The AgreementMakerLight Ontology Matching System

Faria, Daniel; Pesquita, Cátia; Santos, Emanuel; Palmonari, Matteo; Cruz, Isabel F.; Couto, Francisco M.

doi:10.1007/978-3-642-41030-7_38

Cited by 180 publications

(164 citation statements)

References 7 publications

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“…Faria et al [21] have developed ontology matching system named as Agreement Maker. AML (Agreement Maker Light) is upgraded version of Agreement Maker and is an automated ontology matching system which is extensible and efficient.…”

Section: Ontology Matchingmentioning

confidence: 99%

“…OAEI give various tasks related to ontology matching to participant system after that results are evaluated using evaluation measures such as recall, precision, and F-measure. Out of available standard techniques and tools, Agreement Marker Light (AML) tool [21] can be adjusted [22] to find global similarities of two ontologies and thus can congregate ontologies together. OAEI also provide ontologies for experimentation and evaluation of the system.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

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%

“…The dataset we use is selected from OAEI benchmark 7 . and it is created from a bibliographic ontology which we call source ontology.…”

Section: A Dataset Preparationmentioning

confidence: 99%

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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“…The methods that have been proposed to deal with cross-lingual ontology matching most commonly rely on automatic translation of labels to a single target language [5]. However, machine translation tolerates low precision levels and there is often a lack of exact one-to-one correspondence between the terms in different natural languages.…”

Section: Introductionmentioning

confidence: 99%

Light-Weight Cross-Lingual Ontology Matching with LYAM++

Tigrine

Bellahsène

Todorov

2015

Lecture Notes in Computer Science

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Abstract. During the last decade, several automatic ontology matching systems were developed to address the problem of ontology heterogeneity. Aligning cross-lingual ontologies is among the current challenging issues in the field of ontology matching. The majority of the existing approaches rely on machine translation to deal with this problem. However, inherent problems of machine translation are imprecision and ambiguity. In this paper, we propose a novel approach to the cross-lingual ontology matching task, relying on the large multilingual semantic network BabelNet as a source of background knowledge to assist the matching process. We have designed and tested a novel orchestration of the components of the matching workflow. Our approach is implemented under the form of a prototype named LYAM++ (Yet Another Matcher-Light)-a fully automatic cross-lingual ontology matching system that does not rely on machine translation. We report the results of our experiments that show that LYAM++ outperforms considerably the best techniques in the state-of-the-art according to the obtained results on the MultiFarm datasets of the Ontology Alignment Evaluation Initiative 2014.

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The AgreementMakerLight Ontology Matching System

Cited by 180 publications

References 7 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

Light-Weight Cross-Lingual Ontology Matching with LYAM++

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