@conference {998, title = {CompTox Ontology: Leveraging Knowledge Graphs for PFAS Monitoring and Decision-Making}, booktitle = {THE ACM WEB CONFERENCE 2026}, year = {2026}, address = {Dubai, United Arab Emirates, April 13-17}, author = {Yinglun Zhang and Sonia Moavenzadeh and Jarrar Amjad and Onur Apul and Adrita Barua and Fatih Evrendilek and Torsten Hahmann and Ganga Hettiarachchi and Pascal Hitzler and David Kedrowski and Vasu Kilaru and Prayas Lashkari and Katrina Schweikert and Antony Williams and Hande McGinty} } @article {1005, title = {The KnowWhereGraph: {A} Large-Scale Geo-Knowledge Graph for Interdisciplinary Knowledge Discovery and Geo-Enrichment}, journal = {Transactions in GIS }, volume = {30}, year = {2026}, doi = {10.48550/ARXIV.2502.13874}, url = {https://doi.org/10.48550/arXiv.2502.13874}, author = {Rui Zhu and Cogan Shimizu and Shirly Stephen and Colby K. Fisher and Thomas Thelen and Kitty Currier and Krzysztof Janowicz and Pascal Hitzler and Mark Schildhauer and Wenwen Li and Dean Rehberger and Adrita Barua and Antrea Christou and Ling Cai and Abhilekha Dalal and Anthony D{\textquoteright}Onofrio and Andrew Eells and Mitchell Faulk and Zilong Liu and Gengchen Mai and Mohammad Saeid Mahdavinejad and Bryce D. Mecum and Sanaz Saki Norouzi and Meilin Shi and Yuanyuan Tian and Sizhe Wang and Zhangyu Wang and Joseph Zalewski} } @inbook {995, title = {Reasoning in Large Language Models: RAG and Beyond}, booktitle = {Neuro-Symbolic AI: Integrating Neural Networks and Symbolic Reasoning}, year = {2026}, publisher = {Elsevier}, organization = {Elsevier}, chapter = {5}, abstract = {

This chapter presents a comprehensive overview of contemporary approaches that integrate neural networks and large language models (LLMs) with classical symbolic reasoning. We review the evolution of methods designed to embed logical inference within neural architectures and explore recent advances in prompting strategies, hybrid reasoning frameworks, retrieval-augmented learning, and reinforcement-based reasoning optimization methods. We discuss the symbolic foundations of logical reasoning and then analyze how neural and LLM-based methods have progressively evolved to emulate, extend, and optimize symbolic reasoning across diverse tasks. Finally, we explore emerging neurosymbolic paradigms that unify neural and symbolic reasoning to achieve interpretable, scalable, and generalizable intelligence. Our analysis underscores the growing importance of neurosymbolic AI as a foundational direction for developing reliable and explainable reasoning systems.

}, author = {Adrita Barua and Avishek Das and Moumita Sen Sarma and Pascal Hitzler} } @article {1004, title = {Toward a Neurosymbolic Understanding of Hidden Neuron Activations}, journal = {Neurosymbolic Artificial Intelligence}, volume = {2}, year = {2026}, author = {Abhilekha Dalal and Rushrukh Rayan and Adrita Barua and Samatha Ereshi Akkamahadevi and Avishek Das and Cara Widmer and Eugene Y Vasserman and Kamruzzaman Sarker and Pascal Hitzler} } @conference {992, title = {Complex Ontology Alignment using LLMs: A Case Study}, booktitle = {The 20th International Workshop on Ontology Matching collocated with the 24th International Semantic Web Conference ISWC-2025}, year = {2025}, address = {Nara, Japan}, author = {Adrita Barua and Reza Amini and Sanaz Saki Norouzi and Reihaneh Amini and Pascal Hitzler} } @conference {991, title = {Description Logic Concept Learning using Large Language Models}, booktitle = {19th International Conference on Neurosymbolic Learning and Reasoning}, volume = {284:1{\textendash}19, }, year = {2025}, address = {Santa Cruz, California, USA}, url = {https://openreview.net/forum?id=ebVC7S5VMF}, author = {Adrita Barua and Pascal Hitzler} } @conference {976, title = {Concept Induction using LLMs}, booktitle = {The 23rd International Semantic Web Conference, ISWC 2024}, year = {2024}, publisher = { CEUR-WS.org}, organization = { CEUR-WS.org}, address = {Maryland, USA}, author = {Adrita Barua} } @conference {975, title = {Concept Induction using LLMs: A user experiment for assessment}, booktitle = {Proceedings of the 18th International Conference on Neural-Symbolic Learning and Reasoning, NeSy 2024}, year = {2024}, publisher = {Springer Lecture Notes in Computer Science, to appear.}, organization = {Springer Lecture Notes in Computer Science, to appear.}, address = {Barcelona, Spain, September }, author = {Adrita Barua and Cara Widmer and Pascal Hitzler} } @proceedings {985, title = {DaSeLab at LLMs4OL 2024 Task A: Towards Term Typing in Ontology Learning}, journal = {The 23rd Internatinal Semantic Web Conference, ISWC 2024}, volume = {1st LLMs4OL Challenge @ ISWC 2024}, year = {2024}, publisher = {TIB Open Access Publishing proceedings}, address = { Maryland, USA}, author = {Adrita Barua and Saki Norouzi, Sanaz and Hitzler, Pascal} } @inbook {1001, title = {Ontology Population using LLMs}, booktitle = {Handbook on Neurosymbolic AI and Knowledge Graphs}, year = {2024}, author = {Sanaz Saki Norouzi and Adrita Barua and Antrea Christou and Nikita Gautam and Andrew Eells and Pascal Hitzler and Cogan Shimizu} } @conference {972, title = {On the Value of Labeled Data and Symbolic Methods for Hidden Neuron Activation Analysis}, booktitle = {18th International Conference on Neural-Symbolic Learning and Reasoning, NeSy 2024}, year = {2024}, publisher = {Springer }, organization = {Springer }, abstract = {

