Customizable Web Services Description, Discovery and Composition : An Attribute Based Formalism

In this paper, we build a hybrid Web-based metric for computing semantic relatedness between words. The method exploits page counts, titles, snippets and URLs returned by a Web search engine. Our technique uses traditional information retrieval methods and is enhanced by page-count-based similarity scores which are integrated with automatically extracted lexico-synantic patterns from titles, snippets and URLs for all kinds of semantically related words provided by WordNet (synonyms, hypernyms, meronyms, antonyms). A support vector machine is used to solve the arising regression problem of word relatedness and the proposed method is evaluated on standard benchmark datasets. The method achieves an overall correlation of 0.88, which is the highest among other metrics up to date.

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Network-based distributional semantic models

Iosif¹,

Ιωσήφ²

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στοχό την αναπαράσταση των σημασιολογικών γειτονιών πολυλεκτικών όρων αποτελούμενων από ουσιαστικά, καθώς και η εκτίμηση της σημασιολογικής ομοιότητας αυτών. Πολύ καλά αποτελέσματα επιτυγχάνονται για τις ανωτέρω εφαρμογές καταδεικνύοντας την προσαρμοστικότητα των προτεινόμενων μοντέλων.

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Customizable Web Services Description, Discovery and Composition : An Attribute Based Formalism

Abstract: We present in this article a theoretical framework for customizable Web services description, discovery and composition based on an attributive formalism.

Cited by 5 publications

References 27 publications

SWS Based Business Service Composition

SWS Based Business Service Composition

A Hybrid Web-Based Measure for Computing Semantic Relatedness Between Words

Network-based distributional semantic models

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