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.
CITATION STYLE
Chaudhri, V. K., Katragadda, R., Shrager, J., & unknown. (2014). Inconsistency monitoring in a large scientific knowledge base. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8876, pp. 66–79). Springer Verlag. https://doi.org/10.1007/978-3-319-13704-9_6
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