu/~heflin/courses/sw-2008/

Electronic Resources

1. Berners-Lee, T. (1989) Information Management: A Proposal, http://www.w3.org/History/1989/proposal.html Accessed: 27 November 2003.
2. Berners-Lee, T., Hendler, J. & Lassila, O. (2001) Agents and the Semantic Web, http://www.cs.umd.edu/users/hendler/AgentWeb.html Accessed: 27 November 2003.
3. Fensel, D. and Bussler, C. (2003) Semantic Web Enabled Web Services”, Semantic Web Services Initiative, http://www.swsi.org Accessed: 27 November 2003.
4. McCarthy, P. (2004) Use RDF models in your java applications with the Jena Semantic Web Framework, http://www-106.ibm.com/developerworks/java/library/j-jena/ Accessed: 07 July 2004.
5. Herman, I. (2008) Tutorial on Semantic Web, http://www.w3.org/People/Ivan/CorePresentations/RDFTutorial/ Accessed: 17 May 2009.
6. Palmer, S. (2001) The Semantic Web: An Introduction, http://infomesh.net/2001/swintro/ Accessed: 17 May 2009.

Ph.D. Theses

1. Heflin, J. (2001) Towards The Semantic Web: Knowledge Representation in a Dynamic, Distributed Environment, PhD. Thesis, Department of Computer Science, University of Maryland, 137 pages.

Report

1. Sheth, A., Arpinar, I. and Kashyap, V. (2002) Relationships at the Heart of Semantic Web: Modeling, Discovering, and Exploiting Complex Semantic Relationships, University of Georgia & National Library of Medicine, 1-38 pages.

Tools

1. Jena Semantic Web Framework
2. Portégé – Portégé is a free, open source ontology editor and knowledge-base framework developed by Stanford Center for Biomedical Informatics Research.
3. The OWL API – Java interface and implementation for the OWL.

Web Sites

1. SemanticWeb.org
2. Semantic Web Services Initiative
3. SIGSEMIS – Semantic Web and Information Systems: Coltivating The Semantic Web Vision in IS

This is compiled information on (Web) ontology in conjunction with DSL research. Ontology is an emerging research area that attracts research interests among researchers in Information Systems, particularly its applications to the Semantic Web.

What is an Ontology?

• Gruber, “ontology is an explicit specification of conceptualization”
• Borst, “a formal specification of a shared conceptualization”
• Sowa, “Logic itself has no vocabulary for describing the things that exist. Ontology fills that gap: it is the study of existence, of all the kinds of entities – abstract and concrete – that make up the world”

SEMIS: Semantic Web & IS, (Source: SIGSEMIS, 2004 p. 5)

World Wide Web (WWW) has changed the way information is accessed and disseminated. It has also changed the way business is conducted through e-business systems. In its simplest form, Web documents are marked up using hyperlinks and information are accessed and cross-referenced in a non-linear fashion. Web documents can be retrieved based on individual’s information needs. However this type of web-based information seeking fashion has been designed mainly for human interpretation. The increasingly widespread WWW applications have extended the opportunity for intelligent agents to access and interpret Web documents and resources. This is the vision of the Semantic Web, which aims to enable content of web resources to be interpreted and processed by software agents. In fact, the term “semantic” in the Semantic Web refers to the way data in the Web conveys meaning in such a form that makes it machine-readable and processable, thus providing a mechanism for intelligent agents to interpret data in web resources and to perform any task autonomously in the WWW environment. Examples of such tasks include scheduling, searching for information, controlling and managing work processes and even authentication and security. Thus the agents need to understand the meaning associated with web resources before it can read data and process any task. The agents also need to know how to integrate data and information from different resources such as integrating product information from different Web sites to complete a purchase transaction. Web ontology gives an explicit representation of Web resources for the agents to act on our behalf intelligently for the above given tasks.

