Continual Learning of Knowledge Graph Embeddings
- URL: http://arxiv.org/abs/2101.05850v1
- Date: Thu, 14 Jan 2021 19:59:57 GMT
- Title: Continual Learning of Knowledge Graph Embeddings
- Authors: Angel Daruna, Mehul Gupta, Mohan Sridharan, and Sonia Chernova
- Abstract summary: In recent years, there has been a resurgence in methods that use distributed (neural) representations to represent and reason about semantic knowledge for robotics applications.
Our work relaxes the static assumptions of these representations to tackle the incremental knowledge graph embedding problem.
We provide insights about trade-offs for practitioners to match a semantics-driven robotics application to a suitable continual knowledge graph embedding method.
- Score: 17.686595033265558
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In recent years, there has been a resurgence in methods that use distributed
(neural) representations to represent and reason about semantic knowledge for
robotics applications. However, while robots often observe previously unknown
concepts, these representations typically assume that all concepts are known a
priori, and incorporating new information requires all concepts to be learned
afresh. Our work relaxes the static assumptions of these representations to
tackle the incremental knowledge graph embedding problem by leveraging
principles of a range of continual learning methods. Through an experimental
evaluation with several knowledge graphs and embedding representations, we
provide insights about trade-offs for practitioners to match a semantics-driven
robotics application to a suitable continual knowledge graph embedding method.
Related papers
- Advancing Personalized Learning Analysis via an Innovative Domain Knowledge Informed Attention-based Knowledge Tracing Method [0.0]
We propose an innovative attention-based method by effectively incorporating the domain knowledge of knowledge concept routes in the given curriculum.
We leverage XES3G5M dataset to evaluate and compare the performance of our proposed method to the seven State-of-the-art deep learning models.
arXiv Detail & Related papers (2025-01-09T22:41:50Z) - 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) - Towards Automated Knowledge Integration From Human-Interpretable Representations [55.