Overview of the INEX 2008 Entity Ranking Track

Demartini, Gianluca; Vries, Arjen P. de; Iofciu, Tereza; Zhu, Jianhan

doi:10.1007/978-3-642-03761-0_25

Cited by 40 publications

(42 citation statements)

References 6 publications

(2 reference statements)

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“…Test Collection We use test sets of the INEX Entity Ranking track (INEX-XER) [8,9], that use (a dump of the English) Wikipedia as document collection from which (articles corresponding to) entities are to be returned. The collection consists of over 650,000 documents plus a hierarchy of (over 115,000) categories; this is not a strict hierarchy: assignments of categories to articles are not always consistent [8].…”

Section: Methodsmentioning

confidence: 99%

“…In 2007, the INEX Entity Ranking track (INEX-XER) [8,9] introduced tasks where candidate items are restricted to have their own Wikipedia page. Fissaha Adafre et al [10] addressed an early version of the entity ranking and list completion tasks and explored different representations of list descriptions and example entities.…”

Section: Entity Retrievalmentioning

confidence: 99%

“…We focus on abstractions of these scenarios, as they are evaluated in the context of INEX, the INitiative for the evaluation of XML retrieval. In 2007, INEX launched an entity ranking track [8,9]. Here, entities are represented by their Wikipedia page and queries asking for an entity are typed (asking for entities belonging to certain categories) and may come with examples.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Category-Based Query Modeling for Entity Search

Balog

Bron

Rijke

2010

Lecture Notes in Computer Science

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Abstract. Users often search for entities instead of documents and in this setting are willing to provide extra input, in addition to a query, such as category information and example entities. We propose a general probabilistic framework for entity search to evaluate and provide insight in the many ways of using these types of input for query modeling. We focus on the use of category information and show the advantage of a category-based representation over a term-based representation, and also demonstrate the effectiveness of category-based expansion using example entities. Our best performing model shows very competitive performance on the INEX-XER entity ranking and list completion tasks.

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Section: Methodsmentioning

confidence: 99%

Section: Entity Retrievalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Category-Based Query Modeling for Entity Search

Balog

Bron

Rijke

2010

Lecture Notes in Computer Science

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“…For instance, the Question Answering (QA) track of the Text Retrieval Conference (TREC) (Dang et al, 2007 (Demartini et al, 2009), where the task was to find entities in Wikipedia in response to a query. An example of a 2009 query is: "I want a list of movies, fully or partly shot in Venice."…”

Section: Introductionmentioning

confidence: 99%

A domain-independent approach to finding related entities

Vechtomova

Robertson

2012

Information Processing & Management

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We propose an approach to the retrieval of entities that have a specific relationship with the entity given in a query. Our research goal is to investigate whether related entity finding problem can be addressed by combining a measure of relatedness of candidate answer entities to the query, and likelihood that the candidate answer entity belongs to the target entity category specified in the query. An initial list of candidate entities, extracted from top ranked documents retrieved for the query, is refined using a number of statistical and linguistic methods. The proposed method extracts the category of the target entity from the query, identifies instances of this category as seed entities, and computes similarity between candidate and seed entities. The evaluation was conducted on the Related Entity Finding task of the Entity Track of TREC 2010, as well as the QA list questions from TREC 2005 and 2006. Evaluation results demonstrate that the proposed methods are effective in finding related entities.

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“…The Enitity Ranking track of this year's INEX features three tasks: entity ranking, list completion, and entity relation search [3]. In our participation, we focus on the first and second tasks, leaving entity relation search for coming years.…”

Section: Introductionmentioning

confidence: 99%

A Generative Language Modeling Approach for Ranking Entities

Weerkamp

Balog

Meij

2009

Lecture Notes in Computer Science

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Abstract. We describe our participation in the INEX 2008 Entity Ranking track. We develop a generative language modeling approach for the entity ranking and list completion tasks. Our framework comprises the following components: (i) entity and (ii) query language models, (iii) entity prior, (iv) the probability of an entity for a given category, and (v) the probability of an entity given another entity. We explore various ways of estimating these components, and report on our results. We find that improving the estimation of these components has very positive effects on performance, yet, there is room for further improvements.

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Overview of the INEX 2008 Entity Ranking Track

Abstract: Abstract. In some situations search engine users would prefer to retrieve entities instead of just documents. Example queries include "Italian Nobel prize winners", "Formula

Cited by 40 publications

References 6 publications

Category-Based Query Modeling for Entity Search

Category-Based Query Modeling for Entity Search

A domain-independent approach to finding related entities

A Generative Language Modeling Approach for Ranking Entities

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