Tootfinder

Ontology-Guided Query Expansion for Biomedical Document Retrieval using Large Language Models
Zabir Al Nazi, Vagelis Hristidis, Aaron Lawson McLean, Jannat Ara Meem, Md Taukir Azam Chowdhury
https://arxiv.org/abs/2508.11784

Ontology-Guided Query Expansion for Biomedical Document Retrieval using Large Language Models
Effective Question Answering (QA) on large biomedical document collections requires effective document retrieval techniques. The latter remains a challenging task due to the domain-specific vocabulary and semantic ambiguity in user queries. We propose BMQExpander, a novel ontology-aware query expansion pipeline that combines medical knowledge - definitions and relationships - from the UMLS Metathesaurus with the generative capabilities of large language models (LLMs) to enhance retrieval effect…

TextMine: LLM-Powered Knowledge Extraction for Humanitarian Mine Action
Chenyue Zhou, G\"urkan Solmaz, Flavio Cirillo, Kiril Gashteovski, Jonathan F\"urst
https://arxiv.org/abs/2509.15098

TextMine: LLM-Powered Knowledge Extraction for Humanitarian Mine Action
Humanitarian Mine Action has generated extensive best-practice knowledge, but much remains locked in unstructured reports. We introduce TextMine, an ontology-guided pipeline that uses Large Language Models to extract knowledge triples from HMA texts. TextMine integrates document chunking, domain-aware prompting, triple extraction, and both reference-based and LLM-as-a-Judge evaluation. We also create the first HMA ontology and a curated dataset of real-world demining reports. Experiments show o…

GenOM: Ontology Matching with Description Generation and Large Language Model
Yiping Song, Jiaoyan Chen, Renate A. Schmidt
https://arxiv.org/abs/2508.10703 https://

GenOM: Ontology Matching with Description Generation and Large Language Model
Ontology matching (OM) plays an essential role in enabling semantic interoperability and integration across heterogeneous knowledge sources, particularly in the biomedical domain which contains numerous complex concepts related to diseases and pharmaceuticals. This paper introduces GenOM, a large language model (LLM)-based ontology alignment framework, which enriches the semantic representations of ontology concepts via generating textual definitions, retrieves alignment candidates with an embe…

um
https://en.wikipedia.org/wiki/DARPA_LifeLog

Fuzzy Ontology Embeddings and Visual Query Building for Ontology Exploration
Vladimir Zhurov, John Kausch, Kamran Sedig, Mostafa Milani
https://arxiv.org/abs/2508.08128 https://…

Fuzzy Ontology Embeddings and Visual Query Building for Ontology Exploration
Ontologies play a central role in structuring knowledge across domains, supporting tasks such as reasoning, data integration, and semantic search. However, their large size and complexity, particularly in fields such as biomedicine, computational biology, law, and engineering, make them difficult for non-experts to navigate. Formal query languages such as SPARQL offer expressive access but require users to understand the ontology's structure and syntax. In contrast, visual exploration tools and…

Investigating Language Model Capabilities to Represent and Process Formal Knowledge: A Preliminary Study to Assist Ontology Engineering
Hanna Abi Akl
https://arxiv.org/abs/2509.10249

Investigating Language Model Capabilities to Represent and Process Formal Knowledge: A Preliminary Study to Assist Ontology Engineering
Recent advances in Language Models (LMs) have failed to mask their shortcomings particularly in the domain of reasoning. This limitation impacts several tasks, most notably those involving ontology engineering. As part of a PhD research, we investigate the consequences of incorporating formal methods on the performance of Small Language Models (SLMs) on reasoning tasks. Specifically, we aim to orient our work toward using SLMs to bootstrap ontology construction and set up a series of preliminar…

An LLM Agent-Based Complex Semantic Table Annotation Approach
Yilin Geng, Shujing Wang, Chuan Wang, Keqing He, Yanfei Lv, Ying Wang, Zaiwen Feng, Xiaoying Bai
https://arxiv.org/abs/2508.12868

An LLM Agent-Based Complex Semantic Table Annotation Approach
The Semantic Table Annotation (STA) task, which includes Column Type Annotation (CTA) and Cell Entity Annotation (CEA), maps table contents to ontology entities and plays important roles in various semantic applications. However, complex tables often pose challenges such as semantic loss of column names or cell values, strict ontological hierarchy requirements, homonyms, spelling errors, and abbreviations, which hinder annotation accuracy. To address these issues, this paper proposes an LLM-bas…

Real-Time Health Analytics Using Ontology-Driven Complex Event Processing and LLM Reasoning: A Tuberculosis Case Study
Ritesh Chandra, Sonali Agarwal, Navjot Singh
https://arxiv.org/abs/2510.09646

Real-Time Health Analytics Using Ontology-Driven Complex Event Processing and LLM Reasoning: A Tuberculosis Case Study
Timely detection of critical health conditions remains a major challenge in public health analytics, especially in Big Data environments characterized by high volume, rapid velocity, and diverse variety of clinical data. This study presents an ontology-enabled real-time analytics framework that integrates Complex Event Processing (CEP) and Large Language Models (LLMs) to enable intelligent health event detection and semantic reasoning over heterogeneous, high-velocity health data streams. The a…

PhenoGnet: A Graph-Based Contrastive Learning Framework for Disease Similarity Prediction
Ranga Baminiwatte, Kazi Jewel Rana, Aaron J. Masino
https://arxiv.org/abs/2509.14037 ht…

