Authors:
Hilda Kosorus
1
;
Andreas Bögl
2
and
Josef Küng
1
Affiliations:
1
Johannes Kepler University, Austria
;
2
MEOVI, Austria
Keyword(s):
Query recommendation, Semantic similarity, Short text similarity, Taxonomy.
Related
Ontology
Subjects/Areas/Topics:
Applications of Expert Systems
;
Artificial Intelligence and Decision Support Systems
;
Enterprise Information Systems
;
Natural Language Interfaces to Intelligent Systems
Abstract:
Semantic similarity has been extensively studied in the past decades and has become a rapidly growing field of research. Sentence or short text similarity measures play an important role in text-based applications, such as text mining, information retrieval and question answering systems. In this paper we consider the problem of semantic similarity between queries in a question answering system with the purpose of query recommendation. Our approach is based on an existing domain-specific taxonomy. We define innovative three-layered semantic similarity measures between queries using existing similarity measures between ontology concepts combined with various set-based distance measures. We then analyse and evaluate our approach against human intuition using a data set of 90 questions. Further on, we argue that these measures are taxonomy-dependent and are influenced by various factors: taxonomy structure, keyword mappings, keyword weights, query-keyword mappings and the chosen concept
similarity measure.
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