A Research and Development Bibliography on Ontologies: A
Cross-Disciplinary Perspective
Compiled by Charu Chandra
University of Michigan – Dearborn
The objective of this bibliography is to provide the reader with a sampling of crosscutting
research in the field of Knowledge Management using Ontologies.
The motivation behind this is utilizing the power of conceptualization that Ontologies offer in
developing better knowledge bases and management tools for managing supply chain.
The following bibliography is organized in three facets: theory, development, and application.
Ontology Theory
1. Farquhar, A., Fikes, R., Rice, J.1997. “The Ontolingua Server: A tool for collaborative
ontology construction”, International Journal of Human-Computer Studies, 46(6):
pp.707—727
2. Fikes, R., Farquhar, A., 1999. "Distributed repositories of highly expressive reusable
ontologies", IEEE Intelligent Systems & Their Applications, 14(2) Mar-Apr, pp. 73-79.
3. Gruber, T.R. 1993. “A translation approach to portable ontologies”, Knowledge
Acquisition, 5(2), pp. 199-220.
4. Guarino, N. 1995. “Formal Ontology, Conceptual Analysis and Knowledge
Representation”, International Journal of Human and Computer Studies, 43(5/6), pp. 625640.
5. Sowa, J. 2000. “Ontology, Metadata, and Semiotics”, International Conference on
Conceptual Structures, ICCS'2000, pp. 4-18 August 2000, Darmstadt, Germany
6. Uschold, M., Grüninger M., 1996. "ONTOLOGIES: Principles, Methods and
Applications" Knowledge Engineering review 11(2), pp. 93-155.
7. van Heijst, G, Falasconi, S., Abu-Hanna, A, Schraber, G., Stefanelli, M. 1995. “A Case
Study in Ontology Library Construction”. Artificial Intelligence in Medicine 7, pp. 227255.
8. Sowa, J. F. 2000. “Knowledge representation Logical, Philosophical, and Computational
Foundations”. Brooks/Cole, Pacific Groove, CA, USA
9. Staab, S., Schnurr, H. P., Studer, R., Sure Y. 2001. "Knowledge Processes and
Ontologies." IEEE Intelligent Systems. 16(1) pp. 26-34.
Ontology is discussed from philosophical and artificial intelligence (AI) perspectives. Ontology
for a philosopher is the science of being. That is, about categories of a particular system that
reflects a specific view of the world. For AI community, ontology is an engineering artifact that
describes a certain reality with a specific vocabulary, using a set of assumptions regarding the
intended meaning of words in this vocabulary. Ontology was introduced as a philosophical
theory about the nature of existence. However, AI researchers altered this term (ontology) for
expressing “a shared and common understanding of some domain that can be communicated
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between people and application systems” (Gruber, 1993). Ontology-based systems are utilized
for knowledge acquisition, conceptualization, and representation. Ontology is for transforming
the implicit into the explicit knowledge. Ontological principles are suggested for modeling and
formalizing common sense realities and the nature of reasoning and sharing them among people
and software applications.
Ontology and information System
10. Guarino, N. 1998. “Formal Ontology and Information systems”. Proceedings of FOIS'98,
Amsterdam, IOS Press.
11. Vasconcelos, J. Gouveia, F. Kimble C. 2002. “An Organizational Memory Information
System using Ontologies”. Proceedings of the 3rd Conference of the Associação
Portuguesa de Sistemas de Informação. University of Coimbra, Portugal, November
12. Fonseca, F.T., Egenhofer, M.J., Davis, C., Camara, G. 2002. “Semantic granularity on
ontology-driven geographic information systems”, Annals of Mathematics and Artificial
Intelligence. 36(1):121-134
Ontology applications for designing and utilizing information systems are motivated by the
necessity of integrating various information representations, deployed on different, sometime
heterogeneous platforms and for providing a collaborative environment for collectively working
on common problems and tasks. The use of ontology translated into information system
components leads to the idea of ontology-driven information system. Ontology captures the
semantics of information, represents in a formal language and can be used to store related
metadata. Ontology integrates information sources through semantic mediators. Guarino (1998)
discusses the pivotal role of ontology in information system, how it can be used in two
dimensions: temporal and structural. From the latter perspective ontology plays an important role
in all three components of information system: database, applications, and interfaces. From the
former perspective, ontology is utilized in both development and run times.
Ontology and Agent Technology
13. Jennings, N.R, Wooldridge M. 1995 “Applying Agent Technology, Applied Artificial
Intelligence”, An International Journal, Taylor & Francis London, 9 (4) pp.51-361
14. Wooldridge, M., Jennings, N. R. 1995. "Intelligent Agents: Theory and Practice."
Knowledge Engineering Review 10(2) pp.115-161.
15. Zambonelli, F., Jennings, N. R., Omicini, A., Wooldridge, M. 2000. “Coordination of
Internet Agents: Models”, Technologies and Applications. Springer.
16. Cabri G., Leonardi, L. Zambonelli F. 2000. "Mobile-agent Coordination Models for
Internet Applications", IEEE Computer Magazine, Vol. 33, No. 2, Feb.
