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Found 40 papers, 6 papers with code
An Ontology for Representing Curriculum and Learning Material
Educational, learning, and training materials have become extremely commonplace across the Internet.
Building Knowledge Graphs Towards a Global Food Systems Datahub
In this paper, we will present our preliminary results and schemas of our Knowledge Graph and ontologies.
Aligning Generalisation Between Humans and Machines
A crucial yet often overlooked aspect of these interactions is the different ways in which humans and machines generalise.
Accelerating Knowledge Graph and Ontology Engineering with Large Language Models
Large Language Models bear the promise of significant acceleration of key Knowledge Graph and Ontology Engineering tasks, including ontology modeling, extension, modification, population, alignment, as well as entity disambiguation.
Ontology Population using LLMs
Knowledge graphs (KGs) are increasingly utilized for data integration, representation, and visualization.
The S2 Hierarchical Discrete Global Grid as a Nexus for Data Representation, Integration, and Querying Across Geospatial Knowledge Graphs
Geospatial Knowledge Graphs (GeoKGs) have become integral to the growing field of Geospatial Artificial Intelligence.
The KnowWhereGraph Ontology
KnowWhereGraph is one of the largest fully publicly available geospatial knowledge graphs.
ConceptLens: from Pixels to Understanding
ConceptLens is an innovative tool designed to illuminate the intricate workings of deep neural networks (DNNs) by visualizing hidden neuron activations.
Knowledge in Triples for LLMs: Enhancing Table QA Accuracy with Semantic Extraction
Integrating structured knowledge from tabular formats poses significant challenges within natural language processing (NLP), mainly when dealing with complex, semi-structured tables like those found in the FeTaQA dataset.
They Look Like Each Other: Case-based Reasoning for Explainable Depression Detection on Twitter using Large Language Models
Depression is a common mental health issue that requires prompt diagnosis and treatment.
Error-margin Analysis for Hidden Neuron Activation Labels
Understanding how high-level concepts are represented within artificial neural networks is a fundamental challenge in the field of artificial intelligence.
On the Value of Labeled Data and Symbolic Methods for Hidden Neuron Activation Analysis
A major challenge in Explainable AI is in correctly interpreting activations of hidden neurons: accurate interpretations would help answer the question of what a deep learning system internally detects as relevant in the input, demystifying the otherwise black-box nature of deep learning systems.
Concept Induction using LLMs: a user experiment for assessment
To evaluate the output, we compare the concepts generated by the LLM with two other methods: concepts generated by humans and the ECII heuristic concept induction system.
Common Sense Reasoning
Explainable artificial intelligence
+5
Towards Complex Ontology Alignment using Large Language Models
Ontology alignment, a critical process in the Semantic Web for detecting relationships between different ontologies, has traditionally focused on identifying so-called "simple" 1-to-1 relationships through class labels and properties comparison.
Commonsense Ontology Micropatterns
The previously introduced Modular Ontology Modeling methodology (MOMo) attempts to mimic the human analogical process by using modular patterns to assemble more complex concepts.
On the Psychology of GPT-4: Moderately anxious, slightly masculine, honest, and humble
We subject GPT-4 to a number of rigorous psychometric tests and analyze the results.
Conversational Ontology Alignment with ChatGPT
This study evaluates the applicability and efficiency of ChatGPT for ontology alignment using a naive approach.
Understanding CNN Hidden Neuron Activations Using Structured Background Knowledge and Deductive Reasoning
A major challenge in Explainable AI is in correctly interpreting activations of hidden neurons: accurate interpretations would provide insights into the question of what a deep learning system has internally detected as relevant on the input, demystifying the otherwise black-box character of deep learning systems.
A Modular Ontology for MODS -- Metadata Object Description Schema
The Metadata Object Description Schema (MODS) was developed to describe bibliographic concepts and metadata and is maintained by the Library of Congress.
