Systematic Approach for Measuring Semantic Relatedness between Ontologies

A Semantic Network Approach to Measuring Relatedness

Humans are very good at judging the strength of relationships between two terms, a task which, if it can be automated, would be useful in a range of applications. Systems attempting to solve this problem automatically have traditionally either used relative positioning in lexical resources such as WordNet, or distributional relationships in large corpora. This paper proposes a new approach, whe...

Generic Semantic Relatedness Measure for Biomedical Ontologies

This paper presents a new method to measure semantic relatedness between concepts of an ontology with a rich set of relationship types, and preforms a preliminary assessment of its validity. The measure was designed to be applicable to all biomedical ontologies, and to be flexible enough as to allow for different applications to address their own requirements by tuning, for example, the weight ...

A Framework for Measuring the Semantic Relatedness between Concepts

Measuring Semantic Relatedness using Mined Semantic Analysis

Mined Semantic Analysis (MSA) is a novel distributional semantics approach which employs data mining techniques. MSA embraces knowledge-driven analysis of natural languages. It uncovers implicit relations between concepts by mining for their associations in target encyclopedic corpora. MSA exploits not only target corpus content but also its knowledge graph (e.g., "See also" link graph of Wikip...

Measuring Semantic Relatedness Across Languages

Measures of Semantic Relatedness are well established in Natural Language Processing. Their purpose is to determine the degree of relatedness between two words without specifying the nature of their relationship. Most of these measures work only between pairs of words in a single language. We propose a novel method of measuring semantic relatedness between pairs of words in two different langua...