PhenoGnet: A Graph-Based Contrastive Learning Framework for Disease Similarity Prediction
Understanding disease similarity is critical for advancing diagnostics, drug discovery, and personalized treatment strategies. We present PhenoGnet, a novel graph-based contrastive learning framework designed to predict disease similarity by integrating gene functional interaction networks with the Human Phenotype Ontology (HPO). PhenoGnet comprises two key components: an intra-view model that separately encodes gene and phenotype graphs using Graph Convolutional Networks (GCNs) and Graph Atten…

Development and Evaluation of Adaptive LearningSupport System Based on Ontology of MultipleProgramming Languages
Lalita Na Nongkhai, Jingyun Wang, Takahiko Mendori
https://arxiv.org/abs/2507.19728

Development and Evaluation of Adaptive LearningSupport System Based on Ontology of MultipleProgramming Languages
This paper introduces an ontology-based approach within an adaptive learning support system for computer programming. This system (named ADVENTURE) is designed to deliver personalized programming exercises that are tailored to individual learners' skill levels. ADVENTURE utilizes an ontology, named CONTINUOUS, which encompasses common concepts across multiple programming languages. The system leverages this ontology not only to visualize programming concepts but also to provide hints during pra…

Ontology-Aligned Embeddings for Data-Driven Labour Market Analytics
Heinke Hihn, Dennis A. V. Dittrich, Carl Jeske, Cayo Costa Sobral, Helio Pais, Timm Lochmann
https://arxiv.org/abs/2509.04942

Ontology-Aligned Embeddings for Data-Driven Labour Market Analytics
The limited ability to reason across occupational data from different sources is a long-standing bottleneck for data-driven labour market analytics. Previous research has relied on hand-crafted ontologies that allow such reasoning but are computationally expensive and require careful maintenance by human experts. The rise of language processing machine learning models offers a scalable alternative by learning shared semantic spaces that bridge diverse occupational vocabularies without extensive…

The Human Capital Ontology (Extended Abstract)
Shane Babcock, Maxwell Farrington, John Gugliotti
https://arxiv.org/abs/2507.21175 https://arxiv.org/pdf/250…

The Human Capital Ontology (Extended Abstract)
The Human Capital Ontology (HCO) is an ontology that represents data standards maintained and employed by the Office of Personnel Management (OPM) to represent Human Capital Operations and to classify job positions. The HCO is an extension of the Common Core Ontologies and the upper level Basic Formal Ontology (BFO). HCO provides representation of OPM Nature of Action (NOA) codes that are used to describe human resource personnel actions. HCO also represents Occupational Groups and Job Families…

Interesting way to represent uncertain or vague information into #knowledgegraphs (as e.g. easier integration of LLM/Deep Learning Results into KGs) via "Fuzzy OWL". Paper by Fernando Bobillo & Umberto Straccia: Fuzzy Ontology Representation using OWL 2

Fuzzy Ontology Representation using OWL 2
The need to deal with vague information in Semantic Web languages is rising in importance and, thus, calls for a standard way to represent such information. We may address this issue by either extending current Semantic Web languages to cope with vagueness, or by providing a procedure to represent such information within current standard languages and tools. In this work, we follow the latter approach, by identifying the syntactic differences that a fuzzy ontology language has to cope with, and…

HiRef: Leveraging Hierarchical Ontology and Network Refinement for Robust Medication Recommendation
Yan Ting Chok, Soyon Park, Seungheun Baek, Hajung Kim, Junhyun Lee, Jaewoo Kang
https://arxiv.org/abs/2508.10425

HiRef: Leveraging Hierarchical Ontology and Network Refinement for Robust Medication Recommendation
Medication recommendation is a crucial task for assisting physicians in making timely decisions from longitudinal patient medical records. However, real-world EHR data present significant challenges due to the presence of rarely observed medical entities and incomplete records that may not fully capture the clinical ground truth. While data-driven models trained on longitudinal Electronic Health Records often achieve strong empirical performance, they struggle to generalize under missing or nov…

LLM-based Triplet Extraction for Automated Ontology Generation in Software Engineering Standards
Songhui Yue
https://arxiv.org/abs/2509.00140 https://arxiv…

LLM-based Triplet Extraction for Automated Ontology Generation in Software Engineering Standards
Ontologies have supported knowledge representation and whitebox reasoning for decades; thus, the automated ontology generation (AOG) plays a crucial role in scaling their use. Software engineering standards (SES) consist of long, unstructured text (with high noise) and paragraphs with domain-specific terms. In this setting, relation triple extraction (RTE), together with term extraction, constitutes the first stage toward AOG. This work proposes an open-source large language model (LLM)-assiste…

Idk buy a book about ontology maybe

jewish doctor: fine i'll circumcise ur child just before shabbat
non-denominational evangelical mother: finally the blood moon alignment can reveal the black stone prophecy to the chain breaker league (womens division) as foretold by my amazon cart. long have i hunger for Israeli-grade ontology, flag blue of the sea, white of my skin, one twilight world rendered a landing pad for the physical and symbolic phalluses of Jesus and the AH-64
nonbinary rabbi: oy they

HypoGeneAgent: A Hypothesis Language Agent for Gene-Set Cluster Resolution Selection Using Perturb-seq Datasets
Ying Yuan, Xing-Yue Monica Ge, Aaron Archer Waterman, Tommaso Biancalani, David Richmond, Yogesh Pandit, Avtar Singh, Russell Littman, Jin Liu, Jan-Christian Huetter, Vladimir Ermakov
https://arxiv.org/abs/2509.09740

