Tapping into knowledge base for concept feedback: leveraging conceptnet to improve search results for difficult queries

By Alexander Kotov, Chengxiang Zhai. 2012

83 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

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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].”
Learning Concept Embeddings for Query Expansion by Quantum Entropy Minimization (2014, background): “The most known are the recent Deep Structured Semantic Model (DSSM) (Huang et al. 2013) and Supervised Semantic Indexing (SSI) (Bai et al. 2009).”
Semantic Matching in Search (2014, background): “Kotov and Zhai [104] conduct query expansion with ConceptNet 5 [127], a graph-based knowledge base of common sense.”
Generalizing Translation Models in the Probabilistic Relevance Framework (2016, methodology): “That is, we can either use general knowledge about the terms, extracted from exter-nal resources such as logs [5,9], manual or automatic knowledge-bases [16, 35, 37], or we can use some form of pseudo-relevance feedback (PRF) [1,18,31].”
Document Retrieval Using Entity-Based Language Models (2016, unknown): “There is work on query expansion using entity-based information [43, 34, 30, 40, 8, 10, 18, 46, 6, 20, 7, 35, 39, 13, 31, 29, 45] and on projecting queries and documents onto an entity space to compare them [14, 33, 44].”
Query Expansion with Freebase (2015, background): “Another challenge of query expansion is its ‘high risk / high reward’ property, that often as many queries are damaged as improved.”
Query Expansion Techniques for Information Retrieval: a Survey (2017, unknown): “29 and many articles [279,142,58,99, 149,290] have used it for probabilistic query reweighting.”
KORE: keyphrase overlap relatedness for entity disambiguation (2012, background): “has been shown in a wide variety of tasks: named entity disambiguation [23, 19, 16], general word sense disambiguation [31, 24, 26], query expansion for information retrieval [18, 25], Web-based information extraction [1], knowledge base population [9, 30], and more.”
HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding (2018, background): “, Information Retrieval (Kotov and Zhai, 2012; Xiong and Callan, 2015), Question Answering (Dong et al.”
Utilizing Knowledge Graphs in Text-centric Information Retrieval (2017, methodology): “We cover query expansion approaches as well as adaptations of the learning-to-rank paradigm for this task [3, 8, 17].”