{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/earl-joint-entity-and-relation-linking-for","title":"EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs","arxiv_id":"1801.03825","date":"2018-01-11","proceeding":null,"authors":["Mohnish Dubey","Debayan Banerjee","Debanjan Chaudhuri","Jens Lehmann"],"abstract":"Many question answering systems over knowledge graphs rely on entity and\nrelation linking components in order to connect the natural language input to\nthe underlying knowledge graph. Traditionally, entity linking and relation\nlinking have been performed either as dependent sequential tasks or as\nindependent parallel tasks. In this paper, we propose a framework called EARL,\nwhich performs entity linking and relation linking as a joint task. EARL\nimplements two different solution strategies for which we provide a comparative\nanalysis in this paper: The first strategy is a formalisation of the joint\nentity and relation linking tasks as an instance of the Generalised Travelling\nSalesman Problem (GTSP). In order to be computationally feasible, we employ\napproximate GTSP solvers. The second strategy uses machine learning in order to\nexploit the connection density between nodes in the knowledge graph. It relies\non three base features and re-ranking steps in order to predict entities and\nrelations. We compare the strategies and evaluate them on a dataset with 5000\nquestions. Both strategies significantly outperform the current\nstate-of-the-art approaches for entity and relation linking.","url_abs":"http://arxiv.org/abs/1801.03825v4","url_pdf":"http://arxiv.org/pdf/1801.03825v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"earl-joint-entity-and-relation-linking-for","repo_url":"https://github.com/AskNowQA/EARL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"entity-linking","task_name":"Entity Linking"},{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"re-ranking","task_name":"Re-Ranking"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"relation-linking","task_name":"Relation Linking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1801.03825","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}