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Neuro-symbolic systems have recently received significant attention in the scientific community. However, despite efforts in neural-symbolic integration, symbolic processing can still be better exploited, mainly when these hybrid approaches are defined on top of knowledge graphs. This work is built on the statement that knowledge graphs can naturally represent the convergence between data and their contextual meaning (i.e., knowledge). We propose a hybrid system that resorts to symbolic reasoning, expressed as a deductive database, to augment the contextual meaning of entities in a knowledge graph, thus, improving the performance of link prediction implemented using knowledge graph embedding (KGE) models. An entity context is defined as the ego network of the entity in a knowledge graph. Given a link prediction task, the proposed approach deduces new RDF triples in the ego networks of the entities corresponding to the heads and tails of the prediction task on the knowledge graph (KG). Since knowledge graphs may be incomplete and sparse, the facts deduced by the symbolic system not only reduce sparsity but also make explicit meaningful relations among the entities that compose an entity ego network. As a proof of concept, our approach is applied over a KG for lung cancer to predict treatment effectiveness. The empirical results put the deduction power of deductive databases into perspective. They indicate that making explicit deduced relationships in the ego networks empowers all the studied KGE models to generate more accurate links.<\/jats:p>","DOI":"10.3233\/sw-233324","type":"journal-article","created":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T10:35:50Z","timestamp":1686306950000},"page":"1307-1331","source":"Crossref","is-referenced-by-count":12,"title":["A neuro-symbolic system over knowledge graphs for link prediction"],"prefix":"10.1177","volume":"15","author":[{"given":"Ariam","family":"Rivas","sequence":"first","affiliation":[{"name":"Leibniz University of Hannover, Germany"},{"name":"TIB Leibniz Information Centre for Science and Technology, Germany"},{"name":"L3S Research Centre, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diego","family":"Collarana","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Intelligent Analysis and Information Systems, Dresden, Germany"},{"name":"Universidad Privada Boliviana, Cochabamba, Bolivia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria","family":"Torrente","sequence":"additional","affiliation":[{"name":"Department of Medical Oncology, Puerta de Hierro-Majadahonda University Hospital, 28222 Madrid, Spain"},{"name":"Faculty of Health Sciences, Francisco de Vitoria University, 28223 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria-Esther","family":"Vidal","sequence":"additional","affiliation":[{"name":"Leibniz University of Hannover, Germany"},{"name":"TIB Leibniz Information Centre for Science and Technology, Germany"},{"name":"L3S Research Centre, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/SW-233324_ref1","doi-asserted-by":"crossref","unstructured":"F.\u00a0Aisopos, S.\u00a0Jozashoori, E.\u00a0Niazmand, D.\u00a0Purohit, A.\u00a0Rivas, A.\u00a0Sakor, E.\u00a0Iglesias, D.\u00a0Vogiatzis, E.\u00a0Menasalvas, A.R.\u00a0Gonzalez, G.\u00a0Vigueras, D.\u00a0Gomez-Bravo, M.\u00a0Torrente, R.\u00a0Lopez, M.P.\u00a0Pulla, A.\u00a0Dalianis, A.\u00a0Triantafillou, G.\u00a0Paliouras and M.-E.\u00a0Vidal, Knowledge graphs for enhancing transparency in health data ecosystems, in: Semantic Web, 2023, https:\/\/www.semantic-web-journal.net\/content\/knowledge-graphs-enhancing-transparency-health-data-ecosystems-0.","DOI":"10.3233\/SW-223294"},{"key":"10.3233\/SW-233324_ref3","doi-asserted-by":"publisher","DOI":"10.3233\/faia210348"},{"key":"10.3233\/SW-233324_ref4","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-013-5363-6"},{"key":"10.3233\/SW-233324_ref5","unstructured":"A.\u00a0Bordes, N.\u00a0Usunier, A.\u00a0Garcia-Duran, J.\u00a0Weston and O.\u00a0Yakhnenko, Translating embeddings for modeling multi-relational data, in: Advances in Neural Information Processing Systems, C.J.C.\u00a0Burges, L.\u00a0Bottou, M.\u00a0Welling, Z.\u00a0Ghahramani and K.Q.\u00a0Weinberger, eds, Vol.\u00a026, Curran Associates, Inc., 2013, https:\/\/proceedings.neurips.cc\/paper\/2013\/file\/1cecc7a77928ca8133fa24680a88d2f9-Paper.pdf."},{"key":"10.3233\/SW-233324_ref6","doi-asserted-by":"crossref","unstructured":"A.\u00a0Bordes, J.\u00a0Weston, R.\u00a0Collobert and Y.\u00a0Bengio, Learning structured embeddings of knowledge bases, in: 25th Conference on Artificial Intelligence (AAAI), San Francisco, United States, 2011, pp.\u00a0301\u2013306, https:\/\/hal.archives-ouvertes.fr\/hal-00752498.","DOI":"10.1609\/aaai.v25i1.7917"},{"issue":"1","key":"10.3233\/SW-233324_ref7","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1109\/69.43410","article-title":"What you always wanted to know about datalog (and never dared to ask)","volume":"1","author":"Ceri","year":"1989","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.3233\/SW-233324_ref8","unstructured":"A.\u00a0d\u2019Avila Garcez and L.C.\u00a0Lamb, Neurosymbolic AI: The 3rd Wave, 2020, arXiv:2012.05876."},{"key":"10.3233\/SW-233324_ref9","doi-asserted-by":"crossref","unstructured":"A.S.\u00a0d\u2019Avila Garcez, K.\u00a0Broda and D.M.\u00a0Gabbay, Neural-symbolic learning systems\u00a0\u2013 foundations and applications, in: Perspectives in Neural Computing, 2002.","DOI":"10.1007\/978-1-4471-0211-3"},{"key":"10.3233\/SW-233324_ref10","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623623"},{"issue":"1","key":"10.3233\/SW-233324_ref11","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1109\/TSMCB.2005.855568","article-title":"Learning tactical human behavior through observation of human performance","volume":"36","author":"Fernlund","year":"2006","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)"},{"key":"10.3233\/SW-233324_ref12","unstructured":"A.\u00a0Fokoue, M.\u00a0Sadoghi, O.\u00a0Hassanzadeh and P.\u00a0Zhang, Predicting drug-drug interactions through large-scale similarity-based link prediction, in: The Semantic Web. 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