Ontology Matching with Semantic Verification

Jean-Mary, Yves R.; Shironoshita, E. Patrick; Kabuka, Mansur R.

doi:10.2139/ssrn.3199431

Cited by 74 publications

(93 citation statements)

References 20 publications

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“…In the instance matching track of OAEI-2010 4 , the participants for IIMB2010 large dataset were Combinatorial Optimization for Data Integration (CODI) [20], Automated Semantic Mapping of Ontologies with Validation (ASMOV) [35] and RiMOM [19]. We experimented with our core instance matching system without property weight, we called as AFlood(PW-), and our augmented instance matching system with proposed automatic weight factor, we call as AFlood(PW+).…”

Section: Results Of Our Proposed System With Iimb-2010 Data Setmentioning

confidence: 99%

An Efficient Metric of Automatic Weight Generation for Properties in Instance Matching Technique

Seddiqui¹,

Nath²,

Aono³

2015

IJWesT

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The proliferation of heterogeneous data sources of semantic knowledge base intensifies the need of an automatic instance matching technique. However, the efficiency of instance matching is often influenced by the weight of a property associated to instances. Automatic weight generation is a non-trivial, however an important task in instance matching technique. Therefore, identifying an appropriate metric for generating weight for a property automatically is nevertheless a formidable task. In this paper, we investigate an approach of generating weights automatically by considering hypotheses: (1) the weight of a property is directly proportional to the ratio of the number of its distinct values to the number of instances contain the property, and (2) the weight is also proportional to the ratio of the number of distinct values of a property to the number of instances in a training dataset. The basic intuition behind the use of our approach is the classical theory of information content that infrequent words

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Section: Results Of Our Proposed System With Iimb-2010 Data Setmentioning

confidence: 99%

An Efficient Metric of Automatic Weight Generation for Properties in Instance Matching Technique

Seddiqui¹,

Nath²,

Aono³

2015

IJWesT

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“…5 systems participated to the evaluation: DSSim [24], Ri-MOM [25], OKKAM [15], HMatch [26], and ASMOV [27]. In this first instance matching track, 4 systems out of 5 represented generic ontology matching tools, which included instance matching as a part of their functionality, while only one (OKKAM) was specifically aimed at resolving data level coreferences.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of instance matching tools: The experience of OAEI

Ferrara

Nikolov

Noessner

et al. 2013

Journal of Web Semantics

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“…Many approaches were originally proposed as ontology matchers. Examples include RiMOM, LogMap, Asmov and ObjectCoref (Li, Tang, Li & Luo, 2009;Jiménez-Ruiz & Grau, 2011;Jean-Mary, Shironoshita & Kabuka, 2010;Hu, Qu & Sun, 2011). New systems continue to be proposed each year as part of the annual Ontology Alignment Evaluation Initiative 52 (Ferrara, Nikolov, Noessner & Scharffe, 2013).…”

Section: Other Systemsmentioning

confidence: 99%

Populating a linked data entity name system

Kejriwal¹

2017

AI Matters

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Resource Description Framework (RDF) is a graph-based data model used to publish data as a Web of Linked Data (Bizer et al . 2009). RDF is an emergent foundation for large-scale data integration , the problem of providing a unified view over multiple data sources. The structure in RDF data can be conveniently visualized using directed labeled graphs , as illustrated in the real-world graph fragments in Figure 1. Nodes in the graph represent entities (e.g. the node with label dbpedia:Allen_, Paul represents the entity Paul Allen in the DBpedia knowledge graph) and edges represent either attributes of an entity (e.g. '01/21/1953' is the birthdate of Paul Allen) or relationships between two entities (e.g. Paul Allen is the co-founder of the company entity, Microsoft). Facts in the knowledge base are formally represented as a set of triples , with a triple comprising a labeled edge (denoted as a property ) in the RDF graph along with its incoming and outgoing nodes.

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Ontology Matching with Semantic Verification

Cited by 74 publications

References 20 publications

An Efficient Metric of Automatic Weight Generation for Properties in Instance Matching Technique

An Efficient Metric of Automatic Weight Generation for Properties in Instance Matching Technique

Evaluation of instance matching tools: The experience of OAEI

Populating a linked data entity name system

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