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- W992371452 abstract "The usefulness of knowledge models for information retrieval tasks such as digital resource tagging, query expansion, and recommending, among others, requires that the query concept be present in the model, i.e., exists matching. If the query concept is absent in the ontology, exists matching problem. In this case, it can be identified in the model other sematic and syntactically closeness concepts to the query concept. Once identified the closest concept is possible to extract relevant knowledge from the ontology. The goal of this work is to propose a solution to the problem mentioned, by identifying those variables that can affect the closeness between a query and concepts in a domain ontology. Using these variables as a starting point, we propose 6 indexes for measuring the degree of closeness. We present the results of implementing a search-selection algorithm using indexes based on exact words, contained words, coincidences in descriptive fields, new words and approximate depth. These indices are validated via a case study, and, from these results, we recommend adjustments needed for building a global concept closeness index in future works." @default.
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- W992371452 date "2012-05-27" @default.
- W992371452 modified "2023-09-24" @default.
- W992371452 title "Preliminary Ideas on Concept to Query Closeness Metrics" @default.
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