LogMap: Logic-Based and Scalable Ontology Matching

Jiménez-Ruiz, Ernesto; Grau, Bernardo Cuenca

doi:10.1007/978-3-642-25073-6_18

Cited by 306 publications

(262 citation statements)

References 19 publications

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“…CroMatcher is not doing ontology merging and processing queries, its only focus on ontology matching system. Ruiz and Grau [35] have developed LogMap, which have used reasoning using logic based semantics for better alignments. The LogMap can be scaled, and it is an ontology system based on logic, which participates in OAEI since past seven years all tracks and giving top performance.…”

Section: Ontology Matchingmentioning

confidence: 99%

A Known in Advance, What Ontologies to Integrate? For Effective Ontology Merging Using K-means Clustering

Makwana¹,

Ganatra²

2018

IJIES

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A significant aspect of any system is how we present the knowledge. Ontology is one of the methods to present shared knowledge of the particular domain. Ontology can be designed and developed on specific domain or subdomain by many groups and researchers, which will create heterogeneity. To solve this problem ontology integration is necessary. To integrate shared knowledge, we require calculating the similarity between two ontologies, selected from a corpus using ontology matching techniques. Here we define a procedure to create a group of ontologies. Ontology comparison can be made using tools to find similar classes. As a similarity measure, Jaccard Similarity Index (JSI) is used create a group of ontology by an algorithm named k means. For each cluster, we generate buckets of ontologies, and from the bucket, all ontologies are grouped in one ontology reducing the attempt of exploring in enquiring understanding in multiple ontologies. Here we have check performance of ontology matching; clustering and merging algorithms script using standard benchmarking techniques of agriculture domain and conference track. Also, compare response time performance of SPARQL (SPARQL Protocol and RDF Query Language) query on merged ontology using this method. For experimentation, we have selected ontology corpus from agriculture domain and conference tack domain of OAEI (Ontology Alignment Evaluation Initiative). At the end of experimentation through our proposed approach we could achieve improvement in average loading and match time in ontology matching process compare to an existing tool. Also, we could achieve significance result in ontology merging process though benchmark parameter of coverage, compactness, and average merge time of two ontologies. Finally, in SPARQL query experimentation, we got success in improvement in reducing search space and response time of the query.

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Section: Ontology Matchingmentioning

confidence: 99%

A Known in Advance, What Ontologies to Integrate? For Effective Ontology Merging Using K-means Clustering

Makwana¹,

Ganatra²

2018

IJIES

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“…Nowdays, state of the art ontology matching systems also handle terminological heterogeneity well by applying various string-based matchers including TF-IDF, Levenstein distance, Jaccard similarity, Wordnet-based matching, etc [6]. In last five years, advanced ontology matching systems, such as AML [7], Logmap [8], DKP-AOM [9], etc. have designed terminological methods and achieve a very high accuracy in ontology matching.…”

Section: How State Of the Art Systems Deal With These Challenges?mentioning

confidence: 99%

“…An example of error propagation in ontology matching by using similarity flooding. In this case, the calculation reach the fixpoint after eight iterations, marked as S 8 The authors of LILY [12] also report such problem.They try to use more strict constrains and thresholds to prevent errors from one mismatched pair to propagate similarity to their neighbors. However, such constrains and thresholds are hard to set since different evironments or different ontologies would require different constrains and thresholds.…”

Section: Preliminariesmentioning

confidence: 99%

“…and it is created from a bibliographic ontology which we call source ontology. From the source ontology, 106 corresponding ontologies are built by applying different tranformation rules 8 such as random string, synonyms name, different conventions, other languages, expansed hierarchy, suppressed properties, etc. To match the transformed ontology with the reference one we need to match 106 tasks which have non-sequential number from 101 to 266, and evaluate the results on the corresponding reference.…”

Section: A Dataset Preparationmentioning

confidence: 99%

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Leveraging Structural Information in Ontology Matching

Xie

Chekol

Spahiu

et al. 2016

2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA)

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Abstract-Ontology matching is an important part of enabling the semantic web to reach its full potential. Most existing ontology matching methods are mainly based on linguistic information (label, name, title and comment) but from the results achieved it is realized that this information is not sufficient. The latest ontology matching research works are trying to deeply dig into the structural information of ontologies by using "similarityflooding" method. However, there are several innate issues in similarity-flooding methods that lead to wrong matching results. In this paper, we report the problems of similarity-flooding in ontology matching and propose a novel method to effectively leverage the structural information of the ontology. The evaluation is conducted on OAEI ontology matching benchmarks from 2011 to 2015. The result shows that the proposed approach performs comparatively well with other state of the art matching systems.

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“…In our work, we use the alignment tool LogMap 1 [6] since that system obtained good results during the evaluation OAEI 2014 [4] and, moreover, it allows to map individuals as well as classes.…”

Section: Overview Of Our Fusion Processmentioning

confidence: 99%

Dealing with Incompatibilities During a Knowledge Bases Fusion Process

Amarger

Chanet²,

Haemmerlé

et al. 2016

Graph-Based Representation and Reasoning

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. Abstract. More and more data sets are published on the linked open data. Reusing these data is a challenging task as for a given domain, several data sets built for specific usage may exist. In this article we present an approach for existing knowledge bases fusion by taking into account incompatibilities that may appear in their representations. Equivalence mappings established by an alignment tool are considered in order to generate a subset of compatible candidates. The approach has been evaluated by domain experts on datasets dealing with agriculture.

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LogMap: Logic-Based and Scalable Ontology Matching

Cited by 306 publications

References 19 publications

A Known in Advance, What Ontologies to Integrate? For Effective Ontology Merging Using K-means Clustering

A Known in Advance, What Ontologies to Integrate? For Effective Ontology Merging Using K-means Clustering

Leveraging Structural Information in Ontology Matching

Dealing with Incompatibilities During a Knowledge Bases Fusion Process

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