Software Development

Ontologies for Software Engineering and Software Technology by Mario Piattini, Coral Calero, Francisco Ruiz

Posted On February 25, 2017 at 5:26 pm by / Comments Off on Ontologies for Software Engineering and Software Technology by Mario Piattini, Coral Calero, Francisco Ruiz

By Mario Piattini, Coral Calero, Francisco Ruiz

This booklet covers purposes of ontologies in software program engineering and software program know-how: sharing wisdom of the matter area and utilizing a standard terminology between all stakeholders; and filtering the information whilst defining types and metamodels. by means of featuring the complicated use of ontologies in software program examine and software program tasks, this e-book is of profit to software program engineering researchers in either academia and industry.

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Extra resources for Ontologies for Software Engineering and Software Technology

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34. 35. 36. 37. 38. 39. Oscar Corcho, Mariano Fernández-López, Asunción Goméz-Pérez (ISWC’04), Hiroshima, Japan. (Lecture Notes in Computer Science LNCS 3298) Springer-Verlag, Berlin, Germany, pp. 683–697 Euzenat J (2004) An API for Ontology Alignment. In: McIlraith SA, Plexousakis D (eds) 3rd International Semantic Web Conference (ISWC’04), Hiroshima, Japan. (Lecture Notes in Computer Science LNCS 3298) Springer-Verlag, Berlin, Germany, pp. 698–712 Farquhar A, Fikes R, Rice J (1997) The Ontolingua Server: A Tool for Collaborative Ontology Construction.

Similarity aggregation. , one for the similarity of their names and one for the similarity of their natural language descriptions. These different similarity values for one candidate pair must be aggregated into a single aggregated similarity value. A way to obtain the aggregated value is through the weighted means of the similarity values. This is basically QOM’s approach. Neither S-Match nor Pal and colleagues’ method combine similarities. x Activity 5. Interpretation. Individual or aggregated similarity values are used to derive mappings between terms.

Which is enriched with the learned concepts. New concepts are identified using natural language analysis techniques over the resources previously identified by the user. The resulting ontology is pruned and then focused on a specific domain by means of several approaches based on statistics. Finally, relations between concepts are established applying learning methods. Such relations are added to the resulting ontology. Aussenac-Gilles and colleagues’ method [3, 4] is based on knowledge elicitation from technical documents.

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