Calegari, S.; Sanchez, E.: Object-fuzzy concept network : an enrichment of ontologies in semantic information retrieval (2008)
0.01
0.005731291 = product of:
0.017193872 = sum of:
0.017193872 = product of:
0.051581617 = sum of:
0.051581617 = weight(_text_:retrieval in 2393) [ClassicSimilarity], result of:
0.051581617 = score(doc=2393,freq=8.0), product of:
0.15433937 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.051022716 = queryNorm
0.33420905 = fieldWeight in 2393, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.0390625 = fieldNorm(doc=2393)
0.33333334 = coord(1/3)
0.33333334 = coord(1/3)
- Abstract
- This article shows how a fuzzy ontology-based approach can improve semantic documents retrieval. After formally defining a fuzzy ontology and a fuzzy knowledge base, a special type of new fuzzy relationship called (semantic) correlation, which links the concepts or entities in a fuzzy ontology, is discussed. These correlations, first assigned by experts, are updated after querying or when a document has been inserted into a database. Moreover, in order to define a dynamic knowledge of a domain adapting itself to the context, it is shown how to handle a tradeoff between the correct definition of an object, taken in the ontology structure, and the actual meaning assigned by individuals. The notion of a fuzzy concept network is extended, incorporating database objects so that entities and documents can similarly be represented in the network. Information retrieval (IR) algorithm, using an object-fuzzy concept network (O-FCN), is introduced and described. This algorithm allows us to derive a unique path among the entities involved in the query to obtain maxima semantic associations in the knowledge domain. Finally, the study has been validated by querying a database using fuzzy recall, fuzzy precision, and coefficient variant measures in the crisp and fuzzy cases.
- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
