Entity query feature expansion using knowledge base links

By Jeffrey Dalton, Laura Dietz, James Allan. 2014

287 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research, Information Retrieval & Search, Knowledge Graphs & Representation

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Citations in the corpus

Modeling Relational Data with Graph Convolutional Networks (2017, background): “Knowledge bases organize and store factual knowledge and enable a wide variety of applications, including question answering [41, 1, 33, 16, 2, 10] and information retrieval [21, 7, 39, 38].”
Context-Aware Document Term Weighting for Ad-Hoc Search (2020, methodology): “Spam filter was not applied as suggested in [12].”
Query Expansion Techniques for Information Retrieval: a Survey (2017, unknown): “Reference [77] propose entity query feature expansion (EQFE) technique.”
Integrating and Evaluating Neural Word Embeddings in Information Retrieval (2015, background): “[11] demonstrates how query representations can be enriched with features from semantic annotations and their links to knowledge bases, such as FreeBase.”
Knowledge Graphs: Opportunities and Challenges (2023, methodology): “Information retrieval EQFE (Dalton et al. 2014) Query knowledge graph-based feature expansion”
Convolutional Neural Networks for Soft-Matching N-Grams in Ad-hoc Search (2018, unknown): “We followed a standard re-ranking methodology in prior research [8, 28]: re-rank the top 100 candidate documents retrieved by Galago using sequential dependency model queries; the INQUERY stopword list augmented with web-specic stop words; KStemming; and spam ltering using Waterloo spam score with”
Explicit Semantic Ranking for Academic Search via Knowledge Graph Embedding (2017, background): “Each textual attribute provides unique textual similarity features with the document [8].”
Document Retrieval Model Through Semantic Linking (2017, methodology): “As suggested in [7] due to limited computational resources, we do not entity link all documents in the document collections.”
Document Retrieval Using Entity-Based Language Models (2016, methodology): “The resultant approach, which simultaneously expands the query with both terms and entities, is conceptually reminiscent of some methods recently proposed by Dalton et al. [13].”
Bag-of-Entities Representation for Ranking (2016, methodology): “The choice of knowledge base in this paper is Freebase, one of the largest public knowledge bases frequently used in recent IR research [1, 5, 7, 8].”
TREC Complex Answer Retrieval Overview (2018, methodology): “• TREMA-UNH (UNH): We provided three passage runs and three entity runs that were all based on combination of low-level input runs such as BM25, Query likelihood, SDM, RM3 and Entity Context model [4] with combinations trained with coordinate ascent Learning-to-Rank optimized for MAP.”
Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs (2019, methodology): “KGs are the building blocks for many applications like information retrieval [Dalton et al., 2014], recommendation systems [Catherine and Cohen, 2016], question-answering [Cui et al., 2017], etc.”
Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval (2018, background): “, 2016), richer ranking features (Dalton et al., 2014), entity-based connections between query and documents (Liu and Fang, 2015; Xiong and Callan, 2015), and soft-match query and documents through knowledge graph relations or embeddings (Xiong et al.”
The Neural Hype and Comparisons Against Weak Baselines (2019, background): “[6] for their entity query feature expansion (EQFE) model.”
A Survey on Application of Knowledge Graph (2020, background): “For example, the work [31] provides features from entities themselves and links between”
Query Expansion with Freebase (2015, methodology): “Keywords Knowledge Base; Query Expansion; Freebase; Pseudo Relevance Feedback”
Analogical Inference for Multi-relational Embeddings (2017, methodology): “, 2010), information retrieval (Dalton et al., 2014) and natural language processing (Gabrilovich & Markovitch, 2009).”