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of 990
pro vyhledávání: '"Ngomo AN"'
The connection between inconsistent databases and Dung's abstract argumentation framework has recently drawn growing interest. Specifically, an inconsistent database, involving certain types of integrity constraints such as functional and inclusion d
Externí odkaz:
http://arxiv.org/abs/2412.11617
Autor:
Ilievski, Filip, Hammer, Barbara, van Harmelen, Frank, Paassen, Benjamin, Saralajew, Sascha, Schmid, Ute, Biehl, Michael, Bolognesi, Marianna, Dong, Xin Luna, Gashteovski, Kiril, Hitzler, Pascal, Marra, Giuseppe, Minervini, Pasquale, Mundt, Martin, Ngomo, Axel-Cyrille Ngonga, Oltramari, Alessandro, Pasi, Gabriella, Saribatur, Zeynep G., Serafini, Luciano, Shawe-Taylor, John, Shwartz, Vered, Skitalinskaya, Gabriella, Stachl, Clemens, van de Ven, Gido M., Villmann, Thomas
Recent advances in AI -- including generative approaches -- have resulted in technology that can support humans in scientific discovery and decision support but may also disrupt democracies and target individuals. The responsible use of AI increasing
Externí odkaz:
http://arxiv.org/abs/2411.15626
In recent years, knowledge graphs have gained interest and witnessed widespread applications in various domains, such as information retrieval, question-answering, recommendation systems, amongst others. Large-scale knowledge graphs to this end have
Externí odkaz:
http://arxiv.org/abs/2410.21163
In recent years, knowledge graph embedding models have been successfully applied in the transductive setting to tackle various challenging tasks including link prediction, and query answering. Yet, the transductive setting does not allow for reasonin
Externí odkaz:
http://arxiv.org/abs/2410.06742
We introduce a novel embedding method diverging from conventional approaches by operating within function spaces of finite dimension rather than finite vector space, thus departing significantly from standard knowledge graph embedding techniques. Ini
Externí odkaz:
http://arxiv.org/abs/2409.14857
Purpose: The query language GraphQL has gained significant traction in recent years. In particular, it has recently gained the attention of the semantic web and graph database communities and is now often used as a means to query knowledge graphs. Mo
Externí odkaz:
http://arxiv.org/abs/2409.12646
Autor:
Srivastava, Nikit, Kuchelev, Denis, Ngoli, Tatiana Moteu, Shetty, Kshitij, Röder, Michael, Zahera, Hamada, Moussallem, Diego, Ngomo, Axel-Cyrille Ngonga
This paper presents LOLA, a massively multilingual large language model trained on more than 160 languages using a sparse Mixture-of-Experts Transformer architecture. Our architectural and implementation choices address the challenge of harnessing li
Externí odkaz:
http://arxiv.org/abs/2409.11272
Publikováno v:
The Semantic Web . ISWC 2022. ISWC 2022. Lecture Notes in Computer Science, vol 13489. Springer, Cham
We consider fact-checking approaches that aim to predict the veracity of assertions in knowledge graphs. Five main categories of fact-checking approaches for knowledge graphs have been proposed in the recent literature, of which each is subject to pa
Externí odkaz:
http://arxiv.org/abs/2409.06692
Knowledge Graph Embedding (KGE) transforms a discrete Knowledge Graph (KG) into a continuous vector space facilitating its use in various AI-driven applications like Semantic Search, Question Answering, or Recommenders. While KGE approaches are effec
Externí odkaz:
http://arxiv.org/abs/2407.06855
Autor:
Srivastava, Nikit, Ma, Mengshi, Vollmers, Daniel, Zahera, Hamada, Moussallem, Diego, Ngomo, Axel-Cyrille Ngonga
Knowledge Graph Question Answering (KGQA) simplifies querying vast amounts of knowledge stored in a graph-based model using natural language. However, the research has largely concentrated on English, putting non-English speakers at a disadvantage. M
Externí odkaz:
http://arxiv.org/abs/2407.06041