{"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/enriching-knowledge-bases-with-counting","title":"Enriching Knowledge Bases with Counting Quantifiers","arxiv_id":"1807.03656","date":"2018-07-10","proceeding":null,"authors":["Paramita Mirza","Simon Razniewski","Fariz Darari","Gerhard Weikum"],"abstract":"Information extraction traditionally focuses on extracting relations between\nidentifiable entities, such as <Monterey, locatedIn, California>. Yet, texts\noften also contain Counting information, stating that a subject is in a\nspecific relation with a number of objects, without mentioning the objects\nthemselves, for example, \"California is divided into 58 counties\". Such\ncounting quantifiers can help in a variety of tasks such as query answering or\nknowledge base curation, but are neglected by prior work. This paper develops\nthe first full-fledged system for extracting counting information from text,\ncalled CINEX. We employ distant supervision using fact counts from a knowledge\nbase as training seeds, and develop novel techniques for dealing with several\nchallenges: (i) non-maximal training seeds due to the incompleteness of\nknowledge bases, (ii) sparse and skewed observations in text sources, and (iii)\nhigh diversity of linguistic patterns. Experiments with five human-evaluated\nrelations show that CINEX can achieve 60% average precision for extracting\ncounting information. In a large-scale experiment, we demonstrate the potential\nfor knowledge base enrichment by applying CINEX to 2,474 frequent relations in\nWikidata. CINEX can assert the existence of 2.5M facts for 110 distinct\nrelations, which is 28% more than the existing Wikidata facts for these\nrelations.","url_abs":"http://arxiv.org/abs/1807.03656v1","url_pdf":"http://arxiv.org/pdf/1807.03656v1.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":"enriching-knowledge-bases-with-counting","repo_url":"https://github.com/paramitamirza/CINEX","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"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}