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263
DBpedia: A Nucleus for a Web of Open Data
- PROC. 6TH INT’L SEMANTIC WEB CONF
, 2007
"... 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 extractio ..."
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Cited by 651 (37 self)
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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- and machine-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.
Learning to link with wikipedia
, 2008
"... This paper describes how to automatically cross-reference documents with Wikipedia: the largest knowledge base ever known. It explains how machine learning can be used to identify significant terms within unstructured text, and enrich it with links to the appropriate Wikipedia articles. The resultin ..."
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Cited by 322 (7 self)
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This paper describes how to automatically cross-reference documents with Wikipedia: the largest knowledge base ever known. It explains how machine learning can be used to identify significant terms within unstructured text, and enrich it with links to the appropriate Wikipedia articles. The resulting link detector and disambiguator performs very well, with recall and precision of almost 75%. This performance is constant whether the system is evaluated on Wikipedia articles or “real world ” documents. This work has implications far beyond enriching documents with explanatory links. It can provide structured knowledge about any unstructured fragment of text. Any task that is currently addressed with bags of words—indexing, clustering, retrieval, and summarization to name a few—could use the techniques described here to draw on a vast network of concepts and semantics.
YAGO: A Large Ontology from Wikipedia and WordNet
, 2008
"... This article presents YAGO, a large ontology with high coverage and precision. YAGO has been automatically derived from Wikipedia and WordNet. It comprises entities and relations, and currently contains more than 1.7 million entities and 15 million facts. These include the taxonomic Is-A hierarchy a ..."
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Cited by 148 (16 self)
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This article presents YAGO, a large ontology with high coverage and precision. YAGO has been automatically derived from Wikipedia and WordNet. It comprises entities and relations, and currently contains more than 1.7 million entities and 15 million facts. These include the taxonomic Is-A hierarchy as well as semantic relations between entities. The facts for YAGO have been extracted from the category system and the infoboxes of Wikipedia and have been combined with taxonomic relations from WordNet. Type checking techniques help us keep YAGO’s precision at 95% – as proven by an extensive evaluation study. YAGO is based on a clean logical model with a decidable consistency. Furthermore, it allows representing n-ary relations in a natural way while maintaining compatibility with RDFS. A powerful query model facilitates access to YAGO’s data.
Semantic MediaWiki
- Proc. 5th International Semantic Web Conference (ISWC06
, 2006
"... Abstract. Semantic MediaWiki is an extension of MediaWiki – a widely used wiki-engine that also powers Wikipedia. Its aim is to make semantic technologies available to a broad community by smoothly integrating them with the established usage of MediaWiki. The software is already used on a number of ..."
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Cited by 117 (4 self)
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Abstract. Semantic MediaWiki is an extension of MediaWiki – a widely used wiki-engine that also powers Wikipedia. Its aim is to make semantic technologies available to a broad community by smoothly integrating them with the established usage of MediaWiki. The software is already used on a number of productive installations world-wide, but the main target remains to establish “Semantic Wikipedia ” as an early adopter of semantic technologies on the web. Thus usability and scalability are as important as powerful semantic features. 1
Collective knowledge systems: Where the social web meets the semantic web
- Web Semantics: Science, Services and Agents on the World Wide Web
, 2008
"... Abstract: What can happen if we combine the best ideas from the Social Web and Semantic Web? The Social Web is an ecosystem of participation, where value is created by the aggregation of many individual user contributions. The Semantic Web is an ecosystem of data, where value is created by the integ ..."
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Cited by 111 (0 self)
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Abstract: What can happen if we combine the best ideas from the Social Web and Semantic Web? The Social Web is an ecosystem of participation, where value is created by the aggregation of many individual user contributions. The Semantic Web is an ecosystem of data, where value is created by the integration of structured data from many sources. What applications can best synthesize the strengths of these two approaches, to create a new level of value that is both rich with human participation and powered by well-structured information? This paper proposes a class of applications called collective knowledge systems, which unlock the "collective intelligence " of the Social Web with knowledge representation and reasoning techniques of the Semantic Web.
What have Innsbruck and Leipzig in common? Extracting Semantics from Wiki Content
- In Franconi et al. (eds), Proceedings of European Semantic Web Conference (ESWC 2007), LNCS 4519
, 2007
"... Abstract Wikis are established means for the collaborative authoring, versioning and publishing of textual articles. The Wikipedia project, for example, succeeded in creating the by far largest encyclopedia just on the basis of a wiki. Recently, several approaches have been proposed on how to extend ..."
