Web Services Composition Methods and Techniques: A Review <s> 2.F: Similarity Measure <s> The goal of this research is to propose a new ontology-based semantic similarity measure and apply it into the biomedical domain. We also apply the ontology-based semantic similarity measures from NLP into the biomedicine domain within the framework of the UMLS. The proposed measure is based on the path length between the concept nodes as well as the depth of the lcs node in the ontology hierarchy tree. The proposed similarity method was evaluated relative to human experts' ratings, and compared with the existing measures on sets of concepts using the MeSH terminology within the UMLS. The experimental results validate the efficiency of the proposed method, and demonstrate that our semantic similarity measure, compared with the existing techniques, gives the best overall results of correlation with experts' ratings. <s> BIB001 </s> Web Services Composition Methods and Techniques: A Review <s> 2.F: Similarity Measure <s> In this paper we analyze the contribution of semantic, syntactic and word similarity of document features in closed and open domain question answering. Semantic similarity is computed as the similarity of the action in the candidate sentence to the action asked in the question, measured using WordNet::Similarity on main verbs. The syntactic similarity feature measures the unifiability of a candidate's parse tree with the question's parse tree. It uses syntactic restrictions as well as lexical measures to compute the unifiability of critical syntactic participants in the parse trees. Finally, the word similarity of the document containing a candidate sentence is computed as the cosine of the angle between the question keywords vector and the document vector. Since the semantic feature is more reliable on content verbs and syntactic similarity is suitable for questions with a subject- verb-object syntactic structure, we only consider questions with a main content verb in our analysis (non-copulative questions). This type comprise 70% of our closed domain and 33% of our open domain test questions. The combination of these three features achieves an MRR of 28% in our closed domain and 23% in open domain. Our analysis shows that the syntactic feature has a significant contribution in both open and closed domains. However, the path-based lch semantic similarity measure we used, only contributes in our closed domain probably because of less variation in the vocabulary and topic. Document IR score on the other hand, has more contribution in open domain, because query keywords are more discriminating in a large document set with a vast vocabulary range. <s> BIB002
Similarity is a measure that quantifies the dependency (independency) between two terms or two concepts. The similarity measure can represent similarity between two documents, two queries or one document and one query and also is a function that computes the degree of similarity between a pair of text objects. Two ways to measuring similarity follows: • Syntactic Similarity Measure The syntactic similarity feature measures the unifiability of a candidate's parse tree with the question's parse tree and it uses syntactic restrictions as well as lexical measures to compute the unifiability of critical syntactic participants in the parse trees BIB002 . • Semantic Similarity Measure Semantic similarity is the similarity of the action in the candidate sentence to the action asked in the question and is measured Similarity on main verbs BIB002 . The semantic similarity measuring techniques can be classified into three classes as follows: The first and normal way measures the semantic similarity by using ontology or taxonomy (e.g. WordNet) to calculate the distance. The second class of techniques uses training corpora and information content (IC) to estimate the semantic similarity and relatedness between two concepts. In this class, the techniques use machine learning, statistical-based, rule-based or other corpus-based approaches. The third class simply includes the techniques that employ a combination form the first two classes BIB001 .