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- ArticleSeptember 2024
Ethical Reward Machine
AbstractThe Ethical Reward Machine investigates reward design involving ethical constraints with reinforcement learning. Designed to promote good behaviour across specific domains, such as simulated driving and search-and-rescue scenarios, the Ethical ...
- ArticleSeptember 2024
Conceptual Knowledge Modelling for Human-AI Teaming in Data-Frugal Industrial Environments
AbstractWhen AI interacts with humans in complex environments, such as aerospace manufacturing, safety of operation is of paramount importance. Trustworthiness of AI needs to be ensured through, among other things, explainability of its behaviour and ...
- research-articleJune 2024
Querying Knowledge Graphs in Greek Language
PETRA '24: Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive EnvironmentsPages 27–33https://doi.org/10.1145/3652037.3652072We present a method for querying knowledge graphs through natural language, emphasizing its application to the Greek language. It integrates NLP techniques with the capabilities of graph databases to enable seamless interaction with knowledge graphs ...
- research-articleJuly 2024
Non-deterministic approximation fixpoint theory and its application in disjunctive logic programming
AbstractApproximation fixpoint theory (AFT) is an abstract and general algebraic framework for studying the semantics of nonmonotonic logics. It provides a unifying study of the semantics of different formalisms for nonmonotonic reasoning, such as logic ...
- ArticleJuly 2024
Rough Algebraic Semantics of Concepts in a Distributed Cognition Perspective
AbstractUp-directed rough sets are introduced and studied by the present author in earlier papers. This is extended by her in two different granular directions in this research, with a surprising algebraic semantics. The granules are based on ideas of ...
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- ArticleApril 2024
From Knowledge Representation to Knowledge Organization and Back
AbstractKnowledge Representation (KR) and facet-analytical Knowledge Organization (KO) have been the two most prominent methodologies of data and knowledge modelling in the Artificial Intelligence community and the Information Science community, ...
- ArticleApril 2024
On Naive Labellings – Realizability, Construction and Patterns of Redundancy
Foundations of Information and Knowledge SystemsPages 125–143https://doi.org/10.1007/978-3-031-56940-1_7AbstractThis paper is engaged with realizability in the realm of abstract argumentation. In particular, we deal with naive labellings forming the basis for a well-established family of semantics, namely the naivity-based semantics. Consequently, ...
- extended-abstractJanuary 2024
- research-articleJuly 2024
NeSyKHG: Neuro-Symbolic Knowledge Hypergraphs
Procedia Computer Science (PROCS), Volume 235, Issue CPages 1278–1288https://doi.org/10.1016/j.procs.2024.04.121AbstractIn the rapidly evolving landscape of artificial intelligence, there is an escalating demand for enhanced reasoning capabilities and robust representation of higher-order relationships. Despite their prowess in pattern identification and ...
- short-paperDecember 2023
CourtDocs Ontology: Towards a Data Model for Representation of Historical Court Proceedings
K-CAP '23: Proceedings of the 12th Knowledge Capture Conference 2023Pages 175–179https://doi.org/10.1145/3587259.3627562For several decades researchers have studied legal documents for insights into the evolution of legal norms and strategies, in their social and cultural context. Analysing these documents and the associated legislative sessions, trials and court cases ...
- research-articleDecember 2023
A Neuro-Symbolic Approach for Anomaly Detection and Complex Fault Diagnosis Exemplified in the Automotive Domain
K-CAP '23: Proceedings of the 12th Knowledge Capture Conference 2023Pages 35–43https://doi.org/10.1145/3587259.3627546This paper presents an iterative, hybrid neuro-symbolic approach for anomaly detection and complex fault diagnosis, enabling knowledge-based (symbolic) methods to complement (neural) machine learning methods and vice versa. We demonstrate an ...
- ArticleNovember 2023
Reports, Observations, and Belief Change
AbstractWe consider belief change in a context where information comes from reports, and the reporting agents may not be honest. In order to capture this process, we introduce an extended class of epistemic states that includes a history of past reports ...
- ArticleNovember 2023
Comparison of Knowledge Graph Representations for Consumer Scenarios
AbstractKnowledge graphs have been widely adopted across organizations and research domains, fueling applications that span interactive browsing to large-scale analysis and data science. One design decision in knowledge graph deployment is choosing a ...
- ArticleApril 2024
TSKE: Two-Stream Knowledge Embedding for Cyberspace Security
AbstractKnowledge representation models have been extensively studied and adopted in many areas such as search, recommendation, etc. However, due to the highly spatio-temporal relevant characteristics of cyberspace security and the dynamic variability of ...
- ArticleSeptember 2023
Sequence-Based Modeling for Temporal Knowledge Graph Link Prediction
Artificial Neural Networks and Machine Learning – ICANN 2023Pages 550–562https://doi.org/10.1007/978-3-031-44216-2_45AbstractCurrently, the majority of research in temporal knowledge graph link prediction focuses on completing missing facts. Nevertheless, the utilization of knowledge graphs to forecast future facts has garnered significant scholarly attention. The ...
- ArticleSeptember 2023
Towards Systematic Treatment of Partial Functions in Knowledge Representation
AbstractPartial functions are ubiquitous in Knowledge Representation applications, ranging from practical, e.g., business applications, to more abstract, e.g., mathematical and programming applications. Expressing propositions about partial functions may ...
- ArticleOctober 2023
Semantic Role Assisted Natural Language Rule Formalization for Intelligent Vehicle
AbstractThis paper proposes a novel pipeline to translate natural language rules and instructions for intelligent vehicles into temporal logic. The pipeline uses semantic role labeling (SRL), soft rule-based selection restrictions, and large language ...
- ArticleSeptember 2023
Jointly Learning Structure-Augmented Semantic Representation and Logical Rules for Knowledge Graph Completion
AbstractKnowledge Graph Complementation (KGC) aims to predict the missing triples in incomplete knowledge graphs (KGs). However, existing approaches rely either on structural features, semantic features or logical rules. There is not yet a unified way to ...
- extended-abstractSeptember 2023
Fifth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS)
- Vito Walter Anelli,
- Pierpaolo Basile,
- Gerard De Melo,
- Francesco M Donini,
- Antonio Ferrara,
- Cataldo Musto,
- Fedelucio Narducci,
- Azzurra Ragone,
- Markus Zanker
RecSys '23: Proceedings of the 17th ACM Conference on Recommender SystemsPages 1259–1262https://doi.org/10.1145/3604915.3608759Recommender systems have become ubiquitous in daily life, but their limitations in interacting with human users have become evident. Deep learning approaches have led to the development of data-driven algorithms that identify connections between users ...
- ArticleSeptember 2023
Knowledge Graph Enabled Open-Domain Conversational Question Answering
AbstractWith the advent of natural language enabled applications, there has been a growing appetite for conversational question answering systems. This demand is being largely satisfied with the help of such powerful language models as Open AI’s GPT ...