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A neural model for semantically enhancing Web APIs

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2 Author(s)
Chifu, V.R. ; Dept. of Comput. Sci., Tech. Univ. of Cluj-Napoca, Cluj-Napoca, Romania ; Letia, I.A.

The paper describes an unsupervised neural model for classifying the methods of Web APIs into a large number of classes specified by a domain ontology. As a result of the classification, each method of a Web service is associated to one ontology concept, the name of the concept being further used to semantically annotate the method. The ontology concepts define some functionalities to be offered by different API methods. The names of these concepts are linguistically denoted by verbs or verb phrases that define the action performed by a method. The framework is based on a model of hierarchical self-organizing maps. The methods of the web APIs are encoded in a bag-of-words style, by counting the words that occur in their javadoc documentation. We experimented this automatic semantic annotation model with a data set consisting of APIs of RDF storage tools. The ontology and the APIs to be classified in our experiments are collected from this dataset.

Published in:

Intelligent Computer Communication and Processing (ICCP), 2011 IEEE International Conference on

Date of Conference:

25-27 Aug. 2011