Anchor-PROMPT: using non-local context for semantic Matching. Researchers in the ontology-design field have developed the content for ontologies in many domain areas. Recently, ontologies have become increasingly common on the WorldWide Web where they provide semantics for annotations in Web pages. This distributed nature of ontology development has led to a large number of ontologies covering overlapping domains, which researchers now need to merge or align to one another. The processes of ontology alignment and merging are usually handled manually and often constitute a large and tedious portion of the sharing process. We have developed and implemented Anchor-PROMPT---an algorithm that finds semantically similar terms automatically. Anchor-PROMPT takes as input a set of anchors---pairs of related terms defined by the user or automatically identified by lexical matching. AnchorPROMPT treats an ontology as a graph with classes as nodes and slots as links. The algorithm analyzes the paths in the subgraph limited by the anchors and determines which classes frequently appear in similar positions on similar paths. These classes are likely to represent semantically similar concepts. Our experiments show that when we use Anchor-PROMPT with ontologies developed independently by different groups of researchers, 75% of its results are correct.
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References in zbMATH (referenced in 6 articles )
Showing results 1 to 6 of 6.
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- Lee, Yoonkyong; Sayyadian, Mayssam; Doan, AnHai; Rosenthal, Arnon S.: eTuner: tuning schema matching software using synthetic scenarios (2006)