Henning Rode

Ranking very many typed entities on wikipedia

We discuss the problem of ranking very many entities of different types. In particular we deal with a heterogeneous set of types, some being very generic and some very specific. We discuss two approaches for this problem: i) exploiting the entity containment graph and ii) using a Web search engine to compute entity relevance. We evaluate these approaches on the real task of ranking Wikipedia entities typed with a state-of-the-art named-entity tagger. Results show that both approaches can greatly increase the performance of methods based only on passage retrieval.

Modeling multi-step relevance propagation for expert finding

An expert finding system allows a user to type a simple text query and retrieve names and contact information of individuals that possess the expertise expressed in the query. This paper proposes a novel approach to expert finding in large enterprises or intranets by modeling candidate experts (persons), web documents and various relations among them with so-called expertise graphs. As distinct from the state of-the-art approaches estimating personal expertise through one-step propagation of relevance probability from documents to the related candidates, our methods are based on the principle of multi-step relevance propagation in topic specific expertise graphs. We model the process of expert finding by probabilistic random walks of three kinds: finite, infinite and absorbing. Experiments on TREC Enterprise Track data originating from two large organizations show that our methods using multi-step relevance propagation improve over the baseline one-step propagation based method in almost all cases.

Combining document- and paragraph-based entity ranking

We study entity ranking on the INEX entity track and propose a simple graph-based ranking approach that enables to combine scores on document and paragraph level. The combined approach improves the retrieval results not only on the INEX testset, but similarly on TRECs expert finding task.

Using query profiles for clarification

The following paper proposes a new kind of relevance feedback. It shows how so-called query profiles can be employed for disambiguation and clarification. Query profiles provide useful summarized previews on the retrieved answers to a given query. They outline ambiguity in the query and when combined with appropriate means of interactivity allow the user to easily adapt the final ranking. Statistical analysis of the profiles even enables the retrieval system to automatically suggest search restrictions or preferences. The paper shows a preliminary experimental study of the proposed feedback methods within the setting of TRECs interactive HARD track.

Exploiting sequential dependencies for expert finding

We propose an expert finding method based on assumption of sequential dependence between a candidate expert and the query terms in the scope of a document. We assume that the strength of relation of a candidate to the documents content depends on its position in this document with respect to the positions of the query terms. The experiments on the official Enterprise TREC data demonstrate the advantage of our method over the method based on independence of query terms and persons in a document.

Efficient XML and Entity Retrieval with PF/Tijah: CWI and University of Twente at INEX'08

The paper describes the submissions of CWI and University of Twente to the efficiency and entity ranking track of INEX 2008. With the INEX participation, we demonstrate and evaluate the functionality of our open source XML retrieval system PF/Tijah.

University of twente at GeoCLEF 2006: geofiltered document retrieval

This paper describes the approach of the University of Twente at its first participation in GeoCLEF. A large effort went into the construction of a geographic thesaurus which was utilized to add geographic knowledge to the documents and queries. Geographic filtering was applied to the results returned from a retrieval by content run. Employing such a geographic knowledge base however showed no added value - the content-only baseline outperformed all geographically filtered runs.