Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-13704-9_6 | Lecture Notes in Artificial Intelligence |
Field | DocType | Volume |
Data mining,Knowledge representation and reasoning,Underspecification,Domain knowledge,Sociology of scientific knowledge,Inference,Computer science,Subject-matter expert,Data integrity,Knowledge base | Conference | 8876 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.38 |
References | Authors | |
18 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vinay K. Chaudhri | 1 | 587 | 246.49 |
| Rahul Katragadda | 2 | 1 | 0.38 |
| Jeff Shrager | 3 | 1 | 0.38 |
| Michael A. Wessel | 4 | 23 | 2.63 |