The Path to Autonomous Learners
- URL: http://arxiv.org/abs/2211.02403v1
- Date: Fri, 4 Nov 2022 12:18:58 GMT
- Title: The Path to Autonomous Learners
- Authors: Hanna Abi Akl
- Abstract summary: We present a new theoretical approach for enabling domain knowledge acquisition by intelligent systems.
We introduce a hybrid model that starts with minimal input knowledge in the form of an upper ontology of concepts, stores and reasons over this knowledge through a knowledge graph database and learns new information through a Logic Neural Network.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper, we present a new theoretical approach for enabling domain
knowledge acquisition by intelligent systems. We introduce a hybrid model that
starts with minimal input knowledge in the form of an upper ontology of
concepts, stores and reasons over this knowledge through a knowledge graph
database and learns new information through a Logic Neural Network. We study
the behavior of this architecture when handling new data and show that the
final system is capable of enriching its current knowledge as well as extending
it to new domains.
Related papers
- How Do LLMs Acquire New Knowledge? A Knowledge Circuits Perspective on Continual Pre-Training [92.88889953768455]
Large Language Models (LLMs) face a critical gap in understanding how they internalize new knowledge.
We identify computational subgraphs that facilitate knowledge storage and processing.
arXiv Detail & Related papers (2025-02-16T16:55:43Z) - Neural-Symbolic Reasoning over Knowledge Graphs: A Survey from a Query Perspective [55.79507207292647]
Knowledge graph reasoning is pivotal in various domains such as data mining, artificial intelligence, the Web, and social sciences.
The rise of Neural AI marks a significant advancement, merging the robustness of deep learning with the precision of symbolic reasoning.
The advent of large language models (LLMs) has opened new frontiers in knowledge graph reasoning.
arXiv Detail & Related papers (2024-11-30T18:54:08Z) - Aligning Knowledge Graphs Provided by Humans and Generated from Neural Networks in Specific Tasks [5.791414814676125]
This paper develops an innovative method that enables neural networks to generate and utilize knowledge graphs.
Our approach eschews traditional dependencies on or word embedding models, mining concepts from neural networks and directly aligning them with human knowledge.
Experiments show that our method consistently captures network-generated concepts that align closely with human knowledge and can even uncover new, useful concepts not previously identified by humans.
arXiv Detail & Related papers (2024-04-23T20:33:17Z) - Thrill-K Architecture: Towards a Solution to the Problem of Knowledge
Based Understanding [0.9390008801320021]
We introduce a classification of hybrid systems which, based on an analysis of human knowledge and intelligence, combines neural learning with various types of knowledge and knowledge sources.
We present the Thrill-K architecture as a prototypical solution for integrating instantaneous knowledge, standby knowledge and external knowledge sources in a framework capable of inference, learning and intelligent control.
arXiv Detail & Related papers (2023-02-28T20:39:35Z) - Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computing [73.0977635031713]
Neural-symbolic computing (NeSy) has been an active research area of Artificial Intelligence (AI) for many years.
NeSy shows promise of reconciling the advantages of reasoning and interpretability of symbolic representation and robust learning in neural networks.
arXiv Detail & Related papers (2022-10-28T04:38:10Z) - Incorporation of Deep Neural Network & Reinforcement Learning with
Domain Knowledge [0.0]
We present a study of the manners by which Domain information has been incorporated when building models with Neural Networks.
Integrating space data is uniquely important to the development of Knowledge understanding model, as well as other fields that aid in understanding information by utilizing the human-machine interface and Reinforcement Learning.
arXiv Detail & Related papers (2021-07-29T17:29:02Z) - Towards a Universal Continuous Knowledge Base [49.95342223987143]
We propose a method for building a continuous knowledge base that can store knowledge imported from multiple neural networks.
Experiments on text classification show promising results.
We import the knowledge from multiple models to the knowledge base, from which the fused knowledge is exported back to a single model.
arXiv Detail & Related papers (2020-12-25T12:27:44Z) - Neural Networks Enhancement with Logical Knowledge [83.9217787335878]
We propose an extension of KENN for relational data.
The results show that KENN is capable of increasing the performances of the underlying neural network even in the presence relational data.
arXiv Detail & Related papers (2020-09-13T21:12:20Z) - A Heterogeneous Graph with Factual, Temporal and Logical Knowledge for
Question Answering Over Dynamic Contexts [81.4757750425247]
We study question answering over a dynamic textual environment.
We develop a graph neural network over the constructed graph, and train the model in an end-to-end manner.
arXiv Detail & Related papers (2020-04-25T04:53:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.