Books

1. Alexiev, V., Breu, Michael, Bruijn J., Fensel, D. Lara, R. and Lausen, H. (2005) Information Integration with Ontologies : Experiences from an Industrial Showcase, John Wiley & Sons.
2. Calero, C., Ruiz, F., and Piattini, M. (2006) Ontologies for Software Engineering and Software Technology, 1st edn, Springer.
3. Cimiano, P. (2006) Ontology Learning and Population from Text: Algorithms, Evaluation and Applications, 1st edn, Springer.
4. Davies, J., Fensel, D. and Harmelen, F. (2003) Towards The Semantic Web: Ontology-driven Knowledge Management, John Wiley & Sons.
5. Dietz, j. (2006) Enterprise Ontology: Theory and Methodology, 1st edn, Springer.
6. Euzenat, J. (2007) Ontology Matching, 1st edn, Springer.
7. Fensel, D. (2004) Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce, Springer-Verlag, Berlin, New York.
8. Fensel, D., Hendler, J., Lieberman, H. and Wahlster, W. (2003) Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential, The MIT Press.
9. Gasevic, D., Djuric, D., Devedzic, V., and Selic, B. (2006) Model Driven Architecture and Ontology Development, 1st edn, Springer.
10. Gómez-Pérez, A., Fernandez-L๓pez, M. and Corcho, O. (2002) Ontological Engineering: with examples from the areas of Knowledge Management, e-Commerce and Semantic Web, Springer Verlag.
11. Gruber, T. (ed.) (1993) Toward Principles for the Design for Ontologies used for Knowledge Sharing, Kluwer Academic Pulishers, Padova, Italy.
12. Lacy, L. (2005) Owl: Representing Information Using the Web Ontology Language, Trafford Publishing.
13. Rebstock, M., Janina, F., Paulheim, H. (2008) Ontologies-Based Business Integration, 1st edn, Springer.
14. Rittgen, P. (2007) Handbook of Ontologies for Business Interaction, Information Science Reference.
15. Staab, S. and Studer, R. (2004) Handbook on Ontologies, Springer
16. Stuckenschmidt, H. and Harmelen, F. (2005) Information Sharing on the Semanitc Web, Springer-Verlag.
17. Tamma, V., Cranefield, S., Finin, T. and Willmott, S. (Eds.) (2005) Ontologies for Agents: Theory and Experiences, Birkhnไuser Verlag, Basel, Boston, Berlin.
18. Omelayenko, B. and Klein, M. (2003) Knowledge Transformation For The Semantic Web. IOS Press, Amsterdam, Berlin, Oxford, Tokyo, Washington, DC.

Book Sections/Chapters

1. Aart, C., Wielinga, B. and Schreiber, G. (2005) Message Content Ontologies, in Tamma, V., Cranefield, S., Finin, T. and Willmott, S., (eds.) Ontologies for Agents: Theory and Experiences, Birkhไuser Verlag, Basel, Boston, Berlin, pp. 169-200.
2. Benjamins, R. and Fensel, D. (1998) The Ontological Engineering Initiative (KA). in Guarino, N. (ed.) Formal Ontology in Information Systems, IOS Press.
3. Chen, H., Finin, T. and Joshi, A. (2005) The SOUPA Ontology for Pervasive Computing, in amma, V., Cranefield, S., Finin, T. and Willmott, S., (eds.) Ontologies for Agents: Theory and Experiences, T Birkhไuser Verlag, Basel, Boston, Berlin.
4. Cranefield, S., Pan, J. and Purvis, M. (2005) A UML Ontology and Derived Dontent Language for a Travel Booking Scenario, in Tamma, V., Cranefield, S., Finin, T. and Willmott, S., (eds.) Ontologies for Agents: Theory and Experiences, Birkhไuser Verlag, Basel, Boston, Berlin, pp. 259-276.
5. Cranefield, S., Purvis, M., Nowostawski, M. and Hwang, P. (2005) Ontologies for Interaction Protocols, in Tamma, V., Cranefield, S., Finin, T. and Willmott, S., (eds.) Ontologies for Agents: Theory and Experiences, Birkhไuser Verlag, Basel, Boston, Berlin, pp. 1-19.
6. Dickinson, I. and Wooldridge, M. (2005) Some Experiences with the Use of Ontologies in Deliberative Agents, in Tamma, V., Cranefield, S., Finin, T. and Willmott, S., (eds.) Ontologies for Agents: Theory and Experiences, Birkhไuser Verlag, Basel, Boston, Berlin, pp. 277-298.
7. Ding, Y., Fensel, D., Klein, M., Omelayenko, B. and Schulten, E. (2004) The Role of Ontologies in eCommerce, in Staab, S. and Studer, R., Handbook on Ontologies, (eds.), Springer, pp. 593-616.
