Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Jain, Nitisha"'
Autor:
Zhao, Yihang, Zhang, Bohui, Hu, Xi, Ouyang, Shuyin, Kim, Jongmo, Jain, Nitisha, de Berardinis, Jacopo, Meroño-Peñuela, Albert, Simperl, Elena
Past ontology requirements engineering (ORE) has primarily relied on manual methods, such as interviews and collaborative forums, to gather user requirements from domain experts, especially in large projects. Current OntoChat offers a framework for O
Externí odkaz:
http://arxiv.org/abs/2408.15256
Autor:
Koutsiana, Elisavet, Reklos, Ioannis, Alghamdi, Kholoud Saad, Jain, Nitisha, Meroño-Peñuela, Albert, Simperl, Elena
We study collaboration patterns of Wikidata, one of the world's largest collaborative knowledge graph communities. Wikidata lacks long-term engagement with a small group of priceless members, 0.8%, to be responsible for 80% of contributions. Therefor
Externí odkaz:
http://arxiv.org/abs/2407.18278
Autor:
Jain, Nitisha, Akhtar, Mubashara, Giner-Miguelez, Joan, Shinde, Rajat, Vanschoren, Joaquin, Vogler, Steffen, Goswami, Sujata, Rao, Yuhan, Santos, Tim, Oala, Luis, Karamousadakis, Michalis, Maskey, Manil, Marcenac, Pierre, Conforti, Costanza, Kuchnik, Michael, Aroyo, Lora, Benjelloun, Omar, Simperl, Elena
Data is critical to advancing AI technologies, yet its quality and documentation remain significant challenges, leading to adverse downstream effects (e.g., potential biases) in AI applications. This paper addresses these issues by introducing Croiss
Externí odkaz:
http://arxiv.org/abs/2407.16883
Autor:
Akhtar, Mubashara, Benjelloun, Omar, Conforti, Costanza, Gijsbers, Pieter, Giner-Miguelez, Joan, Jain, Nitisha, Kuchnik, Michael, Lhoest, Quentin, Marcenac, Pierre, Maskey, Manil, Mattson, Peter, Oala, Luis, Ruyssen, Pierre, Shinde, Rajat, Simperl, Elena, Thomas, Goeffry, Tykhonov, Slava, Vanschoren, Joaquin, van der Velde, Jos, Vogler, Steffen, Wu, Carole-Jean
Data is a critical resource for Machine Learning (ML), yet working with data remains a key friction point. This paper introduces Croissant, a metadata format for datasets that simplifies how data is used by ML tools and frameworks. Croissant makes da
Externí odkaz:
http://arxiv.org/abs/2403.19546
In this work, we explore the use of Large Language Models (LLMs) for knowledge engineering tasks in the context of the ISWC 2023 LM-KBC Challenge. For this task, given subject and relation pairs sourced from Wikidata, we utilize pre-trained LLMs to p
Externí odkaz:
http://arxiv.org/abs/2309.08491
In this work, we study disagreement in discussions around Wikidata, an online knowledge community that builds the data backend of Wikipedia. Discussions are important in collaborative work as they can increase contributor performance and encourage th
Externí odkaz:
http://arxiv.org/abs/2306.11766
Autor:
Jain, Nitisha, Krestel, Ralf
When it comes to comprehending and analyzing multi-relational data, the semantics of relations are crucial. Polysemous relations between different types of entities, that represent multiple semantics, are common in real-world relational datasets repr
Externí odkaz:
http://arxiv.org/abs/2202.08917
Autor:
Jain, Nitisha
Virtualization is one of the important enabling technologies for Cloud Computing which facilitates sharing of resources among the virtual machines. However, it incurs performance overheads due to contention of physical devices such as disk and networ
Externí odkaz:
http://etd.iisc.ernet.in/2005/3765
http://etd.iisc.ernet.in/abstracts/4636/G26964-Abs.pdf
http://etd.iisc.ernet.in/abstracts/4636/G26964-Abs.pdf
Akademický článek
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Publikováno v:
National Journal of Integrated Research in Medicine. Jan/Feb2021, Vol. 12 Issue 1, p104-110. 7p.