Ontology Matching

By Jérôme Euzenat, Pavel Shvaiko. 2007

2,080 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Knowledge Graphs & Representation

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Citations in the corpus

SIGMa: simple greedy matching for aligning large knowledge bases (2012, background): “A significant amount of research has been done in this area – particularly under the umbrella term of ontology matching [7, 16, 12].”
Scaling Semantic Parsers with On-the-Fly Ontology Matching (2013, methodology): “This section describes, with reference to the example in Figure 3, the operations used by Π and M .”
Information Extraction over Structured Data: Question Answering with Freebase (2014, methodology): “The data challenge is more formally framed as ontology or (textual) schema matching (Hobbs, 1985; Rahm and Bernstein, 2001; Euzenat and Shvaiko, 2007): matching structure of two ontologies/databases or (in extension) mapping between KB relations and NL text.”
Ontological Smoothing for Relation Extraction with Minimal Supervision (2012, background): “Mapping between ontologies Euzenat & Shvaiko (Euzenat and Shvaiko 2007) and Rahm & Bernstein (Rahm and Bernstein 2001) carve the set of approaches for ontology matching into several dimensions.”
Semantic Web in data mining and knowledge discovery: A comprehensive survey (2016, background): “The problem of aligning properties, as well as instances and classes, in ontologies is addressed by ontology matching techniques [132].”
Results of the Ontology Alignment Evaluation Initiative 2014 (2014, background): “Although the standard UMLS distribution does not directly provide alignments (in the sense of [15]) between the integrated ontologies, it is relatively straightforward to extract them from the information provided in the distribution ﬁles (see [18] for details).”
Generic schema matching, ten years later (2011, background): “Most of the listed techniques have been implemented in a large number of tools for schema and ontology matching [26][62].”
Unsupervised generation of data mining features from linked open data (2012, background): “While ontology learning is always concerned with creating or improving a single ontology, ontology matching aims at creating relations between multiple ontologies [2, 12, 13].”
Profiling relational data: a survey (2015, background): “Schematic fit is the degree to which two schemata complement and overlap each other and can be determined using schema matching techniques [44].”
Ontology Alignment Evaluation Initiative: Six Years of Experience (2011, background): “Different approaches to the problem of ontology matching have emerged from the literature [27].”
Ontology-based semantic similarity: A new feature-based approach (2012, background): “The function dis norm fulfills the properties of dissimilarity measures (Euzenat & Shvaiko, 2007): 0 ) , ( , , ≥ Ο ∈ ∀ b a dis b a (positiveness) 0 ) , ( , = Ο ∈ ∀ a a dis a (minimality) ) , ( ) , ( , , a b dis b a dis b a = Ο ∈ ∀ (symmetry) Proof.”
Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL (2011, neutral): “[Euzenat & Shvaiko 2007] J. Euzenat and P. Shvaiko.”
Ontology Matching: State of the Art and Future Challenges (2013, background): “For what concerns thematic part, e.g., hydrography , standard matching technology can be widely reused [2, 32–34], while the spatial and temporal counterparts that constitute the speciﬁcity of GI applications have not received enough attention so far in the ontology matching ﬁeld (with exceptions,…”
Towards a Definition of Knowledge Graphs (2016, background): “We explicitly emphasize that an ontology does not differ from a knowledge base, although ontologies are sometimes erroneously classified as being at the same level as database schemas [6].”
A self-training approach for resolving object coreference on the semantic web (2011, methodology): “Given a set of coreferent URIs, ontology matching techniques [6] could be adopted to discover matchable properties from their values (a so-called extensional way), which takes into account the matchability between properties from pragmatics.”
Ontology matching: A literature review (2015, background): “This classification is an evolution of another previously proposed by the same authors in Euzenat and Shvaiko (2007).”