An Ontology Design Pattern for Role-Dependent Names
We present an ontology design pattern for modeling Names as part of Roles, to capture scenarios where an Agent performs different Roles using different Names associated with the different Roles.
Explaining Deep Learning Hidden Neuron Activations using Concept Induction
One of the current key challenges in Explainable AI is in correctly interpreting activations of hidden neurons.
Towards Human-Compatible XAI: Explaining Data Differentials with Concept Induction over Background Knowledge
Concept induction, which is based on formal logical reasoning over description logics, has been used in ontology engineering in order to create ontology (TBox) axioms from the base data (ABox) graph.
Ontology Design Facilitating Wikibase Integration -- and a Worked Example for Historical Data
Wikibase -- which is the software underlying Wikidata -- is a powerful platform for knowledge graph creation and management.
On the Capabilities of Pointer Networks for Deep Deductive Reasoning
The importance of building neural networks that can learn to reason has been well recognized in the neuro-symbolic community.
Neuro-Symbolic Artificial Intelligence: Current Trends
Neuro-Symbolic Artificial Intelligence -- the combination of symbolic methods with methods that are based on artificial neural networks -- has a long-standing history.
Neural Fuzzy Extractors: A Secure Way to Use Artificial Neural Networks for Biometric User Authentication
Powered by new advances in sensor development and artificial intelligence, the decreasing cost of computation, and the pervasiveness of handheld computation devices, biometric user authentication (and identification) is rapidly becoming ubiquitous.
Completion Reasoning Emulation for the Description Logic EL+
We present a new approach to integrating deep learning with knowledge-based systems that we believe shows promise.
MODL: A Modular Ontology Design Library
Pattern-based, modular ontologies have several beneficial properties that lend themselves to FAIR data practices, especially as it pertains to Interoperability and Reusability.
Efficient Concept Induction for Description Logics
Concept Induction refers to the problem of creating complex Description Logic class descriptions (i. e., TBox axioms) from instance examples (i. e., ABox data).
Reasoning over RDF Knowledge Bases using Deep Learning
Semantic Web knowledge representation standards, and in particular RDF and OWL, often come endowed with a formal semantics which is considered to be of fundamental importance for the field.
Modeling OWL with Rules: The ROWL Protege Plugin
In our experience, some ontology users find it much easier to convey logical statements using rules rather than OWL (or description logic) axioms.
OWLAx: A Protege Plugin to Support Ontology Axiomatization through Diagramming
Once the conceptual overview, in terms of a somewhat informal class diagram, has been designed in the course of engineering an ontology, the process of adding many of the appropriate logical axioms is mostly a routine task.
Rule-based OWL Modeling with ROWLTab Protege Plugin
It has been argued that it is much easier to convey logical statements using rules rather than OWL (or description logic (DL)) axioms.
A Tutorial on Modular Ontology Modeling with Ontology Design Patterns: The Cooking Recipes Ontology
We provide a detailed example for modular ontology modeling based on ontology design patterns.
A Practical Acyclicity Notion for Query Answering over Horn-SRIQ Ontologies
Conjunctive query answering over expressive Horn Description Logic ontologies is a relevant and challenging problem which, in some cases, can be addressed by application of the chase algorithm.
Relating Input Concepts to Convolutional Neural Network Decisions
Many current methods to interpret convolutional neural networks (CNNs) use visualization techniques and words to highlight concepts of the input seemingly relevant to a CNN's decision.
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation
Recent studies in cognitive science, artificial intelligence, and psychology have produced a number of cognitive models of reasoning, learning, and language that are underpinned by computation.
On the Ontological Modeling of Trees
Trees -- i. e., the type of data structure known under this name -- are central to many aspects of knowledge organization.
Explaining Trained Neural Networks with Semantic Web Technologies: First Steps
The ever increasing prevalence of publicly available structured data on the World Wide Web enables new applications in a variety of domains.
Dimensions of Neural-symbolic Integration - A Structured Survey
Research on integrated neural-symbolic systems has made significant progress in the recent past.
