Smart combination of web measures for solving semantic similarity problems
Purpose ‐ Semantic similarity measures are very important in many computer-related fields. Previous works on applications such as data integration, query expansion, tag refactoring or text clustering have used some semantic similarity measures in the past. Despite the
usefulness of semantic similarity measures in these applications, the problem of measuring the similarity between two text expressions remains a key challenge. This paper aims to address this issue. Design/methodology/approach ‐ In this article, the authors propose an optimization
environment to improve existing techniques that use the notion of co-occurrence and the information available on the web to measure similarity between terms. Findings ‐ The experimental results using the Miller and Charles and Gracia and Mena benchmark datasets show that the
proposed approach is able to outperform classic probabilistic web-based algorithms by a wide margin. Originality/value ‐ This paper presents two main contributions. The authors propose a novel technique that beats classic probabilistic techniques for measuring semantic similarity
between terms. This new technique consists of using not only a search engine for computing web page counts, but a smart combination of several popular web search engines. The approach is evaluated on the Miller and Charles and Gracia and Mena benchmark datasets and compared with existing probabilistic
web extraction techniques.