Matching biomedical ontologies based on formal concept analysis

Abstract

BackgroundThe 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.MethodsWe 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.ResultsEvaluation 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.ConclusionsCompared 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.

Biomedical ontology alignment: an approach based on representation learning

“…CroMatcher [ 64 ], AML [ 60 , 61 ] and XMap [ 65 ] perform ontology matching based on heuristic methods that rely on aggregation functions. FCA_Map [ 66 , 67 ] uses Formal Concept Analysis [ 68 ] to derive terminological hierarchical structures that are represented as lattices. The matching is performed by aligning the constructed lattices taking into account the lexical and structural information that they incorporate.…”

“…Moreover, LogMap, AML and XMap exploit complete and incomplete reasoning techniques so as to repair incoherent mappings [ 78 ]. Unlike the aforementioned approaches, FCA_Map [ 66 , 67 ] uses Formal Concept Analysis [ 68 ] to derive terminological hierarchical structures that are represented as lattices. The matching is performed by aligning the constructed lattices taking into account the lexical and structural information that they incorporate.…”

An Efficient Algorithm for Decreasing the Granularity Levels of Attributes in Formal Concept Analysis

“…First, the procedure Generate creates a new concept and adds the new concept to concept lattice (lines 1-2). According to Proposition 9, we test every candidate in c.Children to find real children of newConcept (lines [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Note that the concept c.children.indicator points to has already been obtained after executing the Preprocessprocedure.…”

“…Each concept of a formal lattice consists of an extent and an intent, which are closely connected by the conceptual relationship. FCA focuses on obtaining different forms of outputs from original formal context for interesting information, and it has a wide range of applications in various fields including information retrieval [4]- [6], data mining [7], social network analysis [8]- [10], gene expression [11]- [13] machine learning [14], [15], ontologies [16], [17], graph mining [18]- [20]. A comprehensive survey about research trends and applications on formal concept analysis is given by Singh et al [21].…”

Developing a Product Knowledge Graph of Consumer Electronics to Manage Sustainable Product Information

“…FCA is a mathematical data analysis technique based on lattice and order theory [8], which extracts concepts from binomial relationships between objects and attributes, and constructs hierarchical structures of levels of relationships between concepts. FCA has been applied to various fields [9,10], and is especially used in object-oriented domain modelling [11] and ontology construction with hierarchical concept structures [12,13]. Based on such prior research, this study extracts common vocabularies from a list of product specifications using FCA techniques and elaborates a set of derived formal concepts to design the proposed knowledge model.…”