Evaluation of Instance Matching Tools: The Experience of OAEI

Ferrara, Alfio; Nikolov, Andriy; Noessner, Jan; Scharffe, François

doi:10.2139/ssrn.3199068

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…There is not much support for n:m alignments; otherwise, systems mostly focus on 1:1 ones. Evaluations in [29,30] show that regarding matching geospatial datasets, such as DBpediaand Geonames, the existing tools are efficient for simple geospatial representations, such as (latitude, longitude), while failing with more complex ones.…”

Section: Automatic Schema Matchingmentioning

confidence: 99%

An Automatic Matcher and Linker for Transportation Datasets

Masri

Zeitouni

Kedad

et al. 2017

Preprint

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Multimodality requires the integration of heterogeneous transportation data to construct a broad view of the transportation network. Many new transportation services are emerging while being isolated from previously-existing networks. This leads them to publish their data sources to the web, according to linked data principles, in order to gain visibility. Our interest is to use these data to construct an extended transportation network that links these new services to existing ones. The main problems we tackle in this article fall in the categories of automatic schema matching and data interlinking. We propose an approach that uses web services as mediators to help in automatically detecting geospatial properties and mapping them between two different schemas. On the other hand, we propose a new interlinking approach that enables the user to define rich semantic links between datasets in a flexible and customizable way.

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Section: Automatic Schema Matchingmentioning

confidence: 99%

An Automatic Matcher and Linker for Transportation Datasets

Masri

Zeitouni

Kedad

et al. 2017

Preprint

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“…After discussing a number of linking systems in the mentioned study, the authors focused on one of the tools to employ it in the proposed framework. In the context of the Ontology Alignment Evaluation Initiative, Ferrara et al [18] evaluated several instance matching systems and reported their experimental results on a real-world benchmark task over several LOD datasets. In particular, the presented approach combined real-data and automatically generated data to provide a framework that would produce different causes of data heterogeneity.…”

Section: Background and Related Workmentioning

confidence: 99%

An empirical study on the evaluation of interlinking tools on the Web of Data

Rajabi

Sicilia

Sánchez‐Alonso

2014

Journal of Information Science

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The rise and widespread use of Linked Data has encouraged data providers to publish and link their content in order to classify and organize information in a useful fashion. Interlinking between datasets enhances data navigation and facilitates searching. As a result, the use of interlinking tools as a way of connecting data items to the Linked Open Data cloud has become more prevalent. In this paper, we examine the results obtained by three interlinking tools used to link a large educational collection to the Linked Open Data datasets. The generated output by the interlinking tools, which was later assessed by human experts, illustrates that data publishers can rely on current interlinking approaches and thus adopt them to connect their resources to the Web of Data. Our findings also provide evidence that two of these tools, namely Silk and LIMES, can be considered as the most promising.

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“…Many instance matching approaches have been proposed. However, most of them cannot deal with large-scale knowledge bases nicely because they require traversing all instance pairs between two knowledge bases [1][2][3] . Some other approaches, such as CODI [4] , Silk [5] , PARIS [6] , and SIGMa [7] , are proposed for large-scale instance matching.…”

Section: Introductionmentioning

confidence: 99%

RiMOM-IM: A Novel Iterative Framework for Instance Matching

Shao

et al. 2016

J. Comput. Sci. Technol.

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Instance matching, which aims at discovering the correspondences of instances between knowledge bases, is a fundamental issue for the ontological data sharing and integration in Semantic Web. Although considerable instance matching approaches have already been proposed, how to ensure both high accuracy and efficiency is still a big challenge when dealing with large-scale knowledge bases. This paper proposes an iterative framework, RiMOM-IM (RiMOM-Instance Matching). The key idea behind this framework is to fully utilize the distinctive and available matching information to improve the efficiency and control the error propagation. We participated in the 2013 and 2014 competition of Ontology Alignment Evaluation Initiative (OAEI), and our system was ranked the first. Furthermore, the experiments on previous OAEI datasets also show that our system performs the best.

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Evaluation of Instance Matching Tools: The Experience of OAEI

Cited by 3 publications

References 24 publications

An Automatic Matcher and Linker for Transportation Datasets

An Automatic Matcher and Linker for Transportation Datasets

An empirical study on the evaluation of interlinking tools on the Web of Data

RiMOM-IM: A Novel Iterative Framework for Instance Matching

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