Web Service Classification using Efficient Neural Networks

Classification is a widely used mechanism for facilitating Web service discovery. Existing methods for automatic Web service classification only consider the case where the category set is small. When the category set is big, the conventional classification methods usually require a large sample collection, which is hardly available in real world settings. This paper presents a novel method to conduct service classification with a medium or big category set. It uses the descriptive information of categories in a large-scale taxonomy as sample data, so as to disengage from the dependence on sample service documents. A new feature selection method is introduced to enable efficient classification using this new type of sample data. We demonstrate the effectiveness of our classification method through extensive experiments. category set is very large. We use the descriptive information of each category as the sample documents of its parent category, so that our system does not rely on a collection of pre-classified services. A new feature selection method is proposed to map a high dimension feature space to a low dimension space based on semantic similarity of concepts. OVERVI EW O F TH E CLAS SI FIC ATI O N SY ST EM In this section, we introduce the taxonomy and classification algorithm of our classification system, and give a brief overview of the classification process. INTRO D UC T I ON With the increasing number of available Web services on the internet, Web service discovery becomes a challenging issue. It is time consuming to traverse an entire Web service collection to find a matching service. To speed up service discovery, classification can be applied. The existing work on Web service classification has mainly focused on text classification and property similarity computing methods. In [1], the textual description in a WSDL document is mapped into a feature vector and then automatically classified into domain-specific categories using SVM method, but it ignores the semantic features of the service document and the classification accuracy is low. In [2], it uses binary feature vector and judges the occurrence of term only if the document directly contains this term or contains some terms are equivalent with this term in ontology. This will lead to information loss when semantically related concepts occur in the document. In [3], the similarity between two services is measured in terms of the similarity of their operations and parameters. However, A. The UNSPSC Taxonomy It is desirable that the classification of services be based on a standard and widely used taxonomy, so that users could easily find the requested services in the expected categories. UNSPSC is one of the standard taxonomies established for E-commerce products and services. The structure of UNSPSC is a five-level and tree-structured hierarchical classification. The five levels are root node, segment, family, class and commodity respectively. In this paper, we use UNSPSC as the classification criteria for Web services. As the categories in commodity level are concrete products or services, we only categorize services into the class level of the UNSPSC. B. The SVM Classification Algorithm We use the SVM(Support Vector Machine) text classification algorithm to classify the service documents. The SVM method was first introduced to conduct text classification by Joachims[4]. The process of text classification using SVM method can be summarized as follows: First, we get a docuthe measure for comparing the complete set of parameters is complex and domain dependent. This increases the difficulty ment collection Ω = {d1 , . , d |Ω| } ⊂ D such that each document di corresponds to a category cj in C = {c1 , . , c|C | } of implementation. To the best of our knowledge, the existing methods only considered the case when the category set is very and a feature space T = {t1 , t2 , . t|T | } which contains all the different terms in Ω. Then, each sample document is mapped small, while a practical classification system in the real word setting usually needs to deal with thousands of categories. to a feature vector W = {w1 , w2 , . w |T | }, where wi denotes This paper presents a hierarchical classification system to classify Web services based on their functional features. The system utilizes the taxonomy of UNSPSC1 of which the 1 United Nations Standard Products and Service Code. http://www.unspsc. org the weight of the term ti in this document. (If wi =0, it denotes that term ti does not appear in this document.) Following that, all the feature vectors are input to the SVM to train the classifier. Finally, the feature vector of an unclassified document is passed to the SVM classifier to find the category of the document. ISSN 2278-3091