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Open AccessBook ChapterDOI

DBpedia: a nucleus for a web of open data

TLDR
The extraction of the DBpedia datasets is described, and how the resulting information is published on the Web for human-andmachine-consumption and how DBpedia could serve as a nucleus for an emerging Web of open data.
Abstract
DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against datasets derived from Wikipedia and to link other datasets on the Web to Wikipedia data. We describe the extraction of the DBpedia datasets, and how the resulting information is published on the Web for human-andmachine-consumption. We describe some emerging applications from the DBpedia community and show how website authors can facilitate DBpedia content within their sites. Finally, we present the current status of interlinking DBpedia with other open datasets on the Web and outline how DBpedia could serve as a nucleus for an emerging Web of open data.

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Journal ArticleDOI

Linked Data - the story so far

TL;DR: The authors describe progress to date in publishing Linked Data on the Web, review applications that have been developed to exploit the Web of Data, and map out a research agenda for the Linked data community as it moves forward.
Journal ArticleDOI

DBpedia - A Large-scale, Multilingual Knowledge Base Extracted from Wikipedia

TL;DR: An overview of the DBpedia community project is given, including its architecture, technical implementation, maintenance, internationalisation, usage statistics and applications, including DBpedia one of the central interlinking hubs in the Linked Open Data (LOD) cloud.
Journal ArticleDOI

DBpedia - A crystallization point for the Web of Data

TL;DR: The extraction of the DBpedia knowledge base is described, the current status of interlinking DBpedia with other data sources on the Web is discussed, and an overview of applications that facilitate the Web of Data around DBpedia is given.
Proceedings Article

Embedding Entities and Relations for Learning and Inference in Knowledge Bases

TL;DR: It is found that embeddings learned from the bilinear objective are particularly good at capturing relational semantics and that the composition of relations is characterized by matrix multiplication.
Proceedings ArticleDOI

Knowledge vault: a web-scale approach to probabilistic knowledge fusion

TL;DR: The Knowledge Vault is a Web-scale probabilistic knowledge base that combines extractions from Web content (obtained via analysis of text, tabular data, page structure, and human annotations) with prior knowledge derived from existing knowledge repositories that computes calibrated probabilities of fact correctness.
References
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Proceedings ArticleDOI

Yago: a core of semantic knowledge

TL;DR: YAGO as discussed by the authors is a light-weight and extensible ontology with high coverage and quality, which includes the Is-A hierarchy as well as non-taxonomic relations between entities (such as HASONEPRIZE).
Book ChapterDOI

Why and Where: A Characterization of Data Provenance

TL;DR: An approach to computing provenance when the data of interest has been created by a database query is described, adopting a syntactic approach and present results for a general data model that applies to relational databases as well as to hierarchical data such as XML.
Proceedings ArticleDOI

Semantic Wikipedia

TL;DR: This paper provides an extension to be integrated in Wikipedia, that allows the typing of links between articles and the specification of typed data inside the articles in an easy-to-use manner, and presents the design, implementation, and possible uses of this extension.
Proceedings ArticleDOI

ULDBs: databases with uncertainty and lineage

TL;DR: It is shown that the ULDB representation is complete, and that it permits straightforward implementation of many relational operations, and how ULDBs enable a new approach to query processing in probabilistic databases.

Tabulator: Exploring and Analyzing linked data on the Semantic Web

TL;DR: The Tabulator project is an attempt to demonstrate and utilize the power of linked RDF data with a user-friendly Semantic Web browser that is able to recognize and follow RDF links to other RDF resources based on the user’s exploration and analysis.
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