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2018 ; 9
(1
): 11
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Matching biomedical ontologies based on formal concept analysis
#MMPMID29554977
Zhao M
; Zhang S
; Li W
; Chen G
J Biomed Semantics
2018[Mar]; 9
(1
): 11
PMID29554977
show ga
BACKGROUND: The goal of ontology matching is to identify correspondences between
entities from different yet overlapping ontologies so as to facilitate semantic
integration, reuse and interoperability. As a well developed mathematical model
for analyzing individuals and structuring concepts, Formal Concept Analysis (FCA)
has been applied to ontology matching (OM) tasks since the beginning of OM
research, whereas ontological knowledge exploited in FCA-based methods is
limited. This motivates the study in this paper, i.e., to empower FCA with as
much as ontological knowledge as possible for identifying mappings across
ontologies. METHODS: We propose a method based on Formal Concept Analysis to
identify and validate mappings across ontologies, including one-to-one mappings,
complex mappings and correspondences between object properties. Our method,
called FCA-Map, incrementally generates a total of five types of formal contexts
and extracts mappings from the lattices derived. First, the token-based formal
context describes how class names, labels and synonyms share lexical tokens,
leading to lexical mappings (anchors) across ontologies. Second, the
relation-based formal context describes how classes are in taxonomic, partonomic
and disjoint relationships with the anchors, leading to positive and negative
structural evidence for validating the lexical matching. Third, the positive
relation-based context can be used to discover structural mappings. Afterwards,
the property-based formal context describes how object properties are used in
axioms to connect anchor classes across ontologies, leading to property mappings.
Last, the restriction-based formal context describes co-occurrence of classes
across ontologies in anonymous ancestors of anchors, from which extended
structural mappings and complex mappings can be identified. RESULTS: Evaluation
on the Anatomy, the Large Biomedical Ontologies, and the Disease and Phenotype
track of the 2016 Ontology Alignment Evaluation Initiative campaign demonstrates
the effectiveness of FCA-Map and its competitiveness with the top-ranked systems.
FCA-Map can achieve a better balance between precision and recall for large-scale
domain ontologies through constructing multiple FCA structures, whereas it
performs unsatisfactorily for smaller-sized ontologies with less lexical and
semantic expressions. CONCLUSIONS: Compared with other FCA-based OM systems, the
study in this paper is more comprehensive as an attempt to push the envelope of
the Formal Concept Analysis formalism in ontology matching tasks. Five types of
formal contexts are constructed incrementally, and their derived concept lattices
are used to cluster the commonalities among classes at lexical and structural
level, respectively. Experiments on large, real-world domain ontologies show
promising results and reveal the power of FCA.
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