Abstract
Large scientific knowledge bases (KBs) are bound to contain inconsistencies and under-specified knowledge. Inconsistencies are inherent because the approach to modeling certain phenomena evolves over time, and at any given time, contradictory approaches to modeling a piece of domain knowledge may simultaneously exist in the KB. Underspecification is inherent because a large, complex KB is rarely fully specified, especially when authored by domain experts who are not formally trained in knowledge representation. We describe our approach for inconsistency monitoring in a large biology KB. We use a combination of anti-patterns that are indicative of poor modeling and inconsistencies due to underspecification. We draw the following lessons from this experience: (1) knowledge authoring must include an intermediate step between authoring and run time inference to identify errors and inconsistencies; (2) underspecification can ease knowledge encoding but requires appropriate user control; and (3) since real-life KBs are rarely consistent, a scheme to derive useful conclusions in spite of inconsistencies is essential.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Blair, H.A., Subrahmanian, V.: Paraconsistent logic programming. Theoretical Computer Science 68(2), 135–154 (1989)
Casati, R., Varzi, A.C.: Parts and places: the structures of spatial representations. Bradford Books (1999)
Chaudhri, V.K., Cheng, B., Overholtzer, A., Roschelle, J., Spaulding, A., Clark, P., Greaves, M., Gunning, D.: Inquire Biology: A textbook that answers questions. AI Magazine 34(3) (September 2013)
Chaudhri, V.K., Dinesh, N.: AURA knowledge engineering manual. Technical report, SRI International (2013)
Chaudhri, V.K., Dinesh, N., Heller, C.: Conceptual models of structure and function. In: Second Annual Conference on Advances in Cognitive Systems (2013)
Chaudhri, V.K., Dinesh, N., Inclezan, D.: Three lessons in creating a knowledge base to enable explanation, reasoning and dialog. In: Second Annual Conference on Cognitive Systems (December 2013)
Chaudhri, V.K., Heymans, S., Wessel, M., Tran, S.C.: Large object oriented knowledge bases: A challenge for reasoning engines. In: Newsletter of Association of Logic Programming (October 2013)
Chaudhri, V.K., Heymans, S., Wessel, M., Tran, S.C.: Object-oriented knowledge bases in logic programming. In: Technical Communication of International Conference in Logic Programming (2013)
Chaudhri, V.K., Tran, S.C.: Specifying and reasoning with under-specified knowledge bases. In: International Conference on Knowledge Representation and Reasoning (2012)
Chaudhri, V.K., Wessel, M.A., Heymans, S.: KB_Bio_101: A challenge for OWL reasoners. In: The OWL Reasoner Evaluation Workshop (2013)
Corcho, O., Roussey, C., Vilches-Blázquez, L., Dominguez, I.P.: Pattern-based OWL ontology debugging guidelines. In: CEUR Workshop Proceedings (2009)
Friedland, N., Allen, P., Mathews, G., Whitbrock, M., Baxter, D., Curtis, J., Shepard, B., Miraglia, P., Angele, J., Staab, S., Moench, E., Opperman, H., Wenke, D., Israel, D., Chaudhri, V., Porter, B., Barker, K., Fan, J., Chaw, S.Y., Yeh, P., Tecuci, D., Clark, P.: Project Halo: Towards a digital Aristotle. AI Magazine (2004)
Gebser, M., Kaufmann, B., Neumann, A., Schaub, T.: Conflict-driven answer set solving. IJCAI, 386–392 (2007)
Guizzardi, G.: Ontological foundations for structural conceptual models. CTIT, Centre for Telematics and Information Technology (2005)
Guizzardi, G.: On the representation of quantities and their parts in conceptual modeling. In: FOIS, pp. 103–116 (2010)
Guizzardi, G.: Ontological foundations for conceptual part-whole relations: the case of collectives and their parts. In: Advanced Information Systems Engineering, pp. 138–153. Springer (2011)
Gunning, D., Chaudhri, V.K., Clark, P., Barker, K., Chaw, S.-Y., Greaves, M., Grosof, B., Leung, A., McDonald, D., Mishra, S., Pacheco, J., Porter, B., Spaulding, A., Tecuci, D., Tien, J.: Project Halo update: Progress toward digital Aristotle. In: AI Magazine (Fall 2010)
Kassoff, M., Genesereth, M.R.: Paraconsistent inference from data using existential Ω-entailment. International Journal of Semantic Computing 5(03), 257–269 (2011)
Maier, F., Ma, Y., Hitzler, P.: Paraconsistent OWL and related logics. Semantic Web (2012)
McGuinness, D.L., Fikes, R., Rice, J., Wilder, S.: The Chimaera ontology environment. In: AAAI/IAAI 2000, pp. 1123–1124 (2000)
Noy, N.F., Sintek, M., Decker, S., Crubézy, M., Fergerson, R.W., Musen, M.A.: Creating semantic web contents with Protege-2000. IEEE Intelligent Systems 16(2), 60–71 (2001)
Parsia, B., Sirin, E., Kalyanpur, A.: Debugging OWL ontologies. In: Proceedings of the 14th International Conference on World Wide Web, pp. 633–640. ACM (2005)
Reece, J.B., Urry, L.A., Cain, M.L., Wasserman, S.A., Minorsky, P.V., Jackson, R.B.: Campbell Biology. Benjamin Cummings imprint of Pearson, Boston (2011)
Schlobach, S., Cornet, R.: Non-standard reasoning services for the debugging of description logic terminologies. In: IJCAI, vol. 3, pp. 355–362 (2003)
Shearer, R., Motik, B., Horrocks, I.: Hermit: A highly-efficient OWL reasoner. In: OWLED, vol. 432 (2008)
Spear, A.D.: Ontology for the twenty first century: An introduction with recommendations. Institute for Formal Ontology and Medical Information Science: Saarbrucken, Germany (2006)
Suwa, M., Scott, A.C., Shortliffe, E.H.: An approach to verifying completeness and consistency in a rule-based expert system. AI Magazine 3(4), 16 (1982)
Wang, H., Horridge, M., Rector, A.L., Drummond, N., Seidenberg, J.: Debugging OWL-DL ontologies: A heuristic approach. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 745–757. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Chaudhri, V.K., Katragadda, R., Shrager, J., Wessel, M. (2014). Inconsistency Monitoring in a Large Scientific Knowledge Base. In: Janowicz, K., Schlobach, S., Lambrix, P., Hyvönen, E. (eds) Knowledge Engineering and Knowledge Management. EKAW 2014. Lecture Notes in Computer Science(), vol 8876. Springer, Cham. https://doi.org/10.1007/978-3-319-13704-9_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-13704-9_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13703-2
Online ISBN: 978-3-319-13704-9
eBook Packages: Computer ScienceComputer Science (R0)