Hi. My name's Dave, and I'm a PhD student at the University of Waikato in Hamilton, New Zealand. I'm studying under the supervision of Ian Witten, along with several other talented guys and girls who you should definitely go check out while you are here.

My research is about using Wikipedia to help people search. For brief, non-technical introductions to this, have a look at this poster and this newspaper article. You may also want to check out this Tech Talk.

There is also some software you can play with. Koru and Wikipedia Miner are both relevant to my current research, and both have online demos to try out.

My publications provide the details. The best ones (I think) are:

• Milne, D. and Witten, I.H. (2008) Learning to link with Wikipedia. In Proceedings of the ACM Conference on Information and Knowledge Management (CIKM'2008), Napa Valley, California.

  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.

  
• Milne, D. and Witten, I.H. (2008) An effective, low-cost measure of semantic relatedness obtained from Wikipedia links. In Proceedings of the first AAAI Workshop on Wikipedia and Artificial Intelligence (WIKIAI'08), Chicago, I.L.

  This paper describes a new technique for obtaining measures of semantic relatedness. Like other recent approaches, it uses Wikipedia to provide structured world knowledge about the terms of interest. Our approach is unique in that it does so using the hyperlink structure of Wikipedia rather than its category hierarchy or textual content. Evaluation with manually defined measures of semantic relatedness reveals this to be an effective compromise between the ease of computation of the former approach and the accuracy of the latter.

  
• Milne, D., Witten, I.H. and Nichols, D.M. (2007). A Knowledge-Based Search Engine Powered by Wikipedia. In Proceedings of the ACM Conference on Information and Knowledge Management (CIKM'2007), Lisbon, Portugal.

  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.

  

My research is funded by the Tertiary Education Commission of New Zealand.