Similarity-Based Information Retrieval and Its Role within Spatial Data Infrastructures
GIScience '08 Proceedings of the 5th international conference on Geographic Information Science
Ontology-Based Relevance Assessment: An Evaluation of Different Semantic Similarity Measures
OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part II on On the Move to Meaningful Internet Systems
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
Similarity as a Quality Indicator in Ontology Engineering
Proceedings of the 2008 conference on Formal Ontology in Information Systems: Proceedings of the Fifth International Conference (FOIS 2008)
Semantic rules for context-aware geographical information retrieval
EuroSSC'09 Proceedings of the 4th European conference on Smart sensing and context
Semantic referencing - determining context weights for similarity measurement
GIScience'10 Proceedings of the 6th international conference on Geographic information science
Context-aware pervasive service composition and its implementation
Personal and Ubiquitous Computing
Building a global normalized ontology for integrating geographic data sources
Computers & Geosciences
A hybrid knowledge-based and data-driven approach to identifying semantically similar concepts
Journal of Biomedical Informatics
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Semantic similarity measurement gained attention over the last years as a non-standard inference service for various kinds of knowledge representations including description logics. Most existing similarity measures compute an undirected overall similarity, i.e., they do not take the context of the similarity query into account. If they do, the notion of context is usually reduced to the selection of particular concepts for comparison (instead of comparing all concepts within an examined ontology). The importance of context in deriving meaningful similarity judgments is beyond question and has been examined within recent research. This paper argues that there are several kinds of contexts. Each of them has its own impact on the resulting similarity values, but also on their interpretation. To support this view, the paper introduces definitions for the examined contexts and illustrates their influence by example.