We introduce a novel model-agnostic post-hoc Explainable AI method that provides meaningful interpretations for hidden neuron activations in a Convolutional Neural Network. Our approach uses a Wikipedia-derived concept hierarchy with approx. 2 million classes as background knowledge, and deductive reasoning based Concept Induc- tion for explanation generation. Additionally, we explore and compare the capabilities of off-the-shelf pre-trained multimodal-based explainable methods. Our evaluation shows that our neurosymbolic method holds a competitive edge in both quantitative and qualitative aspects.

}, keywords = {CNN, Concept Induction, Explainable AI, LLM}, author = {Abhilekha Dalal and Rushrukh Rayan and Adrita Barua and Eugene Y. Vasserman and Kamruzzaman Sarker and Pascal Hitzler} } @article {905, title = {Explaining Deep Learning Hidden Neuron Activations using Concept Induction}, year = {2023}, abstract = {

One of the current key challenges in Explainable AI is in correctly interpreting activations of hidden neurons. It seems evident that accurate interpretations thereof would provide insights into the question what a deep learning system has internally detected as relevant on the input, thus lifting some of the black box character of deep learning systems.

The state of the art on this front indicates that hidden node activations appear to be interpretable in a way that makes sense to humans, at least in some cases. Yet, systematic automated methods that would be able to first hypothesize an interpretation of hidden neuron activations, and then verify it, are mostly missing.\ 

In this paper, we provide such a method and demonstrate that it provides meaningful interpretations. It is based on using large-scale background knowledge -- a class hierarchy of approx. 2 million classes curated from the Wikipedia Concept Hierarchy -- together with a symbolic reasoning approach called concept induction\ based on description logics that was originally developed for applications in the Semantic Web field.\ 

Our results show that we can automatically attach meaningful labels from the background knowledge to individual neurons in the dense layer of a Convolutional Neural Network through a hypothesis and verification process.

}, author = {Abhilekha Dalal and Md Kamruzzaman Sarker and Adrita Barua and Pascal Hitzler} } @article {906, title = {The KnowWhereGraph Ontology}, year = {2023}, author = {Cogan Shimizu and Shirly Stephen and Kitty Currier and Pascal Hitzler and Rui Zhu and Krzysztof Janowicz and Mark Schildhauer and Mohammad Saeid Mahdavinejad and Abhilekha Dalal and Adrita Barua and Ling Cai and Gengchen Mai and Zhangyu Wang and Yuanyuan Tian and Sanaz Saki Norouzi and Zilong Liu and Meilin Shi and Colby K. Fisher} } @proceedings {958, title = {The KnowWhereGraph Ontology: A Showcase}, journal = {Proceedings of the Joint Ontology Workshops 2023 Episode IX: The Quebec Summer of Ontology co-located with the 13th International Conference on Formal Ontology in Information Systems (FOIS 2023),}, year = {2023}, month = {2023}, publisher = {CEUR Workshop Proceedings 3637, CEUR-WS.org 2023.}, address = {Sherbrooke, Quebec, Canada}, author = {Cogan Shimizu and Shirly Stephen and Rui Zhu and Kitty Currier and Mark Schildhauer and Dean Rehberger and Krzysztof Janowicz and Pascal Hitzler and Colby Fisher and Mohammad Saeid Mahdavinejad and Antrea Christou and Adrita Barua and Abhilehka Dalal and Sanaz Saki Norouzi and Yuanyuan Tian and Ling Cai and Gengchen Mai and Zhangyu Wang and Zilong Liu and Meilin Shi} } @conference {916, title = {The KnowWhereGraph Ontology: A Showcase}, year = {2023}, author = {Cogan Shimizu and Shirly Stephen and Rui Zhu and Kitty Currier and Mark Schildhauer and Dean Rehberger and Pascal Hitzler and Krzysztof Janowicz and Colby K. Fisher and Mohammad Saeid Mahdavinejad and Antrea Christou and Adrita Barua and Abhilekha Dalal and Sanaz Saki Norouzi and Zilong Liu and Meilin Shi and Ling Cai and Gengchen Mai and Zhangyu Wang and Yuanyuan Tian} } @conference {914, title = {KnowWhereGraph-Lite: A Perspective of the KnowWhereGraph}, booktitle = {KGSWC 2023}, year = {2023}, author = {Cogan Shimizu and Shirly Stephen and Antrea Christou and Kitty Currier and Mohammad Saeid Mahdavinejad and Sanaz Saki Norouzi and Abhilekha Dalal and Adrita Barua and Colby K. Fisher and Anthony D{\textquoteright}Onofrio and Thomas Thelen and Krzysztof Janowicz and Dean Rehberger and Mark Schildhauer and Pascal Hitzler} }