17. Barbuceanu, M. and Fox, M.S., 1994 “The Information Agent: An infrastructure agent
supporting collaborative enterprise architectures”, In Third Workshop on Enabling
Technologies: Infrastructure for Collaborative Enterprises,
18. Jennings, R. J. 2000. On agent-based software engineering. Artificial Intelligence 117:
277–296
19. Castro-Schez, J. J., Jennings, N. R., Luo, X. and Shadbolt, N. (2004) Acquiring domain
knowledge for negotiating agents: a case study. Int. J. of Human-Computer Studies.
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Agent technology is mostly discussed without connection with ontologies. Agent-based
computing is viewed as of significant importance when designing distributed software systems.
In such an environment, the ability of agents to autonomously plan and pursue their actions and
goals to cooperate, coordinate, and negotiate with others, as well as their ability to act
intelligently in unpredictable and dynamic environment will lead to significant improvements in
software systems. The relationship of agents with knowledge-based system is discussed in
Barbuceanu et al, (1994) where the information agent interacts with knowledge management
system and delivers to other agents the knowledge they need to effectively achieve their goals.
Description language (ontology constituent) utilization is discussed here for supplying agents
with required information. Internet is viewed as an environment where agents can be deployed
and operated.
Semantic Web
20. Ding, Y. 2001. “A review of ontologies with the Semantic Web in view”, Journal of
Information Science, 27(6): p. 377-388.
21. Fensel, D., Harmelen, F., Horrocks, I., McGuinness, D. L., Patel-Schnaider, P. F. 2001.
“OIL: An Ontology Infructructure for Semantic Web”, IEEE Intelligent systems,
March/April. pp. 38-45.
22. Guarino, N., Masolo, C., Vetere, G. 1999. "OntoSeek: Content-Based Access to the
Web." IEEE Intelligent Systems & Their Applications 14(3): 70-80.
23. Harmelen, F. 2002. “How the Semantic Web will change KR: challenges and
opportunities for a new research agenda”. The Knowledge Engineering Review, 2002.
17(1).
24. Staab, S., Maedche, A. 2001. “Knowledge portals --- ontologies at work”, AI Magazine,
2001. 21(2).
25. Ding, Y., Fensel, D., Klein, M.,Omelayenko, b. 2002. “The Semantic Web: Yet Another
Hip?” - to appear in Data and Knowledge Engineering, 2002, 18.12.01
26. Staab, S., Maedche, A., Handschuh, S. 2001. An annotation framework for the semantic
web. In Proceedings of the First Workshop on Multimedia Annotation, Tokyo, Japan.
27. Stojanovic, N., Maedche, A., Staab, S., Studer, R., Sure, Y. 2001. SEAL: a framework
for developing SEmantic PortALs. In Proceedings of K-CAP 2001.
Web is currently moving towards its second generation – Semantic web, which provides
automated services based on machine process-able data semantics and algorithms for using these
data. Ontologies will be key assets and the backbone for the semantic web. Services provided by
semantic Web overcome limitations of current Web technologies by bringing further
mechanization to some information processing tasks. The role of ontology is to link formal and
real world semantics. For building Semantic Web, Staab et al (2001) offer knowledge portal
implementation, where ontologies lay its conceptual foundation. Knowledge portals are domain
specific knowledge storage places. If knowledge is represented with ontologies, software agents
can utilize it, thus providing automated service to the Web. Implementation of Semantic Web
gives a new frontier for software agent developments. Semantic Web is still in its initial stage of
development. Various technologies are poised to make it a reality viz. UDDI, SOAP, WSDL to
describe entities and their communications, DAML, OIL, UPML, etc ontology languages for
knowledge representation.
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Enterprise modeling
28. Fox, M. S., Gruninger, M. 1999. "Ontologies for Enterprise Integration". Department of
Industrial Engineering,, University of Ontario.
29. Malone, T. W., Crowston, K., Lee, J., Pentland, B., Dellarocas, C., Wyner, G., Quimby,
J., Osborn, C. S., Bernstain, A., Hermen, G., Klein, M., O’Donnel, E. 1999. “Tools for
Inventing organizations: Towards a Handbook of Organizational Processes”,
Management Science, 45(3), pp. 425-443
30. Ushold, M., King, M., Moralee, S., Zorgios, Y. 1998. "The Enterprise Ontology." The
Knowledge Engineering Review, 13 (Special Issue on Putting Ontologies to Use).
31. Gruninger, M., Fox, M. S. 1996. The Logic of Enterprise Modelling. In Modelling and
Methodologies for Enterprise Integration, pages 140—157.
32. Gruninger, M., Atefi, K., Fox, M. S., 2000. “Ontologies to Support Process Integration in
Enterprise Engineering”, Computational & Mathematical Organization Theory 6, 381–
394, 2000.
