Zobrazeno 1 - 10
of 391
pro vyhledávání: '"Höhndorf, A."'
Ontology embeddings map classes, relations, and individuals in ontologies into $\mathbb{R}^n$, and within $\mathbb{R}^n$ similarity between entities can be computed or new axioms inferred. For ontologies in the Description Logic $\mathcal{EL}^{++}$,
Externí odkaz:
http://arxiv.org/abs/2411.01574
Autor:
Ghunaim, Yasir, Hoehndorf, Robert
Pre-training machine learning models on molecular properties has proven effective for generating robust and generalizable representations, which is critical for advancements in drug discovery and materials science. While recent work has primarily foc
Externí odkaz:
http://arxiv.org/abs/2410.11914
Autor:
Buali, Mahdi, Hoehndorf, Robert
Automated Theorem Proving (ATP) faces challenges due to its complexity and computational demands. Recent work has explored using Large Language Models (LLMs) for ATP action selection, but these methods can be resource-intensive. This study introduces
Externí odkaz:
http://arxiv.org/abs/2407.14521
Autor:
Chen, Jiaoyan, Mashkova, Olga, Zhapa-Camacho, Fernando, Hoehndorf, Robert, He, Yuan, Horrocks, Ian
Ontologies are widely used for representing domain knowledge and meta data, playing an increasingly important role in Information Systems, the Semantic Web, Bioinformatics and many other domains. However, logical reasoning that ontologies can directl
Externí odkaz:
http://arxiv.org/abs/2406.10964
Ontology embeddings map classes, relations, and individuals in ontologies into $\mathbb{R}^n$, and within $\mathbb{R}^n$ similarity between entities can be computed or new axioms inferred. For ontologies in the Description Logic $\mathcal{EL}^{++}$,
Externí odkaz:
http://arxiv.org/abs/2405.04868
Generative Adversarial Networks are used for generating the data using a generator and a discriminator, GANs usually produce high-quality images, but training GANs in an adversarial setting is a difficult task. GANs require high computation power and
Externí odkaz:
http://arxiv.org/abs/2307.16275
Autor:
Callahan, Tiffany J., Tripodi, Ignacio J., Stefanski, Adrianne L., Cappelletti, Luca, Taneja, Sanya B., Wyrwa, Jordan M., Casiraghi, Elena, Matentzoglu, Nicolas A., Reese, Justin, Silverstein, Jonathan C., Hoyt, Charles Tapley, Boyce, Richard D., Malec, Scott A., Unni, Deepak R., Joachimiak, Marcin P., Robinson, Peter N., Mungall, Christopher J., Cavalleri, Emanuele, Fontana, Tommaso, Valentini, Giorgio, Mesiti, Marco, Gillenwater, Lucas A., Santangelo, Brook, Vasilevsky, Nicole A., Hoehndorf, Robert, Bennett, Tellen D., Ryan, Patrick B., Hripcsak, George, Kahn, Michael G., Bada, Michael, Baumgartner Jr, William A., Hunter, Lawrence E.
Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowle
Externí odkaz:
http://arxiv.org/abs/2307.05727
Generating vector representations (embeddings) of OWL ontologies is a growing task due to its applications in predicting missing facts and knowledge-enhanced learning in fields such as bioinformatics. The underlying semantics of OWL ontologies are ex
Externí odkaz:
http://arxiv.org/abs/2305.07163
Several approaches have been developed that generate embeddings for Description Logic ontologies and use these embeddings in machine learning. One approach of generating ontologies embeddings is by first embedding the ontologies into a graph structur
Externí odkaz:
http://arxiv.org/abs/2303.16519
Knowledge graphs and ontologies are becoming increasingly important as technical solutions for Findable, Accessible, Interoperable, and Reusable data and metadata (FAIR Guiding Principles). We discuss four challenges that impede the use of FAIR knowl
Externí odkaz:
http://arxiv.org/abs/2301.01227