{"type":"ScholarlyArticle","title":"Cautious Rule-Based Collective Inference","authors":[{"name":"Martin Svatos","slug":"martin-svatos","given":"Martin","family":"Svatos","affiliation":"Department of Computer Science , Czech Technical University in Prague"}],"abstract":"Collective inference is a popular approach for solving tasks as knowledge graph completion within the statistical relational learning field. There are many existing solutions for this task, however, each of them is subjected to some limitation, either by restriction to only some learning settings, lacking interpretability of the model or theoretical test error bounds. We propose an approach based on cautious inference process which uses first-order rules and provides PAC-style bounds.","doi":"10.24963/ijcai.2019/922","url":"https://ijcai.org/proceedings/2019/922","pdf":"https://ijcai.org/proceedings/2019/0922.pdf","markdown":"https://ijcai.org/proceedings/2019/922.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/922","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":922,"pages":{"first":6466,"last":6467},"keywords":["Machine Learning: Relational Learning","Machine Learning: Knowledge-based Learning"],"videos":[],"references":[{"position":1,"raw":"[Galárraga et al., 2013] Luis Antonio Galárraga, Christina Teflioudi, Katja Hose, and Fabian Suchanek. Amie: as- sociation rule mining under incomplete evidence in onto- logical knowledge bases. In The 22nd International Con- ference on World Wide Web, pages 413-422, 2013.","title":"Amie: association rule mining under incomplete evidence in ontological knowledge bases","authors":"Luis Antonio Galárraga, Christina Teflioudi, Katja Hose, Fabian Suchanek","year":"2013","venue":"The 22nd International Conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Kuzelka et al., 2018] Ondrej Kuzelka, Yuyi Wang, Jesse Davis, and Steven Schockaert. Pac-reasoning in relational domains. In The Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, pages 927-936, 2018.","title":"Pac-reasoning in relational domains","authors":"Kuzelka","year":"2018","venue":"The Thirty-Fourth Conference on Uncertainty in Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Lang et al., 1991] Jérôme Lang, Didier Dubois, and Henri Prade. A logic of graded possibility and certainty cop- ing with partial inconsistency. In The Seventh conference on Uncertainty in Artificial Intelligence, pages 188-196, 1991.","title":"A logic of graded possibility and certainty coping with partial inconsistency","authors":"Jérôme Lang, Didier Dubois, Henri Prade","year":"1991","venue":"The Seventh conference on Uncertainty in Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Moore and Neville, 2017] John Moore and Jeninfer Neville. Deep collective inference. In Thirty-First AAAI Confer- ence on Artificial Intelligence, pages 2364-2372, 2017.","title":"John Moore and Jeninfer Neville. Deep collective inference","authors":"Neville Moore","year":"2017","venue":"Thirty-First AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Richardson and Domingos, 2006] Matthew Richardson and Pedro Domingos. Markov logic networks. Machine learn- ing, 62(1-2):107-136, 2006.","title":"Matthew Richardson and Pedro Domingos. Markov logic networks","authors":"Domingos Richardson","year":"2006","venue":"Machine learning","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Rocktäschel and Riedel, 2017] Tim Rocktäschel and Sebas- tian Riedel. End-to-end differentiable proving. In Pro- ceedings of the Annual Conference on Neural Information Processing Systems, pages 3791-3803, 2017.","title":"Tim Rocktäschel and Sebastian Riedel. End-to-end differentiable proving","authors":"Riedel Rocktäschel","year":"2017","venue":"Proceedings of the Annual Conference on Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Svatoš et al., 2017] Martin Svatoš, Gustav Šourek, Filip Železný, Steven Schockaert, and Ondřej Kuželka. Pruning hypothesis spaces using learned domain theories. In In- ternational Conference on Inductive Logic Programming, pages 152-168. Springer, 2017.","title":"Pruning hypothesis spaces using learned domain theories","authors":"Martin Svatoš, Gustav Šourek, Filip Železný, Steven Schockaert, Ondřej Kuželka","year":"2017","venue":"International Conference on Inductive Logic Programming","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Trouillon et al., 2016] Théo Trouillon, Johannes Welbl, Se- bastian Riedel, Éric Gaussier, and Guillaume Bouchard. Complex embeddings for simple link prediction. In Inter- national Conference on Machine Learning, pages 2071- 2080, 2016.","title":"Complex embeddings for simple link prediction","authors":"Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, Guillaume Bouchard","year":"2016","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}