Explicit Semantic Ranking for Academic Search via Knowledge Graph Embedding

By Chenyan Xiong, Russell Power, Jamie Callan. 2017

473 citations recorded by the source corpus.

This paper introduces Explicit Semantic Ranking (ESR), a new ranking technique that leverages knowledge graph embedding. Analysis of the query log from our academic search engine, SemanticScholar.org, reveals that a major error source is its inability to understand the meaning of research concepts in queries. To addresses this challenge, ESR represents queries and documents in the entity space and ranks them based on their semantic connections from their knowledge graph embedding. Experiments demonstrate ESR's ability in improving Semantic Scholar's online production system, especially on hard queries where word-based ranking fails.

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, methodology): “Recently, Explicit Semantic Ranking (ESR) performs learning to rank with query and documents’ entity representations in scholar search [23].”
A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications (2017, background): “[41], [42] O min(station, company),O ð2Þ min(station, role),O ð2Þ min(destination, boarding) PLE [84] Orank(mention-type) +O min(mention, feature) + O ð2Þ min(type, type) IONE [26] O min(node, node) +O(anchor align) HEBE [45] O min(node, other nodes in one hyperedge) GAKE [38] Oð2Þ max(node, neighbo”
Continual Relation Learning via Episodic Memory Activation and Reconsolidation (2020, background): “…entities from text, e.g., extracting the relation “ the president of ” from the given sentence “ Newton served as the president of the Royal Society ”, which could serve as external resource for various downstream applications (Dong et al., 2015; Xiong et al., 2017; Schlichtkrull et al., 2018).”
Neural Snowball for Few-Shot Relation Learning (2019, background): “Knowledge graphs (KGs) such as Word-Net (Miller, 1995), Freebase (Bollacker et al., 2008) and Wikidata (Vrandeˇci´c and Kr¨otzsch, 2014) have multiple applications in information retrieval (Xiong et al., 2017), question answering (Hao et al., 2017) and recommender systems (Zhang et al., 2016).”
Construction of the Literature Graph in Semantic Scholar (2018, unknown): “In this paper, we focus on the problem of extracting structured data from scientiﬁc documents, which can later be used in natural language interfaces (e.g., Iyer et al., 2017) or to improve ranking of results in academic search (e.g., Xiong et al., 2017).”
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space (2018, background): “), Yago (Suchanek et al., 2007), and WordNet (Miller, 1995). Knowledge graphs are potentially useful to a variety of applications such as question-answering (Hao et al., 2017), information retrieval (Xiong et al., 2017), recommender systems (Zhang et al., 2016), and natural language processing (Yang”
Consistent Representation Learning for Continual Relation Extraction (2022, background): “Relation extraction (RE) is an essential issue in information extraction (IE), which can apply to many downstream NLP tasks, such as information retrieval (Xiong et al., 2017) and question and answer (Tao et al.”
Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion (2020, background): “KGs as supporting knowledge play significant roles across a wide range of natural language processing (NLP) tasks, such as dialogue system [15], information retrieval [40], recommendation system [46], etc.”
Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval (2018, methodology): “EDRM significantly outperforms the word-interaction-based neural ranking model, K-NRM (Xiong et al., 2017a), confirming the advantage of entities in enriching word-based ranking.”
Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications (2022, unknown): “The knowledge graph based semantics has also been employed in data integration [31], recommendation systems [32], and realtime ranking [33]–[35].”
A Survey on Knowledge Graphs: Representation, Acquisition, and Applications (2020, background): “Numerous downstream knowledge-aware applications also come up with many datasets, for example, WikiFacts [203] for language modeling; SimpleQuestions [155] and LC-QuAD [204] for question answering; and Freebase Semantic Scholar [179] for academic search.”
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network (2017, background): “These KBs are useful resources in many applications such as semantic searching and ranking (Kasneci et al., 2008; Schuhmacher and Ponzetto, 2014; Xiong et al., 2017), question answering (Zhang et al., 2016; Hao et al., 2017) and machine reading (Yang and Mitchell, 2017).”
More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020, background): “, 2014), search engine (Xiong et al., 2017; Schlichtkrull et al., 2018) and question answering (Bordes et al.”
Efficient Interaction-based Neural Ranking with Locality Sensitive Hashing (2019, methodology): “Neural networks have been applied in information retrieval and text mining tasks (e.g. [5, 9, 20, 23, 24, 33–36]).”