Query Expansion with Freebase

By Chenyan Xiong, Jamie Callan. 2015

128 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Information Retrieval & Search, Natural Language Processing

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Citations in the corpus

Word-Entity Duet Representations for Document Ranking (2017, background): “Query representation can be improved by introducing related entities and their texts to expand the query [4, 20].”
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval (2020, background): “Addressing this vocabulary mismatch is a core research topic in IR [8, 20, 21, 22].”
Modeling Relational Data with Graph Convolutional Networks (2017, background): “oth tasks. 1 Introduction Knowledge bases organize and store factual knowledge and enable a wide variety of applications, including question answering [42, 1, 34, 17, 3, 11] and information retrieval [22, 8, 40, 39]. Even the largest of knowledge bases (e.g., DBPedia, Wikidata or Yago), despite enor”
Explicit Semantic Ranking for Academic Search via Knowledge Graph Embedding (2017, background): “Freebase entities that are retrieved by the query [6] or frequently appear in top retrieved documents’ annotations [10] are usually related to the query’s information needs; better expansion terms can be found in these related entities’ descriptions [26].”
Bag-of-Entities Representation for Ranking (2016, methodology): “The rich semantic information in knowledge bases has been successfully used by ranking systems to better understand general domain queries, for example, to generate better expansion terms [8], richer learning to rank features [1], and additional connections between query and documents [5, 7].”
EsdRank: Connecting Query and Documents through External Semi-Structured Data (2015, background): “It is also well recognized as one of the most essential factors in determining the usefulness of ex-ternal data in prior research [7, 11, 20, 28].”
Improving Natural Language Inference Using External Knowledge in the Science Questions Domain (2018, methodology): “KGs have been used extensively in Information Retrieval (Bouchoucha, Liu, and Nie 2014), Recommendation Systems (Lalithsena et al. 2017; Kapanipathi et al. 2014) and Question Answering (Sun et al. 2018).”
Generalizing Translation Models in the Probabilistic Relevance Framework (2016, methodology): “Cui et al. [5] and later Gao and Nie [9] used a logarithm to weight a term with respect to the query and the term-term similarities: Another way to normalize the weights on some set T of candidate terms to be added to the query is: This was done for instance by Xiong and Callan [35] when considering”
Document Retrieval Using Entity-Based Language Models (2016, methodology): “Creating L by applying TermsLM over Wikipedia results in WikiRM [46], an ex-ternal corpus expansion approach also used in [13, 45].”
HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding (2018, background): “KGs have been found to be useful for a variety of tasks, viz., Information Retrieval (Kotov and Zhai, 2012; Xiong and Callan, 2015), Question Answering (Dong et al., 2015; Bordes et al., 2015; Yao and Durme, 2014), among others.”
An Empirical Study of Learning to Rank for Entity Search (2016, background): “Publicly available knowledge bases such as DBpedia, Freebase, and Wikipedia are beginning to be used for tasks such as document ranking, card retrieval, and question answering [3, 4, 5].”
To Clean or Not to Clean (2018, methodology): “The filtered version was created by eliminating all documents with a spam score of less than 70% (as suggested in https://plg.uwaterloo.ca/ ∼ gvcormac/clueweb09spam/ and used in References [28, 54, 71, 78]).”
Query Expansion Techniques for Information Retrieval: a Survey (2017, unknown): “For selection of the expansion term, reference [276] developed two methods: (1) utilization of tf.”
Document Retrieval Model Through Semantic Linking (2017, methodology): “In [33] and [7], Freebase and Wikipedia are used for expanding query terms.”