{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T11:07:34Z","timestamp":1749035254831},"reference-count":0,"publisher":"EasyChair","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Domain-oriented knowledge bases (KBs) such as DBpedia and YAGO are largely constructed by applying a set of predefined extraction rules to the semi-structured contents of Wikipedia articles. Although both of these large-scale KBs achieve very high average precision values (above 95% for YAGO3), subtle mistakes in a few of the underlying ex- traction rules may still impose a substantial amount of systematic extraction mistakes for specific relations. For example, by applying the same regular expressions to extract per- son names of both Asian and Western nationality, YAGO erroneously swaps most of the family and given names of Asian person entities. For traditional rule-learning approaches based on Inductive Logic Programming (ILP), it is very difficult to detect these systematic extraction mistakes, since they usually occur only in a relatively small subdomain of the relations\u2019 arguments. In this paper, we thus propose a guided form of ILP, coined \u201cGILP\u201d, that iteratively asks for small amounts of user feedback over a given KB to learn a set of data-cleaning rules that (1) best match the feedback and (2) also generalize to a larger portion of facts in the KB. We propose both algorithms and respective metrics to automatically assess the quality of the learned rules with respect to the user feedback.<\/jats:p>","DOI":"10.29007\/ppgx","type":"proceedings-article","created":{"date-parts":[[2020,4,27]],"date-time":"2020-04-27T22:10:09Z","timestamp":1588025409000},"page":"92-76","source":"Crossref","is-referenced-by-count":3,"title":["Guided Inductive Logic Programming: Cleaning Knowledge Bases with Iterative User Feedback"],"prefix":"10.29007","volume":"72","author":[{"given":"Yan","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinchuan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Plarent","family":"Haxhidauti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vinu Ellampallil","family":"Venugopal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Theobald","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"11545","event":{"name":"GCAI 2020. 6th Global Conference on Artificial Intelligence (GCAI 2020)"},"container-title":["EPiC Series in Computing"],"original-title":[],"deposited":{"date-parts":[[2020,4,27]],"date-time":"2020-04-27T22:10:11Z","timestamp":1588025411000},"score":1,"resource":{"primary":{"URL":"https:\/\/easychair.org\/publications\/paper\/N3D1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[null]]},"references-count":0,"URL":"https:\/\/doi.org\/10.29007\/ppgx","relation":{},"ISSN":["2398-7340"],"issn-type":[{"type":"print","value":"2398-7340"}],"subject":[]}}