{"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/beyond-markov-logic-efficient-mining-of","title":"Beyond Markov Logic: Efficient Mining of Prediction Rules in Large Graphs","arxiv_id":"1802.03638","date":"2018-02-10","proceeding":null,"authors":["Tommaso Soru","André Valdestilhas","Edgard Marx","Axel-Cyrille Ngonga Ngomo"],"abstract":"Graph representations of large knowledge bases may comprise billions of\nedges. Usually built upon human-generated ontologies, several knowledge bases\ndo not feature declared ontological rules and are far from being complete.\nCurrent rule mining approaches rely on schemata or store the graph in-memory,\nwhich can be unfeasible for large graphs. In this paper, we introduce\nHornConcerto, an algorithm to discover Horn clauses in large graphs without the\nneed of a schema. Using a standard fact-based confidence score, we can mine\nclose Horn rules having an arbitrary body size. We show that our method can\noutperform existing approaches in terms of runtime and memory consumption and\nmine high-quality rules for the link prediction task, achieving\nstate-of-the-art results on a widely-used benchmark. Moreover, we find that\nrules alone can perform inference significantly faster than embedding-based\nmethods and achieve accuracies on link prediction comparable to\nresource-demanding approaches such as Markov Logic Networks.","url_abs":"http://arxiv.org/abs/1802.03638v2","url_pdf":"http://arxiv.org/pdf/1802.03638v2.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":"beyond-markov-logic-efficient-mining-of","repo_url":"https://github.com/mommi84/horn-concerto","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"link-prediction","task_name":"Link Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}