33. Kim, H. M., Fox, M. S., Grüninger, M. 2001. "Construction of Ontology Based
Enterprise Models for E-Commerce", Work Report, Schulich School of Business, York
University, 4700 Keele St., Toronto, Ontario Canada, M3J 1P3.
Ontology application for modeling an enterprise is presented by Uschold et al. A collection of
terms and definitions is formulated relevant to a business enterprise. The role of enterprise
modeling is to achieve model-driven enterprise design, analysis, and operations. These models
provide reusability of developed models, consistent usage by various applications, and
accessibility by humans and software tools (if the models are designed as ontologies). Domain
structures, organizational processes are captured in these models, e.g. TOVE ontology
(Gruninger and Fox 1996) describes activities, states, and time within first-order logic.
Ontology and agents for supply chain management
34. Fox, M., Barbuceanu, M., Teigen, R. 2000. “Agent-Oriented Supply-Chain
Management”, The International Journal of Flexible Manufacturing Systems, 12, pp.
165–188.
35. Swaminathan, J. M., Smith, S. F., Sadeh, N. M. 1998. “Modeling Supply Chain
Dynamics:A Multiagent Approach” Decision Sciences, 29 (3), Summer.
36. Norman, T. J., Preece, A., Chalmers, S., Jennings, N. R., Luck, M., Dang, V. D., Nguyen,
T. D., Deora, V., Shao, J., Gray, A. and Fiddian, N. (2004) Agent-based formation of
virtual organisations. Int. J. Knowledge Based Systems.
The architecture for Integrated Supply Chain Management is viewed as set of activities managed
by intelligent agents responsible for one or more tasks. Agents are utilizing knowledge
encapsulated in ontologies and micro-theories, and integrate SC activities: planning and
workflow. Supply chain can be designed as a collection of various models that capture generic
supply chain processes and concepts, thereby promoting modular construction and reuse of
models for a wide range of applications.
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Ontology-driven knowledge design for SCM
37. Chandra, C., Tumanyan, A., (2001) " Managing Logistics for Mass Customization: The
New Production Frontier" Proceedings of the Technology Day, University of MichiganDearborn, Dearborn, USA.
38. Chandra C., Tumanyan A. (2002) Supply Chain Reconfiguration: Designing Information
Support with System Taxonomy Principles Proceedings of the 11th Annual Industrial
Engineering Research Conference IERC-2002, Orlando, Florida, USA.
39. Chandra, C., Grabis, J., Marukyan, R., Tumanyan, A. (2002), Supply chain taxonomy:
Development and applications, Presentation at Research and Application Congress, SAP
Institute for Innovation and Development, Tampa, Florida, Feb. 23-25, 2002.
40. Chandra, C., Tumanyan, A., (2002) "Analytical Modeling of Logistics for Reconfigurable Supply Chain in Mass Customization of Vehicles" Proceedings of the
Technology Day, University of Michigan-Dearborn, Dearborn, USA.
41. Chandra C., Tumanyan A. (2003) Supply Chain System Taxonomy: development and
application Proceedings of the 12th Annual Industrial Engineering Research Conference
IERC-2003, Portland, Oregon USA.
42. Chandra, C., Grabis, J., Tumanyan, A. (2003) Analytical Modeling of Logistics for ReConfigurable Supply Chain in Mass Customization of Vehicles Proceedings of the
Technology Day, University of Michigan – Dearborn, Dearborn, USA.
43. Chandra, C. Tumanyan, A. 2004. “Modeling and Design of Ontology-driven, Problemoriented Information System for Supply Chain. Extended Abstract Submitted to Journal
of the Association for Information Systems special issue on "Ontologies in the Context of
Information Systems".
44. Chandra C., Tumanyan A. (2004) “Information Modeling to Manage Supply Chain:
Problems Taxonomy” submitted to Proceedings of the 13th Annual Industrial
Engineering Research Conference IERC-2004, May 16-18, 2004, Houston, Texas, USA.
45. Chandra C., Tumanyan A. (2004) “Ontology Driven Knowledge Design and
Development for Supply Chain Management” submitted to Proceedings of the 13th
Annual Industrial Engineering Research Conference IERC-2004, May 16-18, 2004,
Houston, Texas, USA.
Ontological principles are applied for SC system analysis, information model design,
development, and utilization. The main idea of this research initiative it to provide an integrated
environment, where SC members can share data and knowledge, and collaboratively work on
common problems. System taxonomy introduced in early work is applied to systematically
represent the body of information. Problem-orientation is viewed as the main informational
requirement that is to be met. Ontology is applied as a methodology for capturing, assembling,
representing, and sharing problem domain specific knowledge among SC members. This
approach is unique in terms of: (1) it encompasses all aspects of SC, when dealing with problem
relationships, (2) developed ontology can be directly used by decision modeling tools, (3)
Ontologies can be used to model business processes, by decomposing the process and collecting
process component models, which are ontology objects, (4) this framework supports
generalization and specialization, thus supporting reusability of developed ontologies, (5) The
ontology representation is in widely accepted XML format, and (6) proposed ontology
development framework supports integration of information from diverse sources.
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