Quantifying the semantic relatedness of two terms is a field with vast amounts of research, as the knowledge provided by it has applications for many Natural Language Processing problems. While many algorithmic measures have been proposed, it is often hard to say if one measure outperforms another, since their evaluation often lacks meaningful comparisons to human judgement on semantic relatedn...

We introduce a novel approach to measuring semantic relatedness of terms based on an automatically generated, large-scale semantic network. We present promising first results that indicate potential competitiveness with approaches based on manually created resources.

This paper introduces a novel method for measuring semantic relatedness using semantic profiles constructed from salient encyclopedic features. The model is built on the notion that the meaning of a word can be characterized by the salient concepts found in its immediate context. In addition to being computationally efficient, the new model has superior performance and remarkable consistency wh...

This paper presents REWOrD, an approach to compute semantic relatedness between entities in the Web of Data representing real word concepts. REWOrD exploits graph nature RDF data and SPARQL query language access this data. Through simple queries, constructs weighted vectors keeping informativeness predicates used make statements about being compared. The most informative path is also considered...

Vector space methods that measure semantic similarity and relatedness often rely on distributional information such as co–occurrence frequencies or statistical measures of association to weight the importance of particular co–occurrences. In this paper, we extend these methods by incorporating a measure of semantic similarity based on a human curated taxonomy into a second–order vector represen...

There are many methods having different approaches for assessing similarity and relatedness and they are used in many application areas, including web service discovery, invocation and composition, word sense disambiguation, information retrieval, ontology alignment and merging, document clustering, and short answer grading. These methods can be categorized as path-based, information content-ba...