David N. Milne
| David N. Milne|
(Alternative names for this author)
|Co-authors||Anna Huang, Catherine Legg, David M. Nichols, Eibe Frank, Ian H. Witten, Olena Medelyan|
|Authorship||Publications (12), datasets (0), tools (0)|
|Citations||Total (15), average (1.25), median (1), max (4), min (0)|
|DBLP · Google Scholar|
|Export and share|
|BibTeX, CSV, RDF, JSON|
|Browse properties · List of authors|
David N. Milne is an author.
PublicationsOnly those publications related to wikis are shown here.
|Title||Keyword(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|A link-based visual search engine for Wikipedia||Exploratory search
|Clustering Documents Using a Wikipedia-Based Concept Representation||English||2009||This paper shows how Wikipedia and the semantic knowledge it contains can be exploited for document clustering. We first create a concept-based document representation by mapping the terms and phrases within documents to their corresponding articles (or concepts) in Wikipedia. We also developed a similarity measure that evaluates the semantic relatedness between concept sets for two documents. We test the concept-based representation and the similarity measure on two standard text document datasets. Empirical results show that although further optimizations could be performed, our approach already improves upon related techniques.||0||0|
|Mining meaning from Wikipedia||Information extraction
Natural Language Processing
|Int. J. Hum.-Comput. Stud.
International Journal of Human Computer Studies
|English||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 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. 2009 Elsevier Ltd. All rights reserved.||0||4|
|Augmenting Domain-Specific Thesauri With Knowledge from Wikipedia||English||2008||0||1|
|Clustering Documents with Active Learning Using Wikipedia||Data Mining, IEEE International Conference on||English||2008||Wikipedia has been applied as a background knowledge base to various text mining problems, but very few attempts have been made to utilize it for document clustering. In this paper we propose to exploit the semantic knowledge in Wikipedia for clustering, enabling the automatic grouping of documents with similar themes. Although clustering is intrinsically unsupervised, recent research has shown that incorporating supervision improves clustering performance, even when limited supervision is provided. The approach presented in this paper applies supervision using active learning. We first utilize Wikipedia to create a concept-based representation of a text document, with each concept associated to a Wikipedia article. We then exploit the semantic relatedness between Wikipedia concepts to find pair-wise instance-level constraints for supervised clustering, guiding clustering towards the direction indicated by the constraints. We test our approach on three standard text document datasets. Empirical results show that our basic document representation strategy yields comparable performance to previous attempts; and adding constraints improves clustering performance further by up to 20%.||0||0|
|Learning to link with Wikipedia||English||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 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 anyunstructured 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.
|Topic Indexing with Wikipedia||WikiAI||English||2008||0||2|
|A Knowledge-Based Search Engine Powered by Wikipedia||Information retrieval
|CIKM ‘07||2007||This paper describes a new technique for obtaining measures of semantic relatedness. Like other recent approaches, it uses Wikipedia to provide a vast amount of structured world knowledge about the terms of interest. Our system, the Wikipedia Link Vector Model or WLVM, is unique in that it does so using only the hyperlink structure of Wikipedia rather than its full textual content. To evaluate the algorithm we use a large, widely used test set of manually defined measures of semantic relatedness as our bench-mark. This allows direct comparison of our system with other similar techniques.||0||1|
|A knowledge-based search engine powered by Wikipedia||English||2007||This paper describes Koru, a new search interface that offers effective domain-independent knowledge-based information retrieval. Koru exhibits an understanding of the topics of both queries and documents. This allows it to (a) expand queries automatically and (b) help guide the user as they evolve their queries interactively. Its understanding is mined from the vast investment of manual effort and judgment that is Wikipedia. We show how this open, constantly evolving encyclopedia can yield inexpensive knowledge structures that are specifically tailored to expose the topics, terminology and semantics of individual document collections. We conducted a detailed user study with 12 participants and 10 topics from the 2005 TREC HARD track, and found that Koru and its underlying knowledge base offers significant advantages over traditional keyword search. It was capable of lending assistance to almost every query issued to it; making their entry more efficient, improving the relevance of the documents they return, and narrowing the gap between expert and novice seekers.||0||1|
|Computing Semantic Relatedness using Wikipedia Link Structure||Wikipedia
|Proc. of NZCSRSC, 2007||2007||This paper describes a new technique for obtaining measures of semantic relatedness. Like other recent approaches, it uses Wikipedia to provide a vast amount of structured world knowledge about the terms of interest. Our system, the Wikipedia Link Vector Model or WLVM, is unique in that it does so using only the hyperlink structure of Wikipedia rather than its full textual content. To evaluate the algorithm we use a large, widely used test set of manually defined measures of semantic relatedness as our bench-mark. This allows direct comparison of our system with other similar techniques.||0||2|
|Mining Domain-Specific Thesauri from Wikipedia: A Case Study||English||2006||0||1|
|Mining Domain-Specific Thesauri from Wikipedia: A case study||Datamining information-retrieval semantic text-mining wikipedia||ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06)||2006||Domain-specific thesauri are high-cost, high-maintenance, high-value knowledge structures. We show how the classic thesaurus structure of terms and links can be mined automatically from Wikipedia, a vast, open encyclopedia. In a comparison with a professional thesaurus for agriculture (Agrovoc) we find that Wikipedia contains a substantial proportion of its domain-specific concepts and semantic relations; furthermore it has impressive coverage of a collection of contemporary documents in the domain. Thesauri derived using these techniques are attractive because they capitalize on existing public efforts and tend to reflect contemporary language usage better than their costly, painstakingly-constructed manual counterparts.||0||1|