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Capturing Knowledge in Semantically-typed Relational Patterns to Enhance Relation Linking

Published: 04 December 2017 Publication History

Abstract

Transforming natural language questions into formal queries is an integral task in Question Answering (QA) systems. QA systems built on knowledge graphs like DBpedia, require a step after natural language processing for linking words, specifically including named entities and relations, to their corresponding entities in a knowledge graph. To achieve this task, several approaches rely on background knowledge bases containing semantically-typed relations, e.g., PATTY, for an extra disambiguation step. Two major factors may affect the performance of relation linking approaches whenever background knowledge bases are accessed: a) limited availability of such semantic knowledge sources, and b) lack of a systematic approach on how to maximize the benefits of the collected knowledge. We tackle this problem and devise SIBKB, a semantic-based index able to capture knowledge encoded on background knowledge bases like PATTY. SIBKB represents a background knowledge base as a bi-partite and a dynamic index over the relation patterns included in the knowledge base. Moreover, we develop a relation linking component able to exploit SIBKB features. The benefits of SIBKB are empirically studied on existing QA benchmarks and observed results suggest that SIBKB is able to enhance the accuracy of relation linking by up to three times.

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  • (2023)Candidate Set Expansion for Entity and Relation Linking Based on Mutual Entity–Relation InteractionBig Data and Cognitive Computing10.3390/bdcc70100567:1(56)Online publication date: 22-Mar-2023
  • (2023)Joint linking of entity and relation for question answering over knowledge graphMultimedia Tools and Applications10.1007/s11042-023-15646-w82:29(44801-44818)Online publication date: 5-May-2023
  • (2023)Information extraction pipelines for knowledge graphsKnowledge and Information Systems10.1007/s10115-022-01826-x65:5(1989-2016)Online publication date: 7-Jan-2023
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  1. Capturing Knowledge in Semantically-typed Relational Patterns to Enhance Relation Linking

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      cover image ACM Conferences
      K-CAP '17: Proceedings of the 9th Knowledge Capture Conference
      December 2017
      271 pages
      ISBN:9781450355537
      DOI:10.1145/3148011
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      Published: 04 December 2017

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      Author Tags

      1. Knowledge Capture
      2. Knowledge Graphs
      3. Question Answering Systems
      4. Relation Linking

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      K-CAP 2017
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      K-CAP 2017: Knowledge Capture Conference
      December 4 - 6, 2017
      TX, Austin, USA

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      Cited By

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      • (2023)Candidate Set Expansion for Entity and Relation Linking Based on Mutual Entity–Relation InteractionBig Data and Cognitive Computing10.3390/bdcc70100567:1(56)Online publication date: 22-Mar-2023
      • (2023)Joint linking of entity and relation for question answering over knowledge graphMultimedia Tools and Applications10.1007/s11042-023-15646-w82:29(44801-44818)Online publication date: 5-May-2023
      • (2023)Information extraction pipelines for knowledge graphsKnowledge and Information Systems10.1007/s10115-022-01826-x65:5(1989-2016)Online publication date: 7-Jan-2023
      • (2022)SPaReL: A Semantic Parsing Relation Linking Method for Knowledge Base Question AnsweringProceedings of the 11th International Joint Conference on Knowledge Graphs10.1145/3579051.3579055(73-81)Online publication date: 27-Oct-2022
      • (2021)Towards combinational relation linking over knowledge graphsWorld Wide Web10.1007/s11280-021-00951-x24:6(1975-1994)Online publication date: 1-Nov-2021
      • (2020)Leveraging Semantic Parsing for Relation Linking over Knowledge BasesThe Semantic Web – ISWC 202010.1007/978-3-030-62419-4_23(402-419)Online publication date: 2-Nov-2020
      • (2019)Leveraging Context Information for Joint Entity and Relation LinkingWeb and Big Data10.1007/978-3-030-33982-1_3(23-36)Online publication date: 1-Aug-2019
      • (2019)Entity Enabled Relation LinkingThe Semantic Web – ISWC 201910.1007/978-3-030-30793-6_30(523-538)Online publication date: 17-Oct-2019
      • (2018)Why Reinvent the WheelProceedings of the 2018 World Wide Web Conference10.1145/3178876.3186023(1247-1256)Online publication date: 10-Apr-2018
      • (2018)Extracting Meaningful Correlations among Heterogeneous Datasets for Medical Question Answering with Domain Knowledge2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)10.1109/IEMCON.2018.8615045(297-301)Online publication date: Nov-2018
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