{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T05:48:11Z","timestamp":1751348891390},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:p>Knowledge graphs represent real-world entities and their relations in a semantically-rich structure supported by ontologies. Exploring this data with machine learning methods often relies on knowledge graph embeddings, which produce latent representations of entities that preserve structural and local graph neighbourhood properties, but sacrifice explainability. However, in tasks such as link or relation prediction, understanding which specific features better explain a relation is crucial to support complex or critical applications.\n\n\n\nWe propose SEEK, a novel approach for explainable representations to support relation prediction in knowledge graphs. It is based on identifying relevant shared semantic aspects (i.e., subgraphs) between entities and learning representations for each subgraph, producing a multi-faceted and explainable representation.\n\n\n\nWe evaluate SEEK on two real-world highly complex relation prediction tasks: protein-protein interaction prediction and gene-disease association prediction.\n\nOur extensive analysis using established benchmarks demonstrates that SEEK achieves comparable or even superior performance to standard learning representation methods while identifying both sufficient and necessary explanations based on shared semantic aspects.<\/jats:p>","DOI":"10.24963\/kr.2023\/62","type":"proceedings-article","created":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T22:27:47Z","timestamp":1690842467000},"page":"635-646","source":"Crossref","is-referenced-by-count":2,"title":["Explainable Representations for Relation Prediction in Knowledge Graphs"],"prefix":"10.24963","author":[{"given":"Rita T.","family":"Sousa","sequence":"first","affiliation":[{"name":"LASIGE, Faculdade de Ci\u00eancias da Universidade de Lisboa"}]},{"given":"Sara","family":"Silva","sequence":"additional","affiliation":[{"name":"LASIGE, Faculdade de Ci\u00eancias da Universidade de Lisboa"}]},{"given":"Catia","family":"Pesquita","sequence":"additional","affiliation":[{"name":"LASIGE, Faculdade de Ci\u00eancias da Universidade de Lisboa"}]}],"member":"10584","event":{"number":"20","sponsor":["Artificial Intelligence Journal","Principles of Knowledge Representation and Reasoning Inc.","Academic College of Tel-Aviv","European Association for Artificial Intelligence","National Science Foundation"],"acronym":"KR-2023","name":"20th International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}","start":{"date-parts":[[2023,9,2]]},"theme":"Artificial Intelligence","location":"Rhodes, Greece","end":{"date-parts":[[2023,9,8]]}},"container-title":["Proceedings of the Twentieth International Conference on Principles of Knowledge Representation and Reasoning"],"original-title":[],"deposited":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T22:28:50Z","timestamp":1690842530000},"score":1,"resource":{"primary":{"URL":"https:\/\/proceedings.kr.org\/2023\/62"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2023,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/kr.2023\/62","relation":{},"subject":[],"published":{"date-parts":[[2023,9]]}}}