HypoGeneAgent: A Hypothesis Language Agent for Gene-Set Cluster Resolution Selection Using Perturb-seq Datasets
Large-scale single-cell and Perturb-seq investigations routinely involve clustering cells and subsequently annotating each cluster with Gene-Ontology (GO) terms to elucidate the underlying biological programs. However, both stages, resolution selection and functional annotation, are inherently subjective, relying on heuristics and expert curation. We present HYPOGENEAGENT, a large language model (LLM)-driven framework, transforming cluster annotation into a quantitatively optimizable task. Init…

SSBD Ontology: A Two-Tier Approach for Interoperable Bioimaging Metadata
Yuki Yamagata, Koji Kyoda, Hiroya Itoga, Emi Fujisawa, Shuichi Onami
https://arxiv.org/abs/2508.02084 ht…

SSBD Ontology: A Two-Tier Approach for Interoperable Bioimaging Metadata
Advanced bioimaging technologies have enabled the large-scale acquisition of multidimensional data, yet effective metadata management and interoperability remain significant challenges. To address these issues, we propose a new ontology-driven framework for the Systems Science of Biological Dynamics Database (SSBD) that adopts a two-tier architecture. The core layer provides a class-centric structure referencing existing biomedical ontologies, supporting both SSBD:repository -- which focuses on…

Putting Perspective into OWL [sic]: Complexity-Neutral Standpoint Reasoning for Ontology Languages via Monodic S5 over Counting Two-Variable First-Order Logic (Extended Version with Appendix)
Luc\'ia G\'omez \'Alvarez, Sebastian Rudolph
https://arxiv.org/abs/2508.00653

Putting Perspective into OWL [sic]: Complexity-Neutral Standpoint Reasoning for Ontology Languages via Monodic S5 over Counting Two-Variable First-Order Logic (Extended Version with Appendix)
Standpoint extensions of knowledge representation formalisms have been recently introduced as a means to incorporate multi-perspective modelling and reasoning through modal operators that attribute pieces of knowledge to specific entities or agents. In these extensions, the integration between conceptual modelling and perspective annotations can vary in strength, with monodic standpoint extensions offering a well-balanced approach. They allow for advanced modelling features, such as the express…

QBism and relational interpretation of quantum mechanics from the point of view of a contextual quantum realism (CQR)
Francois-Igor Pris
https://arxiv.org/abs/2510.09237 https:/…

QBism and relational interpretation of quantum mechanics from the point of view of a contextual quantum realism (CQR)
A realist interpretation of quantum mechanics is proposed - Contextual Quantum Realism (CQR) - according to which there exists a categorical distinction between the ideal (theory, observation instrument) and the real (quantum physical systems, properties), and, consequently, quantum ontology is context-sensitive. CQR is compared with QBism and Relational Quantum Mechanics (RQM), both of which also claim to offer realist interpretations. However, both approaches conflate the ideal and the real, …

An Ontology for Unified Modeling of Tasks, Actions, Environments, and Capabilities in Personal Service Robotics
Margherita Martorana, Francesca Urgese, Ilaria Tiddi, Stefan Schlobach
https://arxiv.org/abs/2509.22434

An Ontology for Unified Modeling of Tasks, Actions, Environments, and Capabilities in Personal Service Robotics
Personal service robots are increasingly used in domestic settings to assist older adults and people requiring support. Effective operation involves not only physical interaction but also the ability to interpret dynamic environments, understand tasks, and choose appropriate actions based on context. This requires integrating both hardware components (e.g. sensors, actuators) and software systems capable of reasoning about tasks, environments, and robot capabilities. Frameworks such as the Robo…

Towards Ontology-Based Descriptions of Conversations with Qualitatively-Defined Concepts
Barbara Gendron (LORIA, UL), Ga\"el Guibon (LIPN, LORIA), Mathieu D'aquin (LORIA, UL)
https://arxiv.org/abs/2509.04926

Towards Ontology-Based Descriptions of Conversations with Qualitatively-Defined Concepts
The controllability of Large Language Models (LLMs) when used as conversational agents is a key challenge, particularly to ensure predictable and user-personalized responses. This work proposes an ontology-based approach to formally define conversational features that are typically qualitative in nature. By leveraging a set of linguistic descriptors, we derive quantitative definitions for qualitatively-defined concepts, enabling their integration into an ontology for reasoning and consistency c…

Replaced article(s) found for q-bio.OT. https://arxiv.org/list/q-bio.OT/new
[1/1]:
- NIRS: An Ontology for Non-Invasive Respiratory Support in Acute Care
Md Fantacher Islam, Jarrod Mosier, Vignesh Subbian

Empty Signifier as the Key to a General Ontology of Being

Optimizing Geometry Problem Sets for Skill Development
Michael Bouzinier, Sergey Trifonov
https://arxiv.org/abs/2509.02758 https://arxiv.org/pdf/2509.02758…

Optimizing Geometry Problem Sets for Skill Development
This article describes an ontology and methodology for annotating and organizing Euclidean Geometry problems, developed in the early 1990s and implemented as a software tool. While the majority of this work -- including the ontology and solution graph paradigm -- was completed over thirty years ago, we argue that it has renewed relevance in the context of modern artificial intelligence. In particular, we explore the hypothesis that this established framework can facilitate automated solution va…

