{"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/feature-based-reformulation-of-entities-in","title":"Feature-based reformulation of entities in triple pattern queries","arxiv_id":"1807.01801","date":"2018-07-04","proceeding":null,"authors":["Amar Viswanathan","Geeth de Mel","James A. Hendler"],"abstract":"Knowledge graphs encode uniquely identifiable entities to other entities or\nliteral values by means of relationships, thus enabling semantically rich\nquerying over the stored data. Typically, the semantics of such queries are\noften crisp thereby resulting in crisp answers. Query log statistics show that\na majority of the queries issued to knowledge graphs are often entity centric\nqueries. When a user needs additional answers the state-of-the-art in assisting\nusers is to rewrite the original query resulting in a set of approximations.\nSeveral strategies have been proposed in past to address this. They typically\nmove up the taxonomy to relax a specific element to a more generic element.\nEntities don't have a taxonomy and they end up being generalized. To address\nthis issue, in this paper, we propose an entity centric reformulation strategy\nthat utilizes schema information and entity features present in the graph to\nsuggest rewrites. Once the features are identified, the entity in concern is\nreformulated as a set of features. Since entities can have a large number of\nfeatures, we introduce strategies that select the top-k most relevant and\n{informative ranked features and augment them to the original query to create a\nvalid reformulation. We then evaluate our approach by showing that our\nreformulation strategy produces results that are more informative when compared\nwith state-of-the-art","url_abs":"http://arxiv.org/abs/1807.01801v1","url_pdf":"http://arxiv.org/pdf/1807.01801v1.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":"feature-based-reformulation-of-entities-in","repo_url":"https://github.com/N00bsie/QueryReformulation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":null,"task_name":"valid"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}