Egor Kostylev

• Kaminski, Mark; Kostylev, Egor; Grau, Bernardo Cuenca; Motik, Boris & Horrocks, Ian (2022). The Complexity and Expressive Power of Limit Datalog. Journal of the ACM. ISSN 0004-5411. 69(1). doi: 10.1145/3495009.
• Tena Cucala, David; Wałęga, Przemysław; Grau, Bernardo Cuenca & Kostylev, Egor (2021). Stratified Negation in Datalog with Metric Temporal Operators. In Leyton-Brown, Kevin & Mausam, Mausam (Ed.), Proceedings of the 35th National Conference on Artificial Intelligence‚ AAAI 2021 (volume 35 no. 7). AAAI Press. ISSN 978-1-57735-866-4. p. 6488–6495.
• Kostylev, Egor; Wałęga, Przemysław; Tena Cucala, David & Grau, Bernardo Cuenca (2021). DatalogMTL with Negation Under Stable Models Semantics. In Bienvenu, Meghyn; Gerhard, Lakemeyer & Erdem, Esra (Ed.), Proceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning (KR2021) . International Joint Conferences on Artificial Intelligence Organization (IJCAI). ISSN 978-1-956792-99-7. doi: 10.24963/kr.2021/58.
• Kamburjan, Eduard & Kostylev, Egor (2021). Type Checking Semantically Lifted Programs via Query Containment under Entailment Regimes. CEUR Workshop Proceedings. ISSN 1613-0073. 2954. Full text in Research Archive

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• Kostylev, Egor; Liu, Shuwen; Horrocks, Ian & Grau, Bernardo Cuenca (2021). INDIGO: GNN-Based Inductive Knowledge Graph Completion Using Pair-Wise Encoding. Show summary

  The aim of knowledge graph (KG) completion is to extend an incomplete KG with missing triples. Popular approaches based on graph embeddings typically work by first representing the KG in a vector space, and then applying a predefined scoring function to the resulting vectors to complete the KG. These approaches work well in transductive settings, where predicted triples involve only constants seen during training; however, they are not applicable in inductive settings, where the KG on which the model was trained is extended with new constants or merged with other KGs. The use of Graph Neural Networks (GNNs) has recently been proposed as a way to overcome these limitations; however, existing approaches do not fully exploit the capabilities of GNNs and still rely on heuristics and ad-hoc scoring functions. In this paper, we propose a novel approach, where the KG is fully encoded into a GNN in a transparent way, and where the predicted triples can be read out directly from the last layer of the GNN without the need for additional components or scoring functions. Our experiments show that our model outperforms state-of-the-art approaches on inductive KG completion benchmarks.

• Zhou, Dongzhuoran; Zhou, Baifan; Chen, Jieying; Cheng, Gong; Kostylev, Egor & Kharlamov, Evgeny (2021). Towards Ontology Reshaping for KG Generation with User-in-the-Loop: Applied to Bosch Welding. Show summary

  Knowledge graphs (KG) are used in a wide range of applications. The automation of KG generation is very desired due to the data volume and variety in industries. One important approach of KG generation is to map the raw data to a given KG schema, namely a domain ontology, and construct the entities and properties according to the ontology. However, the automatic generation of such ontology is demanding and existing solutions are often not satisfactory. An important challenge is a trade-off between two principles of ontology engineering: knowledge-orientation and data-orientation. The former one prescribes that an ontology should model the general knowledge of a domain, while the latter one emphasises on reflecting the data specificities to ensure good usability. We address this challenge by our method of ontology reshaping, which automates the process of converting a given domain ontology to a smaller ontology that serves as the KG schema. The domain ontology can be designed to be knowledge-oriented and the KG schema covers the data specificities. In addition, our approach allows the option of including user preferences in the loop. We demonstrate our on-going research on ontology reshaping and present an evaluation using real industrial data, with promising results.

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