8. Dou, D., McDermott, D. and Qi, P. (2005) Ontology Translation by Ontology Merging and Automated Reasoning, in Tamma, V., Cranefield, S., Finin, T. and Willmott, S., Ontologies for Agents: Theory and Experiences, (eds.) Birkhไuser Verlag, Basel, Boston, Berlin, pp. 73-94.
9. Gerogiannakis, S., Sintichakis, M. and Achilleopoulos, N. (2003) Collaborative Knowledge Management and Ontologies The ONTO-LOGGING Platform, in Wimmer, M. (ed.) Knowledge Management in Electronic Government, Springer Berlin / Heidelberg, pp. 127-138.
10. Guarino, N. and Giaretta, P. (1995) Ontologies and Knowledge Bases, in Mars, N. (ed.) Towards Very Large Knowlede Bases: Knowledge Building and Knowledge Sharing, IOS Press, Amsterdam, pp. 25-32.
11. Hoss, A. and Carver, D. (2008) Towards Combining Ontologies and Model Weaving for the Evolution of Requirements Models, in Paech, B. and Martell, C. (eds.) Innovations for Requirement Analysis. From Stakeholders’ Needs to Formal Designs, Springer Berlin / Heidelberg, pp. 85-102,
12. Lister, K., Hristozova, M. and Sterling, L. (2005) Reconciling Implicit and Evolving Ontologies for Semantic Interoperability, in Tamma, V., Cranefield, S., Finin, T. and Willmott, S., Ontologies for Agents: Theory and Experiences, (eds.) Birkhไuser Verlag, Basel, Boston, Berlin, pp. 121-144.
13. Maedche, A., Pekar, V. and Staab, S. (2003) Ontology Learning Part One – on Discovering Taxonomic Relations from the Web, in Zhong, N., Liu, J. and Yao, Y., (eds.) Web Intelligence, Springer, Berlin, pp. 301-322.
14. Martin, P. (2003). Knowledge Representation, Sharing, and Retrieval on the Web, in Zhong, N., Liu, J. and Yao, Y., (eds.) Web Intelligence, Springer, Berlin, pp. 243-276.
15. Magyar, G., Wojtkowski, W., Wojtkowski, W., Zupancic, J. and Knapp, G. (2007) Advances in Information Systems Development: New Methods and Practice for the Networked Society, in Magyar, G., Knapp, G., Wojtkowski, W., Wojtkowski, W. and Zupancic, J. (eds.), Ontology-baed User Modeling for Web-bsed Information Systems, Springer, USA. pp. 457-468.
16. Pazienza, M. and Vindigni, M. (2005) Agent to Agent Talk: Nobody There? Supporting Agent Linguistic Communication, in Tamma, V., Cranefield, S., Finin, T. and Willmott, S., (eds.) Ontologies for Agents: Theory and Experiences, Birkhไuser Verlag, Basel, Boston, Berlin, pp. 43-72.
17. Razmerita, L., Angehrn, A. and Maedche, A. (2007) Ontology-based User Modeling for Knowledge Management Systems, in Sharman, R., Kishore, R. and Ramesh, R. (eds.), Ontologies: A Handbook of Priciples, Concepts and Applications in Information Systems, Springer, USA, pp. 653-664.
18. Sashima, A., Izumi, N. and Kurumatani, K. (2005) Location-Mediated Agent Coordination in Ubiquitous Computing, in Tamma, V., Cranefield, S., Finin, T. and Willmott, S., (eds.) Ontologies for Agents: Theory and Experiences, Birkhไuser Verlag, Basel, Boston, Berlin, pp. 299-322.
19. Soh, L.-K. (2005) Collaborative Understanding of Distributed Ontologies in a Multiagent Framework: Experiments on Operational Issues, in amma, V., Cranefield, S., Finin, T. and Willmott, S., (eds.) Ontologies for Agents: Theory and Experiences, T Birkhไuser Verlag, Basel, Boston, Berlin, pp. 95-120.
20. Stuckenschmidt, H., Harmelen, F. and Giunchiglia, F. (2005) Query Processing in Ontology-Based Peer-to-Peer Systems, in Tamma, V., Cranefield, S., Finin, T. and Willmott, S., (eds.) Ontologies for Agents: Theory and Experiences, Birkhไuser Verlag, Basel, Boston, Berlin, pp. 145-168.
21. Zimmermann, R., Kไs, S., Butscher, R. and Bodendorf, F. (2005) An Ontology for Agent-Based Monitoring of Fulfillment Processes, in Tamma, V., Cranefield, S., Finin, T. and Willmott, S., (eds.) Ontologies for Agents: Theory and Experiences, Birkhไuser Verlag, Basel, Boston, Berlin, pp. 323-345.

