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.

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 TREC's expert finding task.

We propose an expert nding method based on 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 document's content depends on its position in this document with respect to the positions of the query terms. The experiments on the ocial Enter- prise TREC data demonstrate the advantage of our method over the method based on independence of query terms and persons in a document.

We introduce a novel approach to expert finding based on multi-step relevance propagation from documents to related candidates. Relevance propagation is modeled with an absorbing random walk. The evaluation on the two official Enterprise TREC data sets demonstrates the advantage of our method over the state-of-the-art method based on onestep propagation. Categories and Subject Descriptors: H.3 [Information Storage and Retrieval]: H.3.3 Information

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.