Text-to-SQL Task-oriented Dialogue Ontology Construction
Renato Vukovic, Carel van Niekerk, Michael Heck, Benjamin Ruppik, Hsien-Chin Lin, Shutong Feng, Nurul Lubis, Milica Gasic
https://arxiv.org/abs/2507.23358

Text-to-SQL Task-oriented Dialogue Ontology Construction
Large language models (LLMs) are widely used as general-purpose knowledge sources, but they rely on parametric knowledge, limiting explainability and trustworthiness. In task-oriented dialogue (TOD) systems, this separation is explicit, using an external database structured by an explicit ontology to ensure explainability and controllability. However, building such ontologies requires manual labels or supervised training. We introduce TeQoDO: a Text-to-SQL task-oriented Dialogue Ontology constr…

From Ethical Declarations to Provable Independence: An Ontology-Driven Optimal-Transport Framework for Certifiably Fair AI Systems
Sukriti Bhattacharya, Chitro Majumdar
https://arxiv.org/abs/2510.08086

From Ethical Declarations to Provable Independence: An Ontology-Driven Optimal-Transport Framework for Certifiably Fair AI Systems
This paper presents a framework for provably fair AI that overcomes the limits of current bias mitigation methods by systematically removing all sensitive information and its proxies. Using ontology engineering in OWL 2 QL, it formally defines sensitive attributes and infers their proxies through logical reasoning, constructing a sigma algebra G that captures the full structure of biased patterns. Fair representations are then obtained via Delbaen Majumdar optimal transport, which generates var…

A Semantic Framework for Patient Digital Twins in Chronic Care
Amal Elgammal, Bernd J. Kr\"amer, Michael P. Papazoglou, Mira Raheem
https://arxiv.org/abs/2510.09134 https:/…

A Semantic Framework for Patient Digital Twins in Chronic Care
Personalized chronic care requires the integration of multimodal health data to enable precise, adaptive, and preventive decision-making. Yet most current digital twin (DT) applications remain organ-specific or tied to isolated data types, lacking a unified and privacy-preserving foundation. This paper introduces the Patient Medical Digital Twin (PMDT), an ontology-driven in silico patient framework that integrates physiological, psychosocial, behavioral, and genomic information into a coherent…

RELATE: Relation Extraction in Biomedical Abstracts with LLMs and Ontology Constraints
Olawumi Olasunkanmi, Mathew Satursky, Hong Yi, Chris Bizon, Harlin Lee, Stanley Ahalt
https://arxiv.org/abs/2509.19057

Hello from #CoRDI2025 👋 !
🛠️ This year @… is presenting how our Culture Information Portal, the Metadata API, and the Culture Knowledge Graph work together to make #culturalheritage

ODKE : Ontology-Guided Open-Domain Knowledge Extraction with LLMs
Samira Khorshidi, Azadeh Nikfarjam, Suprita Shankar, Yisi Sang, Yash Govind, Hyun Jang, Ali Kasgari, Alexis McClimans, Mohamed Soliman, Vishnu Konda, Ahmed Fakhry, Xiaoguang Qi
https://arxiv.org/abs/2509.04696

ODKE+: Ontology-Guided Open-Domain Knowledge Extraction with LLMs
Knowledge graphs (KGs) are foundational to many AI applications, but maintaining their freshness and completeness remains costly. We present ODKE+, a production-grade system that automatically extracts and ingests millions of open-domain facts from web sources with high precision. ODKE+ combines modular components into a scalable pipeline: (1) the Extraction Initiator detects missing or stale facts, (2) the Evidence Retriever collects supporting documents, (3) hybrid Knowledge Extractors apply …

Enabling Transparent Cyber Threat Intelligence Combining Large Language Models and Domain Ontologies
Luca Cotti, Anisa Rula, Devis Bianchini, Federico Cerutti
https://arxiv.org/abs/2509.00081

Enabling Transparent Cyber Threat Intelligence Combining Large Language Models and Domain Ontologies
Effective Cyber Threat Intelligence (CTI) relies upon accurately structured and semantically enriched information extracted from cybersecurity system logs. However, current methodologies often struggle to identify and interpret malicious events reliably and transparently, particularly in cases involving unstructured or ambiguous log entries. In this work, we propose a novel methodology that combines ontology-driven structured outputs with Large Language Models (LLMs), to build an Artificial Int…

ChemLog: Making MSOL Viable for Ontological Classification and Learning
Simon Fl\"ugel, Martin Glauer, Till Mossakowski, Fabian Neuhaus
https://arxiv.org/abs/2507.13987

ChemLog: Making MSOL Viable for Ontological Classification and Learning
Despite its prevalence, in many domains, OWL is not expressive enough to define ontology classes. In this paper, we present an approach that allows to use monadic second-order formalisations for ontology classification. As a case study, we have applied our approach to 14 peptide-related classes from the chemistry ontology ChEBI. For these classes, a monadic second-order logic formalisation has been developed and applied both to ChEBI as well as to 119 million molecules from the chemistry databa…

Multi-Ontology Integration with Dual-Axis Propagation for Medical Concept Representation
Mohsen Nayebi Kerdabadi, Arya Hadizadeh Moghaddam, Dongjie Wang, Zijun Yao
https://arxiv.org/abs/2508.21320