Journal Articles

1. Adams, N., J. Fraser, et al. (2002) Towards an Ontology for Electroic Transaction Services, International Journal of Intelligent Systems in Accounting, Finance and Management, 11:, pp. 173-181.
2. Albers, M., Jonker, C. and Karami, M. (2004) Agent Models and Different User Ontologies for an Electronic Market Place, Knowledge and Information Systems, 6, pp. 1-41.
3. Bermudez, L. and Piasecki, M. (2006) Metadata Community Profiles for the Semantic Web, GeoInformatica, 10(2), pp. 159-176.
4. Bhatt, M., Wouters, C. and Flahive, A. (2004) Semantic Completeness in Sub-Ontology Extraction Using Distributed Methods, Computational Science and Applications Part III, 3045(Lecture Notes in Computer Science), pp. 508-517.
5. Bechhofer, S., Yesilada, Y., Stevens, R., Jupp, S. and Horan, B. (2008) Using Ontologies and Vocabularies for Dynamic Linking, IEEE Internet Computing, 12(3), pp.32-39.
6. Chun, S., Atluri, V. and Adam, N. (2005) Using Semantics for Policy-Based Web Service Composition, Distributed and Parallel Databases, 18(1), pp. 37-64.
7. Corcho, O. and Gomez-Perez, A. (2005) A Layered Model for Building Ontology Translation Systems, International Journal on Semantic Web and Information Systems, 1(2), pp. 22-48.
8. Cristani, M. and Cuel, R. (2005) A Survey on Ontology Creation Methodologies, International Journal on Semantic Web and Information Systems, 1(2), pp. 49-69.
9. Doan, A., Madhavan, J., Dhamankar, R. Domingos, P. and Halevy, A. (2003) Learning to match ontologies on the Semantic Web, The VLDB Journal, 12(4), pp. 303-319.
10. Fensel, D., Harmelen, F., Klein, M. and Akkermans, H. (2002) On-To-Knowledge: Ontology-based Tools for Knowledge Management, German Journal Kunstliche Intelligenz, Special Issus on Knowledge Management.
11. Garcํa, R. Gil, R. and Delgado, J. (2007) A web ontologies framework for digital rights management, Artificial Intelligence and Law, 15(2), pp. 137-154.
12. Hadzic, M. and Chang, E. (2005) Medical ontologies to support human disease research and control, International Journal of Web and Grid Services, 1(2), pp. 139-150.
13. Hepp, M. (2006) Products and Services Ontologies: A Methodology for Deriving OWL Ontologies from Industrial Categorization Standards, International Journal on Semantic Web and Information Systems, 2(1), pp. 72-99.
14. Hecker, M., Dillon, Tharam, and Chang, E. (2008) Privacy Ontology Support for E-Commerce, IEEE Internet Computing, 12(2), pp. 54-61.
15. Hobbs, J. and Pan, F. (2004) An Ontology of Time for The Semantic Web, ACM Transactions on Asian Language Information Processing, 3(1), pp. 66-85.
16. Jacob, E. (2003) Ontologies and the Semantc Web, Bulletin of the American Society for Information Science and Technology, 29(4), pp. 19-22.
17. Jepsen, T. C. (2009) Just What Is an Ontology, Anyway?, IT Professional, 11(5), pp.22-27.
18. Jurisica, I., Mylopoulos, J. and Eric, Y. (2004) Ontologies for Knowledge Management: An Information Systems Perspective, Knowledge and Information Systems, 6(4), pp. 380-401.
19. Kalyanpur, A., Parsia, B. and Hendler, J. (2005) A Tool for Working with Web Ontologies, International Journal on Semantic Web and Information Systems, 1(1), pp. 36-49.
20. Maedche, A., Motik, B., Stojanovic, L., Studer, R. and Volz, R. (2003) Ontologies for Enterprise Knowledge Management, IEEE Intelligent Systems, 18(2), pp. 26-33.
21. Maedche, A. and Staab, S., Ontology Learning for the Semantic Web, IEEE Intelligent Systems, 16(2), pp. 72-79.
22. Mirhaji, P., Zhu, M., Vagnoni, M., Bernstam, E., Zhang, J. and Smith, J. (2009) Ontology driven integration platform for clinical and translational research, BMC Bioinformatics, 10(Suppl 2), p. S2.
23. Mohamed, A., Lee, S. and Salim, S. (2006) An Ontology-Based Knowledge Model for Software Experience Management, International Journal of The Computer, the Internet and Management, 14 (3), pp. 79-88.
24. Noy, N. F. and Klein, K. (2004) Ontology Evolution: Not the Same as Schema Evolution, Knowledge and Information Systems, 6, pp. 428-440.
25. Noy, N. F., Rubin, D. L. and Musen, M. A. (2004) Making Biomedical Ontologies and Ontology Repositories Work, IEEE Intelligent Systems, 19(6), pp. 78-81.
26. Pinto, H. and Martins, J. (2004) Ontologies: How can They be Built?, Knowledge Information Systems, 6, pp. 441-464.
27. Poli, R. (2002) Ontological methdology, International Journal of Human-Computer Studies, 56, pp. 639-664.
28. Poole, D., Smyth, C. and Sharma, R. (2009) Ontology Design for Scientific Theories That Make Probabilistic Preictions, The IEEE Intelligent Systems, January/February, pp. 27-36.
29. Opdahl, A. and Henderson-Sellers, B. (2002) Ontological Evaluation of the UML Using the Bunge-Wand-Weber Model, Software and Systems Modeling, 1, pp. 43-67.
30. Pinto, H. and Martins, J. (2004) Ontologies: How can They be Built?, Knowledge Information Systems, 6, pp. 441-464.
31. Soares, A. and Fonseca, F. (2007) ONTOLOGY-DRIVEN INFORMATION SYSTEMS: AT DEVELOPMENT TIME, International Journal of Computers, Systems and Signals, 8(2), pp. 50-59.
32. Sure, Y., Staab, S. and Studer, R. (2002) Methodology for Development and Employment of Ontology based Knowledge Management Applications, Communications of the ACM, 31(4), pp. 18-23
33. Teller, P. (2008) The Use of A Formal Representation of Accounting Standards, International Journal of Computer Science and Applications, 5(3b), pp. 93 – 116.
34. Uschold, M. and Gruninger, M. (1996) ONTOLOGIES: Principles, Methods and Applications, Knowledge Engineering Review, 11(2), pp. 93-155.
35. Uschold, M., King, M., Moralee, S. and Zorgios, Y. (1998) The Enterprise Ontology, The Knowledge Engineering Review, 13 (Special Issue on Putting Ontologies to Use).
36. Vega, J. C. A., Gómez-Pérez, A., Tello, A. L. and Pinto, H. S. A. N. P. (2000) Reference Ontology and (ONTO) Agent: The Ontology Yellow Pages, Knowledge and Information Systems, 2, pp. 387-412.
37. Vijayan, S. and Veda, C. S. (2006) The role of domain ontologies in database design: An ontology management and conceptual modeling environment, ACM Transactions on Database Systems, 31(3), pp. 1064-1094.
38. Vrochidis, S., Doulaverakis, C., Gounaris, A., Nidelkou, E., Makris, L. and Kompatsiaris, I. (2008) A Hybrid Ontology and Visual-based Retrieval Model for Cultural Heritage Multimedia Collections, International Journal of Metadata, Semantics and Ontologies, 3(3), pp.167-182.
39. Wiesman, F. and Roos, N. (2004) Domain independent learning of ontology mappings, in Proceedings of the 3rd International Joint Conference on Autonomous Agents and Multiagent Systems, 19 – 23 August, New York, NY, USA, pp. 844-851.
40. Wollersheim, D. and Rahayu, W. (2005) Ontology Based Query Expansion Framework for Use in Medical Information Systems, International Journal of Web Information Systems, 1(2), pp. 1744-1760.
41. Wouters, C., Dillon, T., Rahayu, W. and Chang, E. (2005) Large scale ontology visualisation using ontology extraction, International Journal of Web and Grid Services, 1(1), pp. 113-135.
42. Zillner, S. and Winiwarter, W. (2005) Integration of ontological knowledge within the authoring and retrieval of multimedia metaobjects, International Journal of Web and Grid Services, 1(3/4), pp. 397-415.

