DBpedia – A large-scale, multilingual knowledge base extracted from Wikipedia

Lehmann, Jens; Isele, Robert; Jakob, Max; Jentzsch, Anja; Kontokostas, Dimitris; Mendes, Pablo N.; Hellmann, Sebastian; Morsey, Mohamed; Kleef, Patrick van; Auer, Sören; Bizer, Christian

doi:10.3233/sw-140134

Cited by 2,418 publications

(1,310 citation statements)

References 40 publications

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“…The proposed quality assessment tool is being used in many use cases. These includes the projects QROWD, SLIPO, and an industrial application by Alethio 15 .…”

Section: Use Casesmentioning

confidence: 99%

A Scalable Framework for Quality Assessment of RDF Datasets

Sejdiu

Rula

Lehmann

et al. 2019

Lecture Notes in Computer Science

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Over the last years, Linked Data has grown continuously. Today, we count more than 10,000 datasets being available online following Linked Data standards. These standards allow data to be machine readable and inter-operable. Nevertheless, many applications, such as data integration, search, and interlinking, cannot take full advantage of Linked Data if it is of low quality. There exist a few approaches for the quality assessment of Linked Data, but their performance degrades with the increase in data size and quickly grows beyond the capabilities of a single machine. In this paper, we present DistQualityAssessment -an open source implementation of quality assessment of large RDF datasets that can scale out to a cluster of machines. This is the first distributed, in-memory approach for computing different quality metrics for large RDF datasets using Apache Spark. We also provide a quality assessment pattern that can be used to generate new scalable metrics that can be applied to big data. The work presented here is integrated with the SANSA framework and has been applied to at least three use cases beyond the SANSA community. The results show that our approach is more generic, efficient, and scalable as compared to previously proposed approaches.

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“…The proposed quality assessment tool is being used in many use cases. These includes the projects QROWD, SLIPO, and an industrial application by Alethio 15 .…”

Section: Use Casesmentioning

confidence: 99%

A Scalable Framework for Quality Assessment of RDF Datasets

Sejdiu

Rula

Lehmann

et al. 2019

Lecture Notes in Computer Science

Self Cite

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“…Linked Data is a method in which structured data are interlinked using RDF on the Web and become more useful through semantic queries (Auer et al 2011;Bizer 2009;Heath and Bizer 2011;Lehmann et al 2015). Tim Berners-Lee defined fundamentals that can effectively publish and connect structured data on the Web if it follows the linked data principles.…”

Section: Linked Open Data-dbpediamentioning

confidence: 99%

“…However, linked data applications that utilize data-level links can find new data sources at runtime. Linked data applications can produce comparatively complete and updated answers because they consult with new data sources that appear on the Web (Lehmann et al 2015). LOD are linked data released under an open license and allow the reuse of data free of cost.…”

Section: Linked Open Data-dbpediamentioning

confidence: 99%

Social Semantic Web Framework for Industrial Synergies Initiation

Ghali

Frayret

2018

J of Industrial Ecology

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Summary Industrial synergies join two or more organizations that initially functioned as independent economic actors—that may originate from different sectors—together in order to share resources and exchange by‐products for mutual environmental, financial, and social benefits for its participants. Industrial symbioses (ISs) are networks of industrial synergies that can be initiated and created over time in various manners. In practice, the initiation of an industrial synergy, and particularly the identification of by‐product compatibilities, relies on direct or facilitated knowledge and information sharing, which is essential for discovering industrial synergy opportunities. Beyond its potential contribution to facilitate knowledge and information sharing among organizations, the Social Semantic Web (SSW) also has the potential to facilitate the initiation of industrial synergy by systematically and automatically identifying and recommending by‐products exchange compatibilities to potential partners. This framework exploits the ability of the sematic web to enable the search for analogies between potential partners within a region or district and existing industrial synergies around the world. This paper proposes the Social Semantic Web for Industrial Synergies Initiation (SSWISI) framework for the initiation of industrial synergies, which is based on the Social Semantic Web. The framework proposed in this paper adopts the concept of Linked Open Data (LOD), which enables the sharing and exchanging of information with external systems. This feature distinguishes the proposed framework from the existing approaches in its initiation of industrial synergies.

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“…To illustrate, consider an application of Compage targeting DBPedia (Lehmann et al, 2015), a Linked Data publication of structured Wikipedia information. A researcher interested in studying Benedictine writers might incorporate our protagonist the Venerable Bede into a result setby typing his name into a rudimentary search interface, or by loading a pre-specified workset of Benedictine scholars.…”

Section: Realising Applied Prioritisation As a Space For Serendipitymentioning

confidence: 99%

“…Our simulation targets DBPedia (Lehmann et al, 2015). This Linked Data publication of structured Wikipedia information exhibits comprehensive coverage, a heterogeneous nature, and a high degree of interconnectedness.…”

Section: Dataset and Seed Worketsmentioning

confidence: 99%

Serendipity in context: Prioritised contextual browsing in large‐scale digital libraries

Weigl

Emsley

Page

2018

Proc. Assoc. Info. Sci. Tech.

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In previous work we proposed a model of information‐seeking behaviour in scholarly workset creation, combining and extending established models by Bates, Ellis, and Wilson to encompass strategies for scholarly research in large‐scale information systems. However, this model simplifies contextual browsing, a key aspect of information seeking in large‐scale information systems, as a single, holistic, strategy. Here, we extend this model with granular strategies for contextual browsing, defining new modes to characterise contextual browsing as combinations of these strategies, which we show to be consistent with serendipitous discovery as described by Makri et al. We study the properties of prioritised contextual browsing as a mechanism for implementing these strategies. We describe the Compage framework, a proof‐of‐concept implementation for prioritised contextual browsing of Linked Data resources, using Jaccard similarity for prioritisation. Extending Compage, we develop a simulation environment in which we investigate the utility of prioritised contextual browsing over a large‐scale digital library dataset. Our simulation applies three strategies for the traversal of contextual metadata: reset, unprioritised, and prioritised. Results empirically demonstrate the advantages of prioritised contextual browsing, and that elements of serendipity can be identified and incorporated within our information‐seeking model. In doing so, we evaluate our model's suitability for this scenario, yielding a more detailed understanding of the strategies and modes of behaviour underlying contextual browsing.

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DBpedia – A large-scale, multilingual knowledge base extracted from Wikipedia

Cited by 2,418 publications

References 40 publications

A Scalable Framework for Quality Assessment of RDF Datasets

A Scalable Framework for Quality Assessment of RDF Datasets

Social Semantic Web Framework for Industrial Synergies Initiation

Serendipity in context: Prioritised contextual browsing in large‐scale digital libraries

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