Kelly Y. Itakura

University of Waterloo at INEX 2008: Adhoc, Book, and Link-the-Wiki Tracks

In this paper, we describe University of Waterloos ap- proaches to the Adhoc, Book, and Link-the-Wiki tracks. For the Adhoc track, we submitted runs for all the tasks, the Focused, the Relevant-in-Context, and the Best-in-Context tasks. The results show that we ranked first among all participants for each task, by the simple scoring of elements using Okapi BM25. In the Book track, we participated in the Book retrieval and the Page-in-Context tasks, by using the approaches we used in the Adhoc track. We attribute our poor performance to lack of training. In the Link-the-Wiki track, we submitted runs for both File-to-File and Anchor-to-BEP tasks, using PageRank [1] algorithms on top of our previous years algorithms that yielded high performance. The results indicate that our baseline approaches work best, although other approaches have rooms for improvement.

Using dynamic markov compression to detect vandalism in the wikipedia

We apply the Dynamic Markov Compression model to detect spam edits in the Wikipedia. The method appears to outperform previous efforts based on compression models, providing performance comparable to methods based on manually constructed rules.

University of waterloo at INEX 2009: ad hoc, book, entity ranking, and link-the-wiki tracks

This year, University of Waterloo participated in four tracks; Ad Hoc, Book, Entity Ranking, and Link-the-Wiki tracks. In Ad Hoc and Book tracks, we implemented a variation of Okapi BM25F [20,5,18,15] that gave substantial improvements over the baseline BM25 that ranked first in the previous year [12,13], during the training and in the official Ad Hoc-focused results. In Entity ranking track, we used redundancy techniques [4] for question answering to retrieve entities. In Link-the-Wiki track, we employed topic-oriented PageRank with KL divergence in addition to the baseline described in [11].

A framework for BM25F-based XML retrieval

We evaluate a framework for BM25F-based XML element retrieval. The framework gathers contextual information associated with each XML element into an associated field, which we call a characteristic field. The contents of the element and the contents of the characteristic field are then treated as distinct fields for BM25F weighting purposes. Evidence supporting this framework is drawn from both our own experiments and experiments reported in related work.

Topical and structural linkage in wikipedia

We explore statistical properties of links within Wikipedia. We demonstrate that a simple algorithm can predict many of the links that would normally be added to a new article, without considering the topic of the article itself. We then explore a variant of topic-oriented PageRank, which can effectively identify topical links within existing articles, when compared with manual judgments of their topical relevance. Based on these results, we suggest that linkages within Wikipedia arise from a combination of structural requirements and topical relationships