Conference Papers

1. Abels, S., Haak, L. and Hahn, A. (2005) Identification of Common Methods Used for Ontology Integration Tasks. In Proceedings of the 1st International ACM Workshop on Interoperability of Heterogeneous Information Systems (IHIS’05), 4 November, Bremen, Germany, pp. 75-78.
2. Alani, H. and Brewster, C. (2005) Ontology Ranking based on the Analysis of Concept Structures, in Proceedings of the 3rd International Conference on Knowledge Capture, 2 – 5 October, Banff, Alberta, Canada, pp. 51-58.
3. Beale, T. and MD, S. H. (2007) An Ontology-based Model of Clinical Information, in Proceedings of the 12th World Congress on Health (Medical) Informatics, 20 – 24 August, the Brisbane Convention Centre, Southbank Brisbane, Australia, pp. 760-764.
4. Bhatt, M., Flahive, A., Wouters, C., Rahayu, W., Taniar, D. and Dillon, T. (2004) A Distributed Approach to Sub-Ontology Extraction, in Proceedings of the 18th International Conference on Advanced Information Networking and Applications, 29 – 31 Mar., Fukuoka Institute of Technology (FIT), Fukuoka, Japan, pp. 634-641.
5. Boer, A., Engers, T. and Winkels, R. (2003) Using ontologies for comparing and harmonizing legislation, in Proceedings of the 9th International Conference on Artificial Intelligence and Law, 24 – 28 June, Scotland, United Kingdom, pp. 60-69.
6. Constantopoulos, P. and Dritsou, V. (2007) An ontological model for digital preservation, in Proceedings of An International Symposium on Digital Curation, 18 – 20 April, Chapel Hill, NC, USA.
7. Corazzon, R. (2004) Descriptive and Formal Ontology: A resource guide to contemporary research
8. Das, A., Wu, W. and McGuinness, D. (2001) Industrial Strength Ontology Management, in Proceedings of the International Semantic Web Working Symposium Workshop on Ontology and Ontology Maintenance, 30 Jul. – 1 Aug., Stanford University, California, USA
9. Davies, J., Duke, A. and Stonkus, A. (2002) OntoShare: Using Ontology for Knowledge Sharing, in Proceedings of the WWW2002 International Workshop on the Semantic Web, 7 May, Hawaii.
10. De Lazzari, C., Guerrieri, E. and Pisanelli, D. M. (2003) A Domain Ontology for Mechanical Circulatory Support Systems, in Proceedings of Computers in Cardiology 2003, 8 May, Thessaloniki, Greece, pp. 417-419.
11. Ding, Y. and Fensel, D. (2001) Ontology Library Systems: The key to successful Ontology Re-use, in Proceedings of International Semantic Web Working Symposium (SWWS) on Ontology and Ontology Maintenance, 30 July – 1 August, Stanford University, California, USA.
12. Doan, A., Madhavan, J., Domingos, P. and Halevy, A. (2002) Learning to Map between Ontologies on the Semantic Web, in Proceedings of the 11th International Conference on World Wide Web, 7 – 11 May, Hawaii, pp. 662-673.
13. Ferrario, R. and Oltramari, A. (2004) Towards a Computational Ontology of Mind, in Proceedings of the International Conference on Formal Ontology in Information Systems, 4 – 6 November, Torino, Italy, pp. 287-297.
14. Flahive, A., Apduhan, B., Rahayu, W. and Taniar, D. (2005) Validating the Distributed Ontology Framework for Development onto the Semantic Grid Environment, in Proceedings of the 11th International Conference on Parallel and Distributed Systems, Fukuoka Institute of Technology, Japan, pp. 684-688.
15. Flahive, A., Rahayu, W., Taniar, D. and Apduhan, B. (2005) A Distributed Ontology Framework in the Sementic Grid Environment, in Proceedings of the 19th International Conference on Advanced Information Networking and Applications, 28 – 30 March, Tamkang University, Taiwan, pp. 193-196.
16. Foo, N. (1995) Ontology Revision, in Proceedings of the 3rd International Conference on Conceptual Structures, 14 – 18 August, University of California, Santa Cruz, pp. 1-14.
17. Gahleitner, E., Behrendt, W., Palkoska, J. and Weippl, E. (2005) On Cooperatively Creating Dynamic Ontologies, in Proceedings of the sixteenth ACM conference on Hypertext and hypermedia, 6 – 9 September, Salzburg, Austria, pp. 208-210.
18. Guarino, N. (1998) Formal Ontology and Information Systems, in Proceedings of the International Conference on Formal Ontology in Information Systems (FOIS98), In conjunction with the 6th International Conference on Principles of Knowledge Representation and Reasoning (KR98), 6 – 9 June, Trento, Italy, pp. 3-15.
