Document Retrieval Model Through Semantic Linking

By Faezeh Ensan, Ebrahim Bagheri. 2017

79 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Information Retrieval & Search

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Chenyan Xiong
2. Zhiyuan Liu
3. Jamie Callan
4. Maosong Sun
5. Zhenghao Liu
6. Tie-Yan Liu
7. Jimmy J. Lin
8. Zhuyun Dai

In Information Systems

1. Jamie Callan
2. Chenyan Xiong
3. Zhenghao Liu
4. Zhiyuan Liu
5. Tie-Yan Liu
6. Maosong Sun
7. Jimmy J. Lin
8. Zhuyun Dai

In Information Retrieval & Search

1. Chenyan Xiong
2. Jamie Callan
3. Zhenghao Liu
4. Tie-Yan Liu
5. Zhiyuan Liu
6. Maosong Sun
7. Zhuyun Dai
8. Debasis Ganguly

Citations in the corpus

• Word-Entity Duet Representations for Document Ranking (2017, methodology): “…retrieval models, including vector space models [21] and language models [17]. entity-based soft match was studied by Semantics-Enabled Language Model (SELM); it connects query and documents in the entity space using their entities’ relatedness calculated from an entity linking system [6].”
• Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval (2018, background): “, 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., 2017c; Ensan and Bagheri, 2017).”
• To Clean or Not to Clean (2018, methodology): “…LM-JM, and given that researchers continue to use LM-JM, the recommendations of Zhai and Lafferty [76, 77] notwithstanding (e.g., see References [6, 30, 35, 36, 55, 58]), our experiments thus demonstrate the importance of document parsing from the reproducibility perspective used for a particular…”
• A Survey on Application of Knowledge Graph (2020, background): “The work [33] models queries and documents as a set of semantic concepts obtained from running them through an entity linking system.”
• Product1M: Towards Weakly Supervised Instance-Level Product Retrieval via Cross-Modal Pretraining (2021, methodology): “Intra-modal retrieval [32, 1] has been extensively studied in the keyword-based web document retrieval [11], content-based image retrieval [29], and product recommendation [19, 20].”