Finding Entities in Wikipedia Using Links and Categories

Kaptein, Rianne; Kamps, Jaap

doi:10.1007/978-3-642-03761-0_28

Cited by 15 publications

(12 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some teams expand the target categories, e.g., by using the category structure to expand with categories [15,25,30]. Others expand the target categories using lexical similarity between category labels and query terms [17,26].…”

Section: Entity Ranking At Inexmentioning

confidence: 99%

Category-Based Query Modeling for Entity Search

Balog

Bron

Rijke

2010

Lecture Notes in Computer Science

View full text Add to dashboard Cite

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.

show abstract

Section: Entity Ranking At Inexmentioning

confidence: 99%

Category-Based Query Modeling for Entity Search

Balog

Bron

Rijke

2010

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Since INEX was launched in 2002, which is an entity ranking task specific to structured data and multimedia (Demartini et al, 2010), structured features have been widely used in entity search, such as the most recent studies on Wikipedia links and categories (Vercoustre et al, 2008;Zhu et al, 2008;Jiang et al, 2009;Weerkamp et al, 2009;Kaptein and Kamps, 2009;Balog et al, 2011) and web link structure (Balog et al, 2008b;You et al, 2011;Blanco et al, 2011;Neumayer et al, 2012;Bron et al, 2013).…”

Section: Related Workmentioning

confidence: 99%

Biography-Dependent Collaborative Entity Archiving for Slot Filling

Yan

Wang²,

Chen³

et al. 2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

View full text Add to dashboard Cite

Knowledge Base Population (KBP) tasks, such as slot filling, show the particular importance of entity-oriented automatic relevant document acquisition. Rich, diverse and reliable relevant documents satisfy the fundamental requirement that a KBP system explores the nature of an entity. Towards the bottleneck problem between comprehensiveness and definiteness of acquisition, we propose a collaborative archiving method. In particular we introduce topic modeling methodologies into entity biography profiling, so as to build a bridge between fuzzy and exact matching. On one side, we employ the topics in a small-scale high-quality relevant documents (i.e., exact matching results) to summarize the life slices of a target entity (i.e., biography), and on the other side, we use the biography as a reliable reference material to detect new truly relevant documents from a large-scale partially complete pseudo-feedback (i.e., fuzzy matching results). We leverage the archiving method to enhance slot filling systems. Experiments on KBP corpus show significant improvement over stateof-the-art.

show abstract

“…Nearly all INEX participants have used category information; many of them made this explicit in a separate category component in the overall ranking formula Weerkamp et al 2009;Zhu et al 2008;Jiang et al 2009;Kaptein and Kamps 2009;Vercoustre et al 2009]. A standard way of combining the category and term-based components was to use a language modeling approach and to estimate the probability of an entity given the query and category information [Jiang et al 2009;Weerkamp et al 2009;Zhu et al 2008].…”

Section: Entity Ranking At Inexmentioning

confidence: 99%

“…A standard way of combining the category and term-based components was to use a language modeling approach and to estimate the probability of an entity given the query and category information [Jiang et al 2009;Weerkamp et al 2009;Zhu et al 2008]. Calculating the similarity between the categories of answer entities and the target categories or between the categories of answer entities and the set of categories attached to example entities is sometimes based on lexical similarity , on the content of categories (concatenating all text that belongs to that category) [Kaptein and Kamps 2009], or on the overlap ratio between sets of categories [Weerkamp et al 2009]. Another popular solution was to add categories as a separate metadata field to the content of documents and apply a multi-field retrieval model (e.g., Zhu et al [2008] and Demartini et al [2008]).…”

Section: Entity Ranking At Inexmentioning

confidence: 99%

“…Some teams have experimented with expanding the target categories, for example, using the category structure to expand with categories up to a certain level [Jämsen et al 2008;Weerkamp et al 2009;Tsikrika et al 2008]. Others expand the target categories using lexical similarity between category labels and query terms Kaptein and Kamps 2009]. The categorization in Wikipedia is not a well-defined "is-a" hierarchy; that is, members of a subcategory are not necessarily members of its supercategory.…”

Section: Entity Ranking At Inexmentioning

confidence: 99%

See 1 more Smart Citation

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

Balog

Bron

Rijke

2011

ACM Trans. Inf. Syst.

View full text Add to dashboard Cite

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

Finding Entities in Wikipedia Using Links and Categories

Cited by 15 publications

References 3 publications

Category-Based Query Modeling for Entity Search

Category-Based Query Modeling for Entity Search

Biography-Dependent Collaborative Entity Archiving for Slot Filling

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

Contact Info

Product

Resources

About