19. Guarino, N. (2002) Evaluation Ontological Decisions with OntoClean, Communications of the ACM, 45(2), pp. 61-65.
20. Grüninger, M. and Lee, J. (2002) Ontology Applications and Design, Communications of the ACM, 45(2), pp. 39-41.
21. Guarino, N. (1998) Formal Ontology and Information Systems, in Proceedings of International Conference on Formal Ontology in Information Systems (FOIS98) in Proceedings of the 6th International Conference on Principles of Knowledge Representation and Reasoning (KR98), 6 – 9 June Trento, Italy, pp. 3-15.
22. Haase, P. and Stojanovic, L. (2005) Consistent Evolution of OWL Ontologies, in Proceedings of The Semantic Web: Research and Applications: Second European Semantic Web Conference, 29 May – 1 June, Heraklion, Crete, Greece.
23. Horrocks, I. and Patel-Schneider, P. (2004) A Proposal for an OWL Rules Language, in Proceedings of the Thirteenth International World Wide Web Conference, 17 – 22 May, New York, pp. 723-731.
24. Jasper, R. and Uschold, M. (1999) A Framework for Understanding and Classifying Ontology Applications, in Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence Workshop on Ontology, 31 July – 6 August, City Conference Center, Stockholm, Sweden, pp. 11-1-11-12.
25. Jin, Y., Decker, S. and Wiederhold, G. (2001) OntoWebber: Model-Driven Ontology-Based Web Site Management, in Proceedings of International Semantic Web Working Symposium (SWWS) Workshop on Ontology and Ontology Maintenance, 30 July – 1 August, Stanford University, California, USA.
26. Kakali, C., Lourdi, I., Stasinopoulou, T., Bountouri, L., Papatheodorou, C., Doerr, M. and Gergatsoulis, M. (2007) Integrating Dublin Core metatada for cultural heritage collections using ontologies, in Proceedings of the International Conference on Dublin Core and Metadata Applications (DC-2007), 27 – 31 August, Singapore, pp. 128-139.
27. Klein, M. and Fensel, D. (2001) Ontology versioning on the Semantic Web, in Proceedings of International Semantic Web Working Symposium. 30 July – 1 August, Stanford University, California, USA.
28. Klein, M., Fensel, D., Kiryakov, A. and Ognyanov, D. (2002) Ontology Versioning and Change Detection on the Web, in Proceedings of 13th International Conference on Knowledge Engineering and Knowledge Management, 1 – 4 October, Sigenza, Spain, pp. 197-212.
29. Lacher, M. and Groh, G. (2001) Facilitating the exchange of explicit knowledge through ontology mappings, in Proceedings of the 14th International FLAIRS Conference, 21 – 23 May, Key West, Florida, USA.
30. Lei, Y. (2005) An Instance Mapping Ontology for the Semantic Web, in Proceedings of the 3rd International Conference on Knowledge Capture, 2 – 5 October, Banff, Alberta, Canada, pp. 67-74.
31. Maedche, A. and Staab, S. (2001). Learning Ontologies for the Semantic Web, in Proceedings of the second International Workshop on the Semantic Web, 1 May, Hong Kong, China.
32. Mao, M., Peng, Y. and Spring, M. (2008) Ontology Mapping: As a Binary Classification Problem, in Proceedings of The Fourth International Conference on Semantics, Knowledge and Grid, 3 – 5 December, Beijing, China, pp.20-25.
33. Maturana, H. (1988) Ontology of Observing: The Bilological Foundations of Self Consciousness and The Pysical Domain of Existence, in Proceedings of ASC Conference on Texts in Cybernetic Theory, 18 – 23 October, Felton, CA.
34. Mehrotra, M. (2002) Ontology Analysis for the Semantic Web, in Proceedings of the American Association for Artificial Intelligence Workshop on Ontologies and the Semantic Web, 28 – 29 July, Edmonton, Canada, pp. 1-11.
35. Min, X., Luo, Z. and Qianxing, X. (2008) Semantic Similarity between Concepts based on OWL Ontologies, in Proceedings of the IEEE International Symposium on Multimedia (ISM2008), 15 – 17 December, Berkeley, California, USA, pp. 749-752.
36. Mochol, M. (2008) Interoperability Issues, Ontology Matching and MOMA, in Proceedings of International Conference on Semantic Systems, 3 – 5 September, Graz, Austria, pp. 158-165.
