Discovering missing links in Wikipedia

Adafre, Sisay Fissaha; Rijke, Maarten de

doi:10.1145/1134271.1134284

Cited by 120 publications

(79 citation statements)

References 6 publications

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“…That said, our missing link processor represents a rudimentary but sufficient algorithm for this proof-of-concept stage. Future work may improve this area quite a bit, possibly enhancing the system of Adafre and Rijke (2005), which presented qualitatively promising results. Simply stated, we do a text search for all forms of links that already appear on a page and code matching non-linked forms as links.…”

Section: The Missing Link Problemmentioning

confidence: 96%

“…While the Internet as a whole suffers greatly from link spam, the larger problem in Wikipedia is missing links (Adafre and Rijke, 2005). This, of course, has a detrimental effect on ExploSR as a missing link essentially represents a missing relation.…”

Section: The Missing Link Problemmentioning

confidence: 99%

“…Simply stated, we do a text search for all forms of links that already appear on a page and code matching non-linked forms as links. A link's "forms" include the title of the target of the link, the set of "anchor texts" (Adafre and Rijke, 2005) that are used to describe that link (i.e. the link appears as "GIS" to Wikipedia readers, but the target of the link is "Geographic Information Science"), as well as the set of redirects to the link target defined globally in the Wikipedia data set.…”

Section: The Missing Link Problemmentioning

confidence: 99%

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GeoSR: Geographically Explore Semantic Relations in World Knowledge

Hecht

Raubal

Lecture Notes in Geoinformation and Cartography

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Abstract. Methods to determine the semantic relatedness (SR) value between two lexically expressed entities abound in the field of natural language processing (NLP). The goal of such efforts is to identify a single measure that summarizes the number and strength of the relationships between the two entities. In this paper, we present GeoSR, the first adaptation of SR methods to the context of geographic data exploration. By combining the first use of a knowledge repository structure that is replete with non-classical relations, a new means of explaining those relations to users, and the novel application of SR measures to a geographic reference system, GeoSR allows users to geographically navigate and investigate the world knowledge encoded in Wikipedia. There are numerous visualization and interaction paradigms possible with GeoSR; we present one implementation as a proof-of-concept and discuss others. Although, Wikipedia is used as the knowledge repository for our implementation, GeoSR will also work with any knowledge repository having a similar set of properties.

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Section: The Missing Link Problemmentioning

confidence: 96%

Section: The Missing Link Problemmentioning

confidence: 99%

Section: The Missing Link Problemmentioning

confidence: 99%

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GeoSR: Geographically Explore Semantic Relations in World Knowledge

Hecht

Raubal

Lecture Notes in Geoinformation and Cartography

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“…As a multilingual hypertext medium, Wikipedia has been shown to be a valuable new source of translation information [9,10]. Unlike the web, the hyperlinks in Wikipedia have a more consistent pattern and meaningful interpretation.…”

Section: Domain-specific Lexicon Constructionmentioning

confidence: 99%

Dublin City University at CLEF 2007: Cross-Language Speech Retrieval Experiments

Jones

Zhang

Lam-Adesina

2008

Lecture Notes in Computer Science

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Abstract. The Dublin City University participation in the CLEF 2007 CL-SR English task concentrated primarily on issues of topic translation. Our retrieval system used the BM25F model and pseudo relevance feedback. Topics were translated into English using the Yahoo! BabelFish free online service combined with domain-specific translation lexicons gathered automatically from Wikipedia. We explored alternative topic translation methods using these resources. Our results indicate that extending machine translation tools using automatically generated domainspecific translation lexicons can provide improved CLIR effectiveness for this task.

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“…[6] addressed the problem of discovering missing links in Wikipedia. Their work concentrated on improving the existing link structure of Wikipedia and was not integrated into the Wikipedia authoring interface.…”

Section: Related Workmentioning

confidence: 99%

Making More Wikipedians: Facilitating Semantics Reuse for Wikipedia Authoring

Wang

Zhu

et al. 2007

The Semantic Web

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Abstract. Wikipedia, a killer application in Web 2.0, has embraced the power of collaborative editing to harness collective intelligence. It can also serve as an ideal Semantic Web data source due to its abundance, influence, high quality and well-structuring. However, the heavy burden of up-building and maintaining such an enormous and ever-growing online encyclopedic knowledge base still rests on a very small group of people. Many casual users may still feel difficulties in writing high quality Wikipedia articles. In this paper, we use RDF graphs to model the key elements in Wikipedia authoring, and propose an integrated solution to make Wikipedia authoring easier based on RDF graph matching, expecting making more Wikipedians. Our solution facilitates semantics reuse and provides users with: 1) a link suggestion module that suggests and auto-completes internal links between Wikipedia articles for the user; 2) a category suggestion module that helps the user place her articles in correct categories. A prototype system is implemented and experimental results show significant improvements over existing solutions to link and category suggestion tasks. The proposed enhancements can be applied to attract more contributors and relieve the burden of professional editors, thus enhancing the current Wikipedia to make it an even better Semantic Web data source.

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Discovering missing links in Wikipedia

Cited by 120 publications

References 6 publications

GeoSR: Geographically Explore Semantic Relations in World Knowledge

GeoSR: Geographically Explore Semantic Relations in World Knowledge

Dublin City University at CLEF 2007: Cross-Language Speech Retrieval Experiments

Making More Wikipedians: Facilitating Semantics Reuse for Wikipedia Authoring

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