Multi-Ontology Integration with Dual-Axis Propagation for Medical Concept Representation
Medical ontology graphs map external knowledge to medical codes in electronic health records via structured relationships. By leveraging domain-approved connections (e.g., parent-child), predictive models can generate richer medical concept representations by incorporating contextual information from related concepts. However, existing literature primarily focuses on incorporating domain knowledge from a single ontology system, or from multiple ontology systems (e.g., diseases, drugs, and proce…

Abstraction Principles and the Size of Reality
Bokai Yao
https://arxiv.org/abs/2508.21105 https://arxiv.org/pdf/2508.21105…

Abstraction Principles and the Size of Reality
The Fregean ontology can be naturally interpreted within set theory with urelements, where objects correspond to sets and urelements, and concepts to classes. Consequently, Fregean abstraction principles can be formulated as set-theoretic principles. We investigate how the size of reality-i.e., the number of urelements-interacts with these principles. We show that Basic Law V implies that for some well-ordered cardinal $κ$, there is no set of urelements of size $κ$. Building on recent work by…

Semantic Representation of Processes with Ontology Design Patterns
Ebrahim Norouzi, Sven Hertling, J\"org Waitelonis, Harald Sack
https://arxiv.org/abs/2509.23776 https://

Semantic Representation of Processes with Ontology Design Patterns
The representation of workflows and processes is essential in materials science engineering, where experimental and computational reproducibility depend on structured and semantically coherent process models. Although numerous ontologies have been developed for process modeling, they are often complex and challenging to reuse. Ontology Design Patterns (ODPs) offer modular and reusable modeling solutions to recurring problems; however, these patterns are frequently neither explicitly published n…

if hell is real it'll just be watching this clip over and over fuck you mean "israeli grade ontology" 😭

NIRS: An Ontology for Non-Invasive Respiratory Support in Acute Care
Md Fantacher Islam, Jarrod Mosier, Vignesh Subbian
https://arxiv.org/abs/2507.19992 https://

NIRS: An Ontology for Non-Invasive Respiratory Support in Acute Care
Objective: Develop a Non Invasive Respiratory Support (NIRS) ontology to support knowledge representation in acute care settings. Materials and Methods: We developed the NIRS ontology using Web Ontology Language (OWL) semantics and Protege to organize clinical concepts and relationships. To enable rule-based clinical reasoning beyond hierarchical structures, we added Semantic Web Rule Language (SWRL) rules. We evaluated logical reasoning by adding 17 hypothetical patient clinical scenarios. W…

Presenting the NFDI4Culture Knowledge Graph and the CTO ontology at the 5th Culture Community Plenary at Academy of sciences and literature in Mainz.
https://nfdi4culture.de/resources.html#c866
@…

Digitalizing Uncertain Information
Chris Partridge, Andrew Mitchell, Andreas Cola
https://arxiv.org/abs/2507.21173 https://arxiv.org/pdf/2507.21173

Digitalizing Uncertain Information
The paper sketches some initial results from an ongoing project to develop an ontology-based digital form for representing uncertain information. We frame this work as a journey from lower to higher levels of digital maturity across a technology divide. The paper first sets a baseline by describing the basic challenges any project dealing with digital uncertainty faces. It then describes how the project is facing them. It shows firstly how an extensional ontology (such as the BORO Foundational …

A Scalable and Interoperable Platform for Transforming Building Information with Brick Ontology
Rozita Teymourzadeh, Yuya Nakazawa
https://arxiv.org/abs/2509.16259 https://

A Scalable and Interoperable Platform for Transforming Building Information with Brick Ontology
In the digital twin and building information era, many building automation companies searched for scalable methods to extract and analyze different building data, including Internet of Things (IoT) sensors, actuators, layout sections, zones, etc. The necessity for engineers to continuously manage the entire process for each new building creates scalability challenges. Furthermore, because construction information is sensitive, transferring data on vendor platforms via the cloud creates problems…

Predicting Microbial Ontology and Pathogen Risk from Environmental Metadata with Large Language Models
Hyunwoo Yoo, Gail L. Rosen
https://arxiv.org/abs/2507.21980 https://

Predicting Microbial Ontology and Pathogen Risk from Environmental Metadata with Large Language Models
Traditional machine learning models struggle to generalize in microbiome studies where only metadata is available, especially in small-sample settings or across studies with heterogeneous label formats. In this work, we explore the use of large language models (LLMs) to classify microbial samples into ontology categories such as EMPO 3 and related biological labels, as well as to predict pathogen contamination risk, specifically the presence of E. Coli, using environmental metadata alone. We ev…

AGI as Second Being: The Structural-Generative Ontology of Intelligence
Maijunxian Wang, Ran Ji
https://arxiv.org/abs/2509.02089 https://arxiv.org/pdf/2509…

AGI as Second Being: The Structural-Generative Ontology of Intelligence
Artificial intelligence is often measured by the range of tasks it can perform. Yet wide ability without depth remains only an imitation. This paper proposes a Structural-Generative Ontology of Intelligence: true intelligence exists only when a system can generate new structures, coordinate them into reasons, and sustain its identity over time. These three conditions -- generativity, coordination, and sustaining -- define the depth that underlies real intelligence. Current AI systems, however b…

Improving Knowledge Graph Understanding with Contextual Views
Antrea Christou, Cogan Shimizu
https://arxiv.org/abs/2508.02413 https://arxiv.org/pdf/2508.02…