37. Mommers, L. (2003) Application of a knowledge-based ontology of the legal domain in collaborative workspaces, in Proceedings of the 9th International Conference on Artificial Intelligence and Law, 24 – 28 June, Scotland, United Kingdom, pp. 70-76.
38. Pahl, C. and Casey, M. (2003) Ontology Support for Web Service Processes, in Proceedings of the 9th European Software Engineering Conference held jointly with 10th ACM SIGSOFT International Symposium on Foundations of Software Engineering, September 2003, Helsinki, Finland, pp. 208-216.
39. Prantner, K. Ding, Y. Luger, M. and Yan Z. (2007) Tourism Ontology and Semantic Management System: state-of-the-arts analysis, in Proceedings of the IADIS International Conference WWW/Internet 2007, 5 – 8 October, Vila Real, Portugal, pp. 111-115.
40. Roos, N. and Wiesman, F. (2004) Domain Independent Learning of Ontology Mappings, in Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 19 – 23 July, Columbia University, New York City, pp. 846-853.
41. Ruiz-Martํnez, J. M., Mi๑arro-Gim้nez, J. A., Guill้n-Cแrceles, L., Castellanos-Nieves, D., Valencia-Garcํa, R., Garcํa-Sแnchez, F., Fernแndez-Breis, J. T. and Martํnez-B้jar, R. (2008) Populating Ontologies in the eTourism Domain, in Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 9 – 12 December, Sydney, Australia, pp. 316-319.
42. Schnurr, H.-P., Staab, S. and Studer, R. (1999) Ontology-based Process Support, in Proceedings of The American Association for Artificial Intelligence on KM/CBR Workshop, 19 July, Orland, Florida.
43. Stojanovic, L., Maedche, A., Motik, B. and Stojanovic, N. (2002) User-Driven Ontology Evolution Management, in Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management, 1 – 4 October, Sigueza, Spain, pp. 285-300.
44. Stojanovic, L., Maedche, A., Stojanovic, N. and Studer, R. (2003) Ontology Evolution as Reconfiguration-Design Problem Solving, in Proceedings of International Conference On Knowledge Capture, 23 – 26 October, Sanibel Island, FL, USA, pp. 162-171.
45. Stojanovic, L. and Motik, B. (2002) Ontology Evolution within Ontology Editors, in Proceedings of Proceedings of the 13th International Conference in Knowledge Engineering and Knowledge Management EKAW 2002 on the OntoWeb-SIG3 Workshop, 30 September, Sigueza, Spain.
46. Stojanovic, N., Gonzalez, J. and Stojanovic, L. (2003) ONTOLOGER – A System for Usage-Driven Management of Ontology-Based Information Portals, in Proceedings of the 2nd International Conference on Knowledge Capture, Sanibel Island, FL, USA, pp. 172-179.
47. Sure, Y., Erdmann, M., Angele, J., Staab, S., Studer, R. and Wenke, D. (2002) OntoEdit: Collaborative Ontology Development for the Semantic Web, in Proceedings of The fisrt International Semantic Web Conference (ISWC 2002), 9 – 12 June, Sardinia, Italia.
48. Suárez-Figueroa, M. C. and Gómez-Pérez, A. (2008) Building Ontology Networks: How to Obtain a Particular Ontology Network Life Cycle?, in Proceedings of International Conference on Semantic Systems, 3 – 5 September, Graz, Austria, pp. 142-149.
49. Swartout, B., Patil, R. and Knight, K. (1996) Towards Distributed Use of Large-Scale Ontologies, in Proceedings of the Tenth Knowledge Acquisition for Knowledge-Based Systems Workshop, 9 – 14 November, Banff, Alberta, Canada.
50. Uschold, M. and King, M. (1995) Towards a Methodology for Building Ontologies, in Proceedings of the International Joing Conference on Artificial Intelligence, 20 – 25 August, Montreal.
51. Vergara, J. E. L. d., Villagrแ, V. A., Berrocal, J., Asensio, J. and Pignaton, R. (2003) Semantic Management: Application of Ontologies for the Integration of Management Information Models, in Proceedings of the Eighth IFIP/IEEE International Symposium on Integrated Network Management, 24 – 28 March, Colorado, U.S.A.
52. Wang, H. and Li, L. (2005) CLASSIFICATION BASED ON GEOGRAPHICAL INFORMATION ONTOLOGY, in Proceedings of International Syposium on Spatio-temporal Modeling, Spatial Reasoning, Analysis, Data Mining and Data Fusion, 27 – 29 August, Beijing, China, pp.27-29.
53. Zúñiga, G. (2001) Ontology: Its Transformation From Philosophy to Information Systems, in Proceedings of the International Conference on Formal Ontology in Information Systems, 17 – 19 October, Ogunquit, Maine, USA, pp. 187-197.

