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Modeling multi-step relevance propagation for expert finding
- In CIKM ’08
, 2008
"... 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 (pers ..."
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Cited by 32 (3 self)
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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 stateof-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 topicspecific 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.
Robust Question Answering over the Web of Linked Data
"... Knowledge bases and the Web of Linked Data have become important assets for search, recommendation, and analytics. Natural-language questions are a user-friendly mode of tapping this wealth of knowledge and data. However, question answering technology does not work robustly in this setting as questi ..."
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Cited by 6 (2 self)
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Knowledge bases and the Web of Linked Data have become important assets for search, recommendation, and analytics. Natural-language questions are a user-friendly mode of tapping this wealth of knowledge and data. However, question answering technology does not work robustly in this setting as questions have to be translated into structured queries and users have to be careful in phrasing their questions. This paper advocates a new approach that allows questions to be partially translated into relaxed queries, covering the essential but not necessarily all aspects of the user’s input. To compensate for the omissions, we exploit textual sources associated with entities and relational facts. Our system translates user questionsintoanextendedform ofstructured SPARQLqueries,withtextpredicatesattachedtotriplepatterns. Our solution is based on a novel optimization model, cast into an integer linear program, for joint decomposition and disambiguation of the user question. We demonstrate the quality of our methods through experiments with the QALD benchmark. Categories andSubject Descriptors
Finding Images of Rare and Ambiguous Entities
, 2011
"... Despite much progress on entity-oriented Web search and automatically constructed knowledge bases with millions of entities, it is still difficult to find images of named entities like people or places. While images of famous entities are abundant on the Internet, they are much harder to retrieve fo ..."
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Cited by 1 (1 self)
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Despite much progress on entity-oriented Web search and automatically constructed knowledge bases with millions of entities, it is still difficult to find images of named entities like people or places. While images of famous entities are abundant on the Internet, they are much harder to retrieve for less popular entities such as notable computer scientists or regionally interesting churches. Querying the entity names in image search engines yields large candidate lists, but they often have low precision and unsatisfactory recall. In this paper, we propose a principled model for finding images of rare or ambiguous named entities. We propose a set of efficient, light-weight algorithms for identifying entity-specific keyphrases from a given textual description of the entity, which we then use to score candidate images based on the matches of keyphrases in the underlying Web pages. Our experiments with a variety of entity categories show the high precision-recall quality of
Employing Document Dependency in Blog Search
, 2012
"... The goal in blog search is to rank blogs according to their recurrent relevance to the topic of the query. State of the art approaches view it as an expert search or resource selection problem. We investigate the effect of content-based similarity between posts on the performance of the retrieval sy ..."
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The goal in blog search is to rank blogs according to their recurrent relevance to the topic of the query. State of the art approaches view it as an expert search or resource selection problem. We investigate the effect of content-based similarity between posts on the performance of the retrieval system. We test two different approaches for smoothing (regularizing) relevance scores of posts based on their de-pendencies. In the first approach, we smooth term distributions describing posts by performing a random walk over a document-term graph in which similar posts are highly connected. In the second, we directly smooth scores for posts using a regular-ization framework that aims to minimize the discrepancy between scores for similar documents. We then extend these approaches to consider the time interval between the posts in smoothing the scores. The idea is that if two posts are temporally close, they are good sources for smoothing each other’s relevance scores. We compare these methods with the state of the art approaches in blog search that employ Language Modeling based resource selection algorithms and fusion-based methods for aggregat-ing post relevance scores. We show performance gains over the baseline techniques which do not take advantage of the relation between posts for smoothing relevance 1 estimates.
Declaration
"... I hereby solemnly declare that this work was created on my own, using only the resources and tools mentioned. Information taken from other sources or indirectly adopted data and concepts are explicitly acknowledged with references to the respective sources. This work has not been submitted in a proc ..."
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I hereby solemnly declare that this work was created on my own, using only the resources and tools mentioned. Information taken from other sources or indirectly adopted data and concepts are explicitly acknowledged with references to the respective sources. This work has not been submitted in a process for obtaining an academic degree elsewhere in the same or in similar form. Eidesstattliche Versicherung Hiermit versichere ich an Eides statt, dass ich die vorliegende Arbeit selbststädig und ohne Benutzung anderer als der angegebenen Hilfsmittel angefertigt habe. Die aus anderen Quellen oder indirekt übernommenen Daten und Konzepte sind unter Angabe der Quelle gekennzeichnet. Die Arbeit wurde bisher weder im In- noch im
Search and Retrieval. General Terms:
"... Expert finding is a rapidly developing Information Retrieval task and a popular research domain. The opportunity of search for knowledgeable people in the scope of an organization or world-wide is a feature which makes modern Enterprise search systems commercially successful and socially demanded. A ..."
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Expert finding is a rapidly developing Information Retrieval task and a popular research domain. The opportunity of search for knowledgeable people in the scope of an organization or world-wide is a feature which makes modern Enterprise search systems commercially successful and socially demanded. A number of efficient expert finding approaches was proposed recently. Despite that most of them are based on theoretically sound measures of expertness, they still use rather unrealistic and oversimplified principles. In our research we try to avoid these limitations and come up with models that go beyond the assumptions used in state-of-theart expert finding methods. The fundamental principle of existing approaches to expert finding is to infer expertise by analyzing the co-occurrence of personal identifiers and query terms in the scope of
Too Many Mammals: Improving the Diversity of Automatically Recognized Terms
"... Automatic Term Recognition systems extract domain-specific terms from text corpora. Un-fortunately current systems fail to capture the whole of the domain covered by a corpus. To address this problem, we present a novel term re-ranking method that generates term lists con-taining terms that are not ..."
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Automatic Term Recognition systems extract domain-specific terms from text corpora. Un-fortunately current systems fail to capture the whole of the domain covered by a corpus. To address this problem, we present a novel term re-ranking method that generates term lists con-taining terms that are not only individually salient, but also contribute to a globally diverse list that is truly representative of the corpus. We show that, even without any prior knowl-edge about the domain, our proposed method improves the diversity of the results produced by two popular automatic term recognition algo-rithms.
Search and Retrieval.
"... Expert finding is a rapidly developing Information Re-trieval task and a popular research domain. The opportunity of search for knowledgeable people in the scope of an orga-nization or world-wide is a feature which makes modern En-terprise search systems commercially successful and socially demanded ..."
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Expert finding is a rapidly developing Information Re-trieval task and a popular research domain. The opportunity of search for knowledgeable people in the scope of an orga-nization or world-wide is a feature which makes modern En-terprise search systems commercially successful and socially demanded. A number of efficient expert finding approaches was proposed recently. Despite that most of them are based on theoretically sound measures of expertness, they still use rather unrealistic and oversimplified principles. In our re-search we try to avoid these limitations and come up with models that go beyond the assumptions used in state-of-the-art expert finding methods. The fundamental principle of existing approaches to ex-pert finding is to infer expertise by analyzing the co-occurr-ence of personal identifiers and query terms in the scope of
