YAGO2: A spatially and temporally enhanced knowledge base from Wikipedia

Hoffart, Johannes; Suchanek, Fabian M.; Berberich, Klaus; Weikum, Gerhard

doi:10.1016/j.artint.2012.06.001

Cited by 1,027 publications

(565 citation statements)

References 50 publications

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“…Knowledge graphs such as DBpedia [2], Yago [16] and WikiData [25] as well as similar industrial initiatives by Google, Bing, IBM, BBC, or Thomson Reuters have demonstrated that representing encyclopedic and factual knowledge using RDF and Linked Data is feasible.…”

Section: Related Workmentioning

confidence: 99%

Towards a Knowledge Graph for Science

Auer

Kovtun

Prinz

et al. 2018

Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics

119

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The document-centric workflows in science have reached (or already exceeded) the limits of adequacy. This is emphasized by recent discussions on the increasing proliferation of scientific literature and the reproducibility crisis. This presents an opportunity to rethink the dominant paradigm of document-centric scholarly information communication and transform it into knowledgebased information flows by representing and expressing information through semantically rich, interlinked knowledge graphs. At the core of knowledge-based information flows is the creation and evolution of information models that establish a common understanding of information communicated between stakeholders as well as the integration of these technologies into the infrastructure and processes of search and information exchange in the research library of the future. By integrating these models into existing and new research infrastructure services, the information structures that are currently still implicit and deeply hidden in documents can be made explicit and directly usable. This has the potential to revolutionize scientific work as information and research results can be seamlessly interlinked with each other and better matched to complex information needs. Furthermore, research results become directly comparable and easier to reuse. As our main contribution, we propose the vision of a knowledge graph for science, present a possible infrastructure for such a knowledge graph as well as our early attempts towards an implementation of the infrastructure. * This invited article accompanies Sören Auer's WIMS2018 keynote.

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Section: Related Workmentioning

confidence: 99%

Towards a Knowledge Graph for Science

Auer

Kovtun

Prinz

et al. 2018

Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics

119

View full text Add to dashboard Cite

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“…Both information on Wikipedia pages and links between the pages are often used [48]. An open source tool kit for accessing and using Wikipedia is available [29] and many other common knowledge bases include information or links from Wikipedia, such as YAGO2 [18] and ConceptNet [44].…”

Section: Common Knowledge Basesmentioning

confidence: 99%

Knowledge based query expansion in complex multimedia event detection

Boer

Schutte

Kraaij

2015

Multimed Tools Appl

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A common approach in content based video information retrieval is to perform automatic shot annotation with semantic labels using pre-trained classifiers. The visual vocabulary of state-of-the-art automatic annotation systems is limited to a few thousand concepts, which creates a semantic gap between the semantic labels and the natural language query. One of the methods to bridge this semantic gap is to expand the original user query using knowledge bases. Both common knowledge bases such as Wikipedia and expert knowledge bases such as a manually created ontology can be used to bridge the semantic gap. Expert knowledge bases have highest performance, but are only available in closed domains. Only in closed domains all necessary information, including structure and disambiguation, can be made available in a knowledge base. Common knowledge bases are often used in open domain, because it covers a lot of general information. In this research, query expansion using common knowledge bases ConceptNet and Wikipedia is compared to an expert description of the topic applied to content-based information retrieval of complex events. We run experiments on the Test Set of TRECVID MED 2014. Results show that 1) Query Expansion can improve performance compared to using no query expansion in the case that the main noun of the query could not be matched to a concept detector; 2) Query expansion using expert knowledge is not necessarily better than query expansion using common knowledge; 3) ConceptNet performs slightly better than Wikipedia; 4) Late fusion can slightly improve performance. To conclude, query expansion has potential in complex event detection.

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“…Recent years have witnessed an impressive amount of work on the automatic construction of wide-coverage knowledge resources from Wikipedia [3,13] and the Web [7]. Complementary to this, a plethora of works in Natural Language Processing (NLP) has recently focused on combining knowledge bases with distributional information from text.…”

Section: Introductionmentioning

confidence: 99%

Linked Disambiguated Distributional Semantic Networks

Faralli

Panchenko

Biemann

et al. 2016

Lecture Notes in Computer Science

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Abstract. We present a new hybrid lexical knowledge base that combines the contextual information of distributional models with the conciseness and precision of manually constructed lexical networks. The computation of our countbased distributional model includes the induction of word senses for single-word and multi-word terms, the disambiguation of word similarity lists, taxonomic relations extracted by patterns and context clues for disambiguation in context. In contrast to dense vector representations, our resource is human readable and interpretable, and thus can be easily embedded within the Semantic Web ecosystem.

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YAGO2: A spatially and temporally enhanced knowledge base from Wikipedia

Cited by 1,027 publications

References 50 publications

Towards a Knowledge Graph for Science

Towards a Knowledge Graph for Science

Knowledge based query expansion in complex multimedia event detection

Linked Disambiguated Distributional Semantic Networks

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