Electronic Sources/Articles

1. Firat, A., Madnick, S. and Grosof, B. (2002) Knowledge Integration to Overcome Ontological Heterogeneity: Challenges from Financial Information Systems, http://web.mit.edu/sloan-msa/Papers/4.2.pdf Accessed: 20 June 2009.
2. Gruber, T. (2001) What is an Ontology? http://www-ksl.stanford.edu/kst/what-is-an-ontology.html Accessed: 21 Nov. 2003.
3. Pisanellii, D., Gangemi, A. and Steve, G. (2002) Ontologies and Information Systems: the Marriage of the Century?
4. Swanson, Z. and Freeze, r. (2008) Generally Accepted Accounting Principles: An Ontologial Perspective, , (Accessed: 22 September 2009).
5. The Gene Ontology Project, The Gene Ontology Consortium, http://www.geneontology.org/ Accessed: 17 April 2009.
6. The Open Biomedical Ontologies, The OBO Foundry, http://www.obofoundry.org/ Accessed: 17 April 2009.

Ontology Tools

1. Jena Semantic Web Framework. http://jena.sourceforge.net/
2. OilEd, an ontology editor allowing the user to build ontologies using DAML+OIL. http://oiled.man.ac.uk/index.shtml
3. Portégé – Ontology Editor and Knowledge Acquisition System. http://protege.stanford.edu/
4. RAP – RDF API for PHP. http://www.wiwiss.fu-berlin.de/suhl/bizer/rdfapi/
5. The OWL API – Java interface and implementation for the OWL. http://owlapi.sourceforge.net/index.html

Ph.D. Theses

1. Baida, Z. (2006) Software-aided Service Bundling – Intelligent Methods & Tools for Graphical Service Modeling, Ph.D. thesis, Vrije Universiteit Amsterdam, 301 pages. http://www.baida.nl/research/serviguration.html
2. Borst, W. (1997) Construction of Engineering Ontologies for Knowledge Sharing and Reuse, Ph.D. thesis, Center for Telematics and Information Technology, University of Twente, 243 pages. http://doc.utwente.nl/17864
3. Keet, M. (2004) Aspects of Ontology Integration, Ph.D. thesis, Faculty of Computer Science, University of Bozen-Bolzano, 87 pages. http://www.meteck.org/research.html#PhD
4. Klein, M. (2004) Change Management for Distributed Ontologies, Ph.D. thesis, Mathmatics and Computer Science, Vrije Universiteit, 206 pages. http://www.cs.vu.nl/~mcaklein/thesis/
5. Volz, R. (2004) Web Ontology Reasoning with Logic Databases, Ph.D. thesis, University of Karlsruhe, 287 pages. http://www.ubka.uni-karlsruhe.de/cgi-bin/psview?document=/2004/wiwi/2&search=/2004/wiwi/2
6. Stojanovic, L. (2004) Methods and Tools for Ontology Evolution, Ph.D. thesis, University of Karlsruhe, 249 pages. http://www.ubka.uni-karlsruhe.de/cgi-bin/psview?document=/2004/wiwi/10&search=/2004/wiwi/10
7. Stuckenschmidt, H. (2003) Ontology-Based Information Sharing in Weekly Structured Environments, Ph.D. thesis, Computer Science, Vrije Universiteit, 183 pages. http://www.cs.vu.nl/res/theses/stuckenschmidt.html

Research Groups

1. KSG Conference Papers. http://www.cse.unsw.edu.au/~ksg/Pubs/ksgconf.html
2. Laboratory for Applied Ontology – collection of publications on ontologies. http://www.loa-cnr.it/Publications.html
3. Standard Upper Ontology Working Group (SUO WG), http://suo.ieee.org
4. Semantic Web Science Association (SWSA), http://www.iswsa.org
5. W3 Web-Ontology (WebOnt) Working Group, http://www.w3.org/2001/sw/WebOnt/

Tutorials

1. Guarino, N. (2002) Ontology-Driven Conceptual Modelling – ER2002, http://www.loa-cnr.it/odcm.html Accessed: 11 March 2009.
2. Mizoguchi Laboratory, Ontological Engineering / Ontology Engineering: Tutorial, Mizoguchi Laboratory Accessed: 11 March 2009.
3. Noy, N. F. and McGuinness, D. (2001) Ontology Development 101: A Guide to Creating Your First Ontology, http://protege.stanford.edu/publications/ontology_development/ontology101-noy-mcguinness.html