Name
Abstract | ||
|---|---|---|
The use of hierarchical taxonomies to organise information (or sets of objects) is a common approach for the semantic web and elsewhere, and is based on progressively finer granulations of objects. In many cases, seemingly crisp granulation disguises the fact that categories are based on loosely defined concepts that are better modelled by allowing graded membership. A related problem arises when different taxonomies are used, with different structures, as the integration process may also lead to fuzzy categories. Care is needed when information systems use fuzzy sets to model graded membership in categories - the fuzzy sets are not disjunctive possibility distributions, but must be interpreted conjunctively. We clarify this distinction and show how an extended mass assignment framework can be used to extract relations between fuzzy categories. These relations are association rules and are useful when integrating multiple information sources categorised according to different hierarchies. Our association rules do not suffer from problems associated with use of fuzzy cardinalities. Experimental results on discovering association rules in film databases and terrorism incident databases are demonstrated. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-89765-1_14 | URSW (LNCS Vol.) |
Keywords | Field | DocType |
multiple taxonomies,fuzzy set,mass assignment approach,different taxonomy,information system,different structure,different hierarchy,multiple information source,fuzzy cardinalities,granular association rules,association rule,graded membership,fuzzy category,fuzzy,granules,association rules,hierarchies,semantic web | Information system,Data mining,Fuzzy classification,Computer science,Fuzzy logic,Semantic Web,Cardinality,Fuzzy set,Association rule learning,Membership function | Conference |
Volume | ISSN | Citations |
5327 | 0302-9743 | 4 |
PageRank | References | Authors |
0.39 | 17 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Trevor P. Martin | 1 | 134 | 26.98 |
| Yun Shen | 2 | 185 | 18.88 |
| Ben Azvine | 3 | 16 | 4.63 |