Ranking documents in thesaurus-based boolean retrieval systems
Information Processing and Management: an International Journal
Measuring Similarity between Ontologies
EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
The PROMPT suite: interactive tools for ontology merging and mapping
International Journal of Human-Computer Studies
Ontology mapping: the state of the art
The Knowledge Engineering Review
Comparative and Functional Genomics
DILS '08 Proceedings of the 5th international workshop on Data Integration in the Life Sciences
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Instance-based matching of large life science ontologies
DILS'07 Proceedings of the 4th international conference on Data integration in the life sciences
Using annotations from controlled vocabularies to find meaningful associations
DILS'07 Proceedings of the 4th international conference on Data integration in the life sciences
Graph-based concept identification and disambiguation for enterprise search
Proceedings of the 19th international conference on World wide web
An empirical study of instance-based ontology matching
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Ontology and instance matching
Knowledge-driven multimedia information extraction and ontology evolution
Alignment of biomedical ontologies using life science literature
KDLL'06 Proceedings of the 2006 international conference on Knowledge Discovery in Life Science Literature
Dense subgraphs with restrictions and applications to gene annotation graphs
RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
Modeling ontology evolution with SetPi
Information Sciences: an International Journal
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Entries in biomolecular databases are often annotated with concepts from different ontologies and thereby establish links between pairs of concepts. Such links may reveal meaningful relationships between linked concepts, however they could as well relate concepts by chance. In this work we present InterOnto, a methodology that allows us to rank concept pairs to identify the most meaningful associations. The novelty of our approach compared to previous works is that we take the entire structure of the involved ontologies into account. This way, our method even finds links that are not present in the annotated data, but may be inferred through subsumed concept pairs. We have evaluated our methodology both quantitatively and qualitatively. Using real-life data from TAIR we show that our proposed scoring function is able to identify the most representative concept pairs while preventing overgeneralization. In comparison to prior work our method generally yields rankings of equivalent or better quality.