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- research-articleMay 2024
Universal Knowledge Graph Embeddings
- N'Dah Jean Kouagou,
- Caglar Demir,
- Hamada M. Zahera,
- Adrian Wilke,
- Stefan Heindorf,
- Jiayi Li,
- Axel-Cyrille Ngonga Ngomo
WWW '24: Companion Proceedings of the ACM Web Conference 2024Pages 1793–1797https://doi.org/10.1145/3589335.3651978A variety of knowledge graph embedding approaches have been developed. Most of them obtain embeddings by learning the structure of the knowledge graph within a link prediction setting. As a result, the embeddings reflect only the structure of a single ...
- ArticleSeptember 2023
LitCQD: Multi-hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals
Machine Learning and Knowledge Discovery in Databases: Research TrackPages 617–633https://doi.org/10.1007/978-3-031-43418-1_37AbstractMost real-world knowledge graphs, including Wikidata, DBpedia, and Yago are incomplete. Answering queries on such incomplete graphs is an important, but challenging problem. Recently, a number of approaches, including complex query decomposition (...
- research-articleAugust 2023
Neuro-symbolic class expression learning
IJCAI '23: Proceedings of the Thirty-Second International Joint Conference on Artificial IntelligenceArticle No.: 403, Pages 3624–3632https://doi.org/10.24963/ijcai.2023/403Models computed using deep learning have been effectively applied to tackle various problems in many disciplines. Yet, the predictions of these models are often at most post-hoc and locally explainable. In contrast, class expressions in description logics ...
- ArticleMay 2023
Neural Class Expression Synthesis
AbstractMany applications require explainable node classification in knowledge graphs. Towards this end, a popular “white-box” approach is class expression learning: Given sets of positive and negative nodes, class expressions in description logics are ...
- research-articleJune 2022
Kronecker Decomposition for Knowledge Graph Embeddings
HT '22: Proceedings of the 33rd ACM Conference on Hypertext and Social MediaPages 1–10https://doi.org/10.1145/3511095.3531276Knowledge graph embedding research has mainly focused on learning continuous representations of entities and relations tailored towards the link prediction problem. Recent results indicate an ever increasing predictive ability of current approaches on ...
- ArticleMay 2022
- research-articleApril 2022
EvoLearner: Learning Description Logics with Evolutionary Algorithms
- Stefan Heindorf,
- Lukas Blübaum,
- Nick Düsterhus,
- Till Werner,
- Varun Nandkumar Golani,
- Caglar Demir,
- Axel-Cyrille Ngonga Ngomo
WWW '22: Proceedings of the ACM Web Conference 2022Pages 818–828https://doi.org/10.1145/3485447.3511925Classifying nodes in knowledge graphs is an important task, e.g., for predicting missing types of entities, predicting which molecules cause cancer, or predicting which drugs are promising treatment candidates. While black-box models often achieve high ...
- ArticleJune 2021
- ArticleNovember 2019
A Physical Embedding Model for Knowledge Graphs
AbstractKnowledge graph embedding methods learn continuous vector representations for entities in knowledge graphs and have been used successfully in a large number of applications. We present a novel and scalable paradigm for the computation of knowledge ...