Ontology Alignment for Linked Open Data

Jain, Prateek; Hitzler, Pascal; Sheth, Amit P.; Verma, Kunal; Yeh, Peter Z.

doi:10.1007/978-3-642-17746-0_26

Cited by 178 publications

(136 citation statements)

References 15 publications

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“…Despite the different studies in the theoretical background, we observe a marginal effort in evaluate these approaches in the Linked Data Cloud, which is the most concrete realization of the Semantic Web vision nowadays and they are a valuable resources for different application domain. In literature, the most recent works that use Linked Data in the simalar context of our paper but with different purposes are [4,12]. In particular the first evaluates the implications of owl:sameAs assertions in Linked Data data sets and the second uses the LOD to evaluate an ontology matching system.…”

Section: Related Workmentioning

confidence: 99%

Statistical Analysis of the owl:sameAs Network for Aligning Concepts in the Linking Open Data Cloud

Correndo

Penta

Gibbins

et al. 2012

Lecture Notes in Computer Science

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Abstract. The massively distributed publication of linked data has brought to the attention of scientific community the limitations of classic methods for achieving data integration and the opportunities of pushing the boundaries of the field by experimenting this collective enterprise that is the linking open data cloud. While reusing existing ontologies is the choice of preference, the exploitation of ontology alignments still is a required step for easing the burden of integrating heterogeneous data sets. Alignments, even between the most used vocabularies, is still poorly supported in systems nowadays whereas links between instances are the most widely used means for bridging the gap between different data sets. We provide in this paper an account of our statistical and qualitative analysis of the network of instance level equivalences in the Linking Open Data Cloud (i.e. the sameAs network) in order to automatically compute alignments at the conceptual level. Moreover, we explore the effect of ontological information when adopting classical Jaccard methods to the ontology alignment task. Automating such task will allow in fact to achieve a clearer conceptual description of the data at the cloud level, while improving the level of integration between datasets.

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Section: Related Workmentioning

confidence: 99%

Statistical Analysis of the owl:sameAs Network for Aligning Concepts in the Linking Open Data Cloud

Correndo

Penta

Gibbins

et al. 2012

Lecture Notes in Computer Science

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show abstract

“…Other systems like in [24] serve as proxy over existing schema matching tools, thereby providing means on combining the different functionalities of the individual platforms. BLOOMS [14], aligns classes in different KBs where the schema definitions are available. In our vision, the alignment of KBs should happen even in cases where explicit schema information or other database constraints are not available.…”

Section: Related Workmentioning

confidence: 99%

Online Relation Alignment for Linked Datasets

Koutraki¹,

Preda²,

Vodislav³

2017

The Semantic Web

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The large number of linked datasets in the Web, and their diversity in terms of schema representation has led to a fragmented dataset landscape. Querying and addressing information needs that span across disparate datasets requires the alignment of such schemas. Majority of schema and ontology alignment approaches focus exclusively on class alignment. Yet, relation alignment has not been fully addressed, and existing approaches fall short on addressing the dynamics of datasets and their size. In this work, we address the problem of relation alignment across disparate linked datasets. Our approach focuses on two main aspects. First, online relation alignment, where we do not require full access, and sample instead for a minimal subset of the data. Thus, we address the main limitation of existing work on dealing with the large scale of linked datasets, and in cases where the datasets provide only query access. Second, we learn supervised machine learning models for which we employ various features or matchers that account for the diversity of linked datasets at the instance level. We perform an experimental evaluation on real-world linked datasets, DBpedia, YAGO, and Freebase. The results show superior performance against state-of-the-art approaches in schema matching, with an average relation alignment accuracy of 84%. In addition, we show that relation alignment can be performed efficiently at scale.

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“…One approach is through the use of available schema: semantic information systems have used ontologies to represent domain-specific knowledge and enable users to select ontology terms in query assembly [13]. BLOOMS, for example, is a system for finding schema-level links between LOD datasets using the concept of ontology alignment [11], but it relies mainly on Wikipedia. Ontology alignment typically relies on starting with a single ontology, which is not available for most SPARQL endpoints in the LOD cloud and therefore could not be applied in our case.…”

Section: Related Workmentioning

confidence: 99%

A Roadmap for Navigating the Life Sciences Linked Open Data Cloud

Hasnain

Zainab

Kamdar

et al. 2015

Semantic Technology

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Abstract. Multiple datasets that add high value to biomedical research have been exposed on the web as a part of the Life Sciences Linked Open Data (LSLOD) Cloud. The ability to easily navigate through these datasets is crucial for personalized medicine and the improvement of drug discovery process. However, navigating these multiple datasets is not trivial as most of these are only available as isolated SPARQL endpoints with very little vocabulary reuse. The content that is indexed through these endpoints is scarce, making the indexed dataset opaque for users. In this paper, we propose an approach for the creation of an active Linked Life Sciences Data Roadmap, a set of configurable rules which can be used to discover links (roads) between biological entities (cities) in the LSLOD cloud. We have catalogued and linked concepts and properties from 137 public SPARQL endpoints. Our Roadmap is primarily used to dynamically assemble queries retrieving data from multiple SPARQL endpoints simultaneously. We also demonstrate its use in conjunction with other tools for selective SPARQL querying, semantic annotation of experimental datasets and the visualization of the LSLOD cloud. We have evaluated the performance of our approach in terms of the time taken and entity capture. Our approach, if generalized to encompass other domains, can be used for road-mapping the entire LOD cloud.

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Ontology Alignment for Linked Open Data

Cited by 178 publications

References 15 publications

Statistical Analysis of the owl:sameAs Network for Aligning Concepts in the Linking Open Data Cloud

Statistical Analysis of the owl:sameAs Network for Aligning Concepts in the Linking Open Data Cloud

Online Relation Alignment for Linked Datasets

A Roadmap for Navigating the Life Sciences Linked Open Data Cloud

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