Improving Knowledge Graph Understanding with Contextual Views
Navigating, visualizing, and discovery in graph data is frequently a difficult prospect. This is especially true for knowledge graphs (KGs), due to high number of possible labeled connections to other data. However, KGs are frequently equipped with an ontology as a schema. That is, it informs how the relationships between data may be constrained. This additional information can be leveraged to improve how (knowledge) graph data can be navigated, visualized, or otherwise utilized in a discover…

A Call to Action for a Secure-by-Design Generative AI Paradigm
Dalal Alharthi, Ivan Roberto Kawaminami Garcia
https://arxiv.org/abs/2510.00451 https://arxi…

A Call to Action for a Secure-by-Design Generative AI Paradigm
Large language models have gained widespread prominence, yet their vulnerability to prompt injection and other adversarial attacks remains a critical concern. This paper argues for a security-by-design AI paradigm that proactively mitigates LLM vulnerabilities while enhancing performance. To achieve this, we introduce PromptShield, an ontology-driven framework that ensures deterministic and secure prompt interactions. It standardizes user inputs through semantic validation, eliminating ambiguit…

Crosslisted article(s) found for cs.SC. https://arxiv.org/list/cs.SC/new
[1/1]:
- Heterogeneous LLM Methods for Ontology Learning (Few-Shot Prompting, Ensemble Typing, and Attenti...
Aleksandra Beliaeva, Temurbek Rahmatullaev

RAGulating Compliance: A Multi-Agent Knowledge Graph for Regulatory QA
Bhavik Agarwal, Hemant Sunil Jomraj, Simone Kaplunov, Jack Krolick, Viktoria Rojkova
https://arxiv.org/abs/2508.09893

RAGulating Compliance: A Multi-Agent Knowledge Graph for Regulatory QA
Regulatory compliance question answering (QA) requires precise, verifiable information, and domain-specific expertise, posing challenges for Large Language Models (LLMs). In this work, we present a novel multi-agent framework that integrates a Knowledge Graph (KG) of Regulatory triplets with Retrieval-Augmented Generation (RAG) to address these demands. First, agents build and maintain an ontology-free KG by extracting subject--predicate--object (SPO) triplets from regulatory documents and syst…

Particles before symmetry
Henrique Gomes
https://arxiv.org/abs/2509.25276 https://arxiv.org/pdf/2509.25276

Particles before symmetry
The standard model of particle physics relies heavily on the idea of symmetry. But recently, a new geometry-first picture has been proposed in which the relevant symmetries do not appear explicitly at the ground level of ontology. Here, I extend the initial correspondence to include alternative -- and more transparent -- accounts of spontaneous symmetry breaking and of the Yukawa coupling: both fundamental for particles to have masses. These reformulations allow us to replace explanations that …

Prompting the Market? A Large-Scale Meta-Analysis of GenAI in Finance NLP (2022-2025)
Paolo Pedinotti, Peter Baumann, Nathan Jessurun, Leslie Barrett, Enrico Santus
https://arxiv.org/abs/2509.09544

Prompting the Market? A Large-Scale Meta-Analysis of GenAI in Finance NLP (2022-2025)
Large Language Models (LLMs) have rapidly reshaped financial NLP, enabling new tasks and driving a proliferation of datasets and diversification of data sources. Yet, this transformation has outpaced traditional surveys. In this paper, we present MetaGraph, a generalizable methodology for extracting knowledge graphs from scientific literature and analyzing them to obtain a structured, queryable view of research trends. We define an ontology for financial NLP research and apply an LLM-based extr…

Crossing Domains without Labels: Distant Supervision for Term Extraction
Elena Senger, Yuri Campbell, Rob van der Goot, Barbara Plank
https://arxiv.org/abs/2510.06838 https://…

Crossing Domains without Labels: Distant Supervision for Term Extraction
Automatic Term Extraction (ATE) is a critical component in downstream NLP tasks such as document tagging, ontology construction and patent analysis. Current state-of-the-art methods require expensive human annotation and struggle with domain transfer, limiting their practical deployment. This highlights the need for more robust, scalable solutions and realistic evaluation settings. To address this, we introduce a comprehensive benchmark spanning seven diverse domains, enabling performance evalu…

Towards an Action-Centric Ontology for Cooking Procedures Using Temporal Graphs
Aarush Kumbhakern, Saransh Kumar Gupta, Lipika Dey, Partha Pratim Das
https://arxiv.org/abs/2509.04159

Towards an Action-Centric Ontology for Cooking Procedures Using Temporal Graphs
Formalizing cooking procedures remains a challenging task due to their inherent complexity and ambiguity. We introduce an extensible domain-specific language for representing recipes as directed action graphs, capturing processes, transfers, environments, concurrency, and compositional structure. Our approach enables precise, modular modeling of complex culinary workflows. Initial manual evaluation on a full English breakfast recipe demonstrates the DSL's expressiveness and suitability for futu…

Animer une base de connaissance: des ontologies aux mod{\`e}les d'I.A. g{\'e}n{\'e}rative
Peter Stockinger (ESCOM, PLIDAM, Inalco, CIS)
https://arxiv.org/abs/2509.01304

Animer une base de connaissance: des ontologies aux mod{è}les d'I.A. g{é}n{é}rative
In a context where the social sciences and humanities are experimenting with non-anthropocentric analytical frames, this article proposes a semiotic (structural) reading of the hybridization between symbolic AI and neural (or sub-symbolic) AI based on a field of application: the design and use of a knowledge base for area studies. We describe the LaCAS ecosystem -- Open Archives in Linguistic and Cultural Studies (thesaurus; RDF/OWL ontology; LOD services; harvesting; expertise; publication), d…

