DIVERSITY: An Objective Measure of Semantic Distance in Ontologies
📜 Abstract
Semantic Web ontologies offer the potential to provide more precise answers to complex queries than statistical search engines. The labeling of semantic content can support similarity inference, improving searching and information retrieval quality. In this paper, we argue for the development of a measure of diversity that quantifies the semantic distance between concepts in an ontology. We define such a measure based on an extension of traditional methodologies in distance and similarity measurement across taxonomical trees. This measure is intended to help distinguish between semantic properties accurately, and facilitate innovative solutions in diverse areas such as knowledge representation in artificial intelligence, natural language processing, and diverse web applications.
✨ Summary
This paper introduces a concept of measuring semantic distance termed ‘diversity’ within ontological structures. The authors propose a methodology to quantify this semantic distance to improve information retrieval and searching quality using Semantic Web ontologies. The motivation behind this measure is to provide more accurate answers to complex queries compared to traditional statistical search engines. Although direct references and applications of this work in later research or industry applications are limited, the paper’s ideas align with ongoing developments in information retrieval and Semantic Web advancements, where precision in knowledge representation is crucial. Further information about the paper’s influence could not be confirmed through direct citations, indicating it may serve as one of many foundational pieces contributing to the evolving discourse in these fields. Since the publication date is in 2011, it might have been overshadowed by more recent advances in ontology research and Semantic Web technologies. No direct influential citations of this work were found through a quick web search.