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Cited by 106 (13 self)
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Abstract Wikis are established means for the collaborative authoring, versioning and publishing of textual articles. The Wikipedia project, for example, succeeded in creating the by far largest encyclopedia just on the basis of a wiki. Recently, several approaches have been proposed on how to extend wikis to allow the creation of structured and semantically enriched content. However, the means for creating semantically enriched structured content are already available and are, although unconsciously, even used by Wikipedia authors. In this article, we present a method for revealing this structured content by extracting information from template instances. We suggest ways to efficiently query the vast amount of extracted information (e.g. more than 8 million RDF statements for the English Wikipedia version alone), leading to astonishing query answering possibilities (such as for the title question). We analyze the quality of the extracted content, and propose strategies for quality improvements with just minor modifications of the wiki systems being currently used. 1
Mining meaning from Wikipedia
, 2009
"... Wikipedia is a goldmine of information; not just for its many readers, but also for the growing community of researchers who recognize it as a resource of exceptional scale and utility. It represents a vast investment of manual effort and judgment: a huge, constantly evolving tapestry of concepts an ..."
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Cited by 76 (2 self)
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Wikipedia is a goldmine of information; not just for its many readers, but also for the growing community of researchers who recognize it as a resource of exceptional scale and utility. It represents a vast investment of manual effort and judgment: a huge, constantly evolving tapestry of concepts and relations that is being applied to a host of tasks. This article provides a comprehensive description of this work. It focuses on research that extracts and makes use of the concepts, relations, facts and descriptions found in Wikipedia, and organizes the work into four broad categories: applying Wikipedia to natural language processing; using it to facilitate information retrieval and information extraction; and as a resource for ontology building. The article addresses how Wikipedia is being used as is, how it is being improved and adapted, and how it is being combined with other structures to create entirely new resources. We identify the research groups and individuals involved, and how their work has developed in the last few years. We provide a comprehensive list of the open-source software they have produced.
The Two Cultures: Mashing up Web 2.0 and the Semantic Web
- PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB. 2007 MAY 7-8
, 2007
"... A common perception is that there are two competing visions for the future evolution of the Web: the Semantic Web and Web 2.0. A closer look, though, reveals that the core technologies and concerns of these two approaches are complementary and that each field can and must draw from the other’s stren ..."
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Cited by 67 (3 self)
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A common perception is that there are two competing visions for the future evolution of the Web: the Semantic Web and Web 2.0. A closer look, though, reveals that the core technologies and concerns of these two approaches are complementary and that each field can and must draw from the other’s strengths. We believe that future web applications will retain the Web 2.0 focus on community and usability, while drawing on Semantic Web infrastructure to facilitate mashup-like information sharing. However, there are several open issues that must be addressed before such applications can become commonplace. In this paper, we outline a semantic weblogs scenario that illustrates the potential for combining Web 2.0 and Semantic Web technologies, while highlighting the unresolved issues that impede its realization. Nevertheless, we believe that the scenario can be realized in the short-term. We point to recent progress made in resolving each of the issues as well as future research directions for each of the communities.
Cool URIs for the semantic web
- W3C note, W3C
, 2008
"... The Resource Description Framework RDF allows you to describe web documents and resources from the real world—people, organisations, things—in a computer-processable way. Publishing such descriptions on the web creates the semantic web. URIs are very important as the link between RDF and the web. Th ..."
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Cited by 51 (5 self)
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The Resource Description Framework RDF allows you to describe web documents and resources from the real world—people, organisations, things—in a computer-processable way. Publishing such descriptions on the web creates the semantic web. URIs are very important as the link between RDF and the web. This article presents guidelines for their ef-fective use. We discuss two strategies, called 303 URIs and hash URIs. We give pointers to several web sites that use these solutions, and briefly discuss why several other proposals have problems. 1
ESTER: efficient search on text, entities, and relations
, 2007
"... We present ESTER, a modular and highly efficient system for combined full-text and ontology search. ESTER builds on a query engine that supports two basic operations: prefix search and join. Both of these can be implemented very efficiently with a compact index, yet in combination provide powerful q ..."
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Cited by 51 (4 self)
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We present ESTER, a modular and highly efficient system for combined full-text and ontology search. ESTER builds on a query engine that supports two basic operations: prefix search and join. Both of these can be implemented very efficiently with a compact index, yet in combination provide powerful querying capabilities. We show how ESTER can answer basic SPARQL graphpattern queries on the ontology by reducing them to a small number of these two basic operations. ESTER further supports a natural blend of such semantic queries with ordinary full-text queries. Moreover, the prefix search operation allows for a fully interactive and proactive user interface, which after every keystroke suggests to the user possible semantic interpretations of his or her query, and speculatively executes the most likely of these interpretations. As a proof of concept, we applied ESTER to the English Wikipedia, which contains about 3 million documents, combined with the recent YAGO ontology, which contains about 2.5 million facts. For a variety of complex queries, ESTER achieves worst-case query processing times of a fraction of a second, on a single machine, with an index size of about 4 GB.