You are an expert in ontologies and kn owledge graphs? You have an interest (or even better expertise) in materials science engineering? Then you should apply! Open PostDoc/Senior Researcher position at FIZ Karlsruhe ISE Research Team in the Knowledge Graph department. More info at https://www.fiz-k…

Cloud Investigation Automation Framework (CIAF): An AI-Driven Approach to Cloud Forensics
Dalal Alharthi, Ivan Roberto Kawaminami Garcia
https://arxiv.org/abs/2510.00452 https:/…

Cloud Investigation Automation Framework (CIAF): An AI-Driven Approach to Cloud Forensics
Large Language Models (LLMs) have gained prominence in domains including cloud security and forensics. Yet cloud forensic investigations still rely on manual analysis, making them time-consuming and error-prone. LLMs can mimic human reasoning, offering a pathway to automating cloud log analysis. To address this, we introduce the Cloud Investigation Automation Framework (CIAF), an ontology-driven framework that systematically investigates cloud forensic logs while improving efficiency and accura…

Crosslisted article(s) found for cs.LO. https://arxiv.org/list/cs.LO/new
[1/1]:
- Heterogeneous LLM Methods for Ontology Learning (Few-Shot Prompting, Ensemble Typing, and Attenti...
Aleksandra Beliaeva, Temurbek Rahmatullaev

Using LLMs for Multilingual Clinical Entity Linking to ICD-10
Sylvia Vassileva, Ivan Koychev, Svetla Boytcheva
https://arxiv.org/abs/2509.04868 https://arx…

Using LLMs for Multilingual Clinical Entity Linking to ICD-10
The linking of clinical entities is a crucial part of extracting structured information from clinical texts. It is the process of assigning a code from a medical ontology or classification to a phrase in the text. The International Classification of Diseases - 10th revision (ICD-10) is an international standard for classifying diseases for statistical and insurance purposes. Automatically assigning the correct ICD-10 code to terms in discharge summaries will simplify the work of healthcare prof…

OntoAligner Meets Knowledge Graph Embedding Aligners
Hamed Babaei Giglou, Jennifer D'Souza, S\"oren Auer, Mahsa Sanaei
https://arxiv.org/abs/2509.26417 https://

OntoAligner Meets Knowledge Graph Embedding Aligners
Ontology Alignment (OA) is essential for enabling semantic interoperability across heterogeneous knowledge systems. While recent advances have focused on large language models (LLMs) for capturing contextual semantics, this work revisits the underexplored potential of Knowledge Graph Embedding (KGE) models, which offer scalable, structure-aware representations well-suited to ontology-based tasks. Despite their effectiveness in link prediction, KGE methods remain underutilized in OA, with most p…

AutoPCR: Automated Phenotype Concept Recognition by Prompting
Yicheng Tao, Yuanhao Huang, Jie Liu
https://arxiv.org/abs/2507.19315 https://arxiv.org/pdf/25…

AutoPCR: Automated Phenotype Concept Recognition by Prompting
Phenotype concept recognition (CR) is a fundamental task in biomedical text mining, enabling applications such as clinical diagnostics and knowledge graph construction. However, existing methods often require ontology-specific training and struggle to generalize across diverse text types and evolving biomedical terminology. We present AutoPCR, a prompt-based phenotype CR method that does not require ontology-specific training. AutoPCR performs CR in three stages: entity extraction using a hybri…

Antirealism in sheep's clothing: Wave function realism and scientific realism
Raoni Arroyo, Jonas R. Becker Arenhart
https://arxiv.org/abs/2507.21615 https://

Antirealism in sheep's clothing: Wave function realism and scientific realism
Scientific realism is the philosophical stance that science tracks truth, in particular in its depiction of the world's ontology. Ontologically, this involves a commitment to the existence of entities posited by our best scientific theories; metaontologically, it includes the claim that the theoretical framework itself is true. In this article, we examine wave function realism as a case study within this broader methodological debate. Wave function realism holds that the wave function, as descr…

Computation of Interpolants for Description Logic Concepts in Hard Cases
Jean Christoph Jung, J\k{e}drzej Ko{\l}odziejski, Frank Wolter
https://arxiv.org/abs/2507.15689

Computation of Interpolants for Description Logic Concepts in Hard Cases
While the computation of Craig interpolants for description logics (DLs) with the Craig Interpolation Property (CIP) is well understood, very little is known about the computation and size of interpolants for DLs without CIP or if one aims at interpolating concepts in a weaker DL than the DL of the input ontology and concepts. In this paper, we provide the first elementary algorithms computing (i) ALC-interpolants between ALC-concepts under ALCH-ontologies and (ii) ALC-interpolants between ALCQ…

Ontology Creation and Management Tools: the Case of Anatomical Connectivity
Natallia Kokash, Bernard de Bono, Tom Gillespie
https://arxiv.org/abs/2509.15780 https://

Ontology Creation and Management Tools: the Case of Anatomical Connectivity
We are developing infrastructure to support researchers in mapping data related to the peripheral nervous system and other physiological systems, with an emphasis on their relevance to the organs under investigation. The nervous system, a complex network of nerves and ganglia, plays a critical role in coordinating and transmitting signals throughout the body. To aid in this, we have created ApiNATOMY, a framework for the topological and semantic representation of multiscale physiological circui…

