Name
Abstract | ||
|---|---|---|
Despite of the various benefits obtainable from Formal Concept Analysis (FCA) in knowledge base construction, FCA-based approaches are not enough to help an expert enrich his knowledge. This is because they provide only the clusters constructed with user-defined knowledge and super-sub relation between the clusters. In this paper, we propose an approach that provides a user with a guideline by suggesting undiscovered knowledge in the form of predicates. This approach firstly generates a set of candidate predicates by analyzing a pre-defined predicate by users. Second, it discards unqualified ones from the set of candidate predicates using a filtering method dealing with two criteria, uniqueness and support. The qualified candidate predicates are suggested, and selected by the user, and finally, his knowledge is enriched by merging the selected predicates with pre-defined ones. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/GRC.2011.6122615 | GrC |
Keywords | Field | DocType |
supersub relation,filtering method,knowledge base construction,user defined knowledge,expert systems,predicate derivation,information filtering,formal concept analysis,undiscovered knowledge,fca based approach,logical scaling,knowledge base | Data mining,Computer science,Artificial intelligence,Knowledge base,Uniqueness,Information retrieval,Expert system,Knowledge-based systems,Filter (signal processing),Knowledge extraction,Predicate (grammar),Formal concept analysis,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4577-0372-0 | 0 | 0.34 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eung-Hee Kim | 1 | 4 | 1.80 |
| Hong-Gee Kim | 2 | 104 | 18.80 |
| Suk-hyung Hwang | 3 | 10 | 3.91 |