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Unlocking AI's Potential: Introducing Scallop, the Neurosymbolic Programming Language

In a groundbreaking development, researchers at the University of Pennsylvania have unveiled Scallop, a neurosymbolic programming language that merges deep learning with symbolic reasoning. This innov...

news.lavx.hu/article/unlocking

Our 3rd keynoter will be Enrico Motta from the Knowledge Media Institute of UK’s Open University. He pioneered with hybrid computational architectures integrating #ML with knowledge representation, being applied in application domains as e.g. news & scholarly analytics, and cognitive robotics.

Apply here: 2025.semanticwebschool.org/
Deadline: March 15, 2025

#knowledgegraph #AI #llm #generativeAI #responsibleAI @lysander07 @fiz_karlsruhe #semanticweb #lod #semanticweb #neurosymbolicai #robotics

These last three weeks, I had the chance to be back at Università degli Studi di Bari as an invited researcher, to continue working with Claudia d'Amato on neuro-symbolic AI and knowledge graphs, after a first research visit in 2024.

I also delivered a seminar to the PhD school of Computer Science and Mathematics, entitled: "The Schema Strikes Back: Refining #KnowledgeGraphs with Neuro-Symbolic AI".

TGDK is launching a Special Issue for Use-Case articles. They are interested in submissions describing use-cases (applications, events, products, services, etc.) that put research on Graph Data & Knowledge into practice. If you work on such initiatives, consider telling the community about them by submitting to the Special Issue!
Deadline is March 31, 2025.

drops.dagstuhl.de/entities/jou

@tgdkjournal #knowledgegraphs #semanticweb #ontologies #AI #neurosymbolicAI

drops.dagstuhl.deTGDK

Neuro-Symbolic AI in 2024: A systematic review. ~ Brandon C. Colelough, William Regli. arxiv.org/abs/2501.05435 #AI #NeuroSymbolicAI

arXiv.orgNeuro-Symbolic AI in 2024: A Systematic ReviewBackground: The field of Artificial Intelligence has undergone cyclical periods of growth and decline, known as AI summers and winters. Currently, we are in the third AI summer, characterized by significant advancements and commercialization, particularly in the integration of Symbolic AI and Sub-Symbolic AI, leading to the emergence of Neuro-Symbolic AI. Methods: The review followed the PRISMA methodology, utilizing databases such as IEEE Explore, Google Scholar, arXiv, ACM, and SpringerLink. The inclusion criteria targeted peer-reviewed papers published between 2020 and 2024. Papers were screened for relevance to Neuro-Symbolic AI, with further inclusion based on the availability of associated codebases to ensure reproducibility. Results: From an initial pool of 1,428 papers, 167 met the inclusion criteria and were analyzed in detail. The majority of research efforts are concentrated in the areas of learning and inference (63%), logic and reasoning (35%), and knowledge representation (44%). Explainability and trustworthiness are less represented (28%), with Meta-Cognition being the least explored area (5%). The review identifies significant interdisciplinary opportunities, particularly in integrating explainability and trustworthiness with other research areas. Conclusion: Neuro-Symbolic AI research has seen rapid growth since 2020, with concentrated efforts in learning and inference. Significant gaps remain in explainability, trustworthiness, and Meta-Cognition. Addressing these gaps through interdisciplinary research will be crucial for advancing the field towards more intelligent, reliable, and context-aware AI systems.

Last week, Sven Hertling from @fizise team presented our paper "Towards Large Language Models Interacting with Knowledge Graphs Via Function Calling" at the KBL-LM Workshop at #ISWC2024

presentation at Zenodo: doi.org/10.5281/zenodo.1418492
paper: lm-kbc.github.io/workshop2024/
GitHub: github.com/sven-h/LLMs4IE

#llms #generativeAI #AI #neurosymbolicAI #kbclm2024 #semanticweb #knowledgegraphs @fiz_karlsruhe @altbot

Our team member Sven Hertling is presenting "Towards Large Language Models Interacting with Knowledge Graphs Via Function Calling" at the Knowledge Base Construction from Pre-Trained Language Models Workshop at #ISWC2024

paper: lm-kbc.github.io/workshop2024/
KBC-LM2024 website: lm-kbc.github.io/workshop2024/

#llms #KBC #knowledgegraphs #semanticweb #symbolicAI #neurosymbolicAI @fiz_karlsruhe @unimannheim #iswc