Crosslisted article(s) found for cs.DL. https://arxiv.org/list/cs.DL/new
[1/1]:
- Ontology Creation and Management Tools: the Case of Anatomical Connectivity
Natallia Kokash, Bernard de Bono, Tom Gillespie

Semantic Bridges Between First Order c-Representations and Cost-Based Semantics: An Initial Perspective
Nicholas Leisegang, Giovanni Casini, Thomas Meyer
https://arxiv.org/abs/2510.00817

Semantic Bridges Between First Order c-Representations and Cost-Based Semantics: An Initial Perspective
Weighted-knowledge bases and cost-based semantics represent a recent formalism introduced by Bienvenu et al. for Ontology Mediated Data Querying in the case where a given knowledge base is inconsistent. This is done by adding a weight to each statement in the knowledge base (KB), and then giving each DL interpretation a cost based on how often it breaks rules in the KB. In this paper we compare this approach with c-representations, a form of non-monotonic reasoning originally introduced by Kern…

Retrieval-Augmented Guardrails for AI-Drafted Patient-Portal Messages: Error Taxonomy Construction and Large-Scale Evaluation
Wenyuan Chen, Fateme Nateghi Haredasht, Kameron C. Black, Francois Grolleau, Emily Alsentzer, Jonathan H. Chen, Stephen P. Ma
https://arxiv.org/abs/2509.22565

Retrieval-Augmented Guardrails for AI-Drafted Patient-Portal Messages: Error Taxonomy Construction and Large-Scale Evaluation
Asynchronous patient-clinician messaging via EHR portals is a growing source of clinician workload, prompting interest in large language models (LLMs) to assist with draft responses. However, LLM outputs may contain clinical inaccuracies, omissions, or tone mismatches, making robust evaluation essential. Our contributions are threefold: (1) we introduce a clinically grounded error ontology comprising 5 domains and 59 granular error codes, developed through inductive coding and expert adjudicati…

Semantic Bridges Between First Order c-Representations and Cost-Based Semantics: An Initial Perspective
Nicholas Leisegang, Giovanni Casini, Thomas Meyer
https://arxiv.org/abs/2510.00817

Semantic Bridges Between First Order c-Representations and Cost-Based Semantics: An Initial Perspective
Weighted-knowledge bases and cost-based semantics represent a recent formalism introduced by Bienvenu et al. for Ontology Mediated Data Querying in the case where a given knowledge base is inconsistent. This is done by adding a weight to each statement in the knowledge base (KB), and then giving each DL interpretation a cost based on how often it breaks rules in the KB. In this paper we compare this approach with c-representations, a form of non-monotonic reasoning originally introduced by Kern…

Disentangling concept semantics via multilingual averaging in Sparse Autoencoders
Cliff O'Reilly, Ernesto Jimenez-Ruiz, Tillman Weyde
https://arxiv.org/abs/2508.14275 https:…

Disentangling concept semantics via multilingual averaging in Sparse Autoencoders
Connecting LLMs with formal knowledge representation and reasoning is a promising approach to address their shortcomings. Embeddings and sparse autoencoders are widely used to represent textual content, but the semantics are entangled with syntactic and language-specific information. We propose a method that isolates concept semantics in Large Langue Models by averaging concept activations derived via Sparse Autoencoders. We create English text representations from OWL ontology classes, transla…

LLM-Based Agents for Competitive Landscape Mapping in Drug Asset Due Diligence
Alisa Vinogradova (Optic Inc), Vlad Vinogradov (Optic Inc), Dmitrii Radkevich (Optic Inc), Ilya Yasny (Optic Inc), Dmitry Kobyzev (Optic Inc), Ivan Izmailov (Optic Inc), Katsiaryna Yanchanka (Optic Inc), Andrey Doronichev (Optic Inc)
https://arxiv.org/abs/2508.1…

LLM-Based Agents for Competitive Landscape Mapping in Drug Asset Due Diligence
In this paper, we describe and benchmark a competitor-discovery component used within an agentic AI system for fast drug asset due diligence. A competitor-discovery AI agent, given an indication, retrieves all drugs comprising the competitive landscape of that indication and extracts canonical attributes for these drugs. The competitor definition is investor-specific, and data is paywalled/licensed, fragmented across registries, ontology-mismatched by indication, alias-heavy for drug names, mul…

LLM-Based Agents for Competitive Landscape Mapping in Drug Asset Due Diligence
Alisa Vinogradova (Optic Inc), Vlad Vinogradov (Optic Inc), Dmitrii Radkevich (Optic Inc), Ilya Yasny (Optic Inc), Dmitry Kobyzev (Optic Inc), Ivan Izmailov (Optic Inc), Katsiaryna Yanchanka (Optic Inc), Andrey Doronichev (Optic Inc)
https://arxiv.org/abs/2508.1…

LLM-Based Agents for Competitive Landscape Mapping in Drug Asset Due Diligence
In this paper, we describe and benchmark a competitor-discovery component used within an agentic AI system for fast drug asset due diligence. A competitor-discovery AI agent, given an indication, retrieves all drugs comprising the competitive landscape of that indication and extracts canonical attributes for these drugs. The competitor definition is investor-specific, and data is paywalled/licensed, fragmented across registries, ontology-mismatched by indication, alias-heavy for drug names, mul…