A System for Discovering Relationships by Feature Extraction from Text Databases

Conrad, Jack G.; Utt, Mary Hunter

doi:10.1007/978-1-4471-2099-5_27

Cited by 31 publications

(29 citation statements)

References 4 publications

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“…Conrad and Utt [6] introduce techniques for extracting entities and identifying relationships between entities in large, free-text databases. The degree of association between entities is based on the co-occurrence within a fixed window size.…”

Section: Entity Retrievalmentioning

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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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: Entity Retrievalmentioning

confidence: 99%

“…Simply flattening the target category information and adding category names as terms to the term component is not an effective strategy; see (1) vs. (2), (4) vs. (6), and (5) (6) vs. (7)) leads to consistent improvements for all tasks and measures.…”

Section: The Performance Of Query Modelsmentioning

confidence: 99%

Category-Based Query Modeling for Entity Search

Balog

Bron

Rijke

2010

Lecture Notes in Computer Science

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“…In 1994, Conrad and Utt [8] used all paragraphs in the corpus surrounding named entity mentions to represent the entity, allowing free text queries to find names associated with a query. Ten years later, Raghavan et al [27] extended that idea to use language modeling as a representation and showed that these models could successfully be used to cluster, classify, or answer questions about entities.…”

Section: Entity Context Modelmentioning

confidence: 99%

Entity query feature expansion using knowledge base links

Dalton

Dietz

Allan

2014

Proceedings of the 37th International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

200

131

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Recent advances in automatic entity linking and knowledge base construction have resulted in entity annotations for document and query collections. For example, annotations of entities from large general purpose knowledge bases, such as Freebase and the Google Knowledge Graph. Understanding how to leverage these entity annotations of text to improve ad hoc document retrieval is an open research area. Query expansion is a commonly used technique to improve retrieval effectiveness. Most previous query expansion approaches focus on text, mainly using unigram concepts. In this paper, we propose a new technique, called entity query feature expansion (EQFE) which enriches the query with features from entities and their links to knowledge bases, including structured attributes and text. We experiment using both explicit query entity annotations and latent entities. We evaluate our technique on TREC text collections automatically annotated with knowledge base entity links, including the Google Freebase Annotations (FACC1) data. We find that entity-based feature expansion results in significant improvements in retrieval effectiveness over state-of-the-art text expansion approaches.

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“…Little is known, however, about the algorithms underlying these applications. Conrad and Utt [1994] introduce techniques for extracting entities and identifying relationships between entities in large, free-text databases. The degree of association between entities is based on the number of co-occurrences within a fixed window size.…”

Section: Entity Retrievalmentioning

confidence: 99%

Query modeling for entity search based on terms, categories, and examples

Balog

Bron

Rijke

2011

ACM Trans. Inf. Syst.

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Users often search for entities instead of documents, and in this setting, are willing to provide extra input, in addition to a series of query terms, such as category information and example entities. We propose a general probabilistic framework for entity search to evaluate and provide insights 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.

show abstract

A System for Discovering Relationships by Feature Extraction from Text Databases

Cited by 31 publications

References 4 publications

Category-Based Query Modeling for Entity Search

Category-Based Query Modeling for Entity Search

Entity query feature expansion using knowledge base links

Query modeling for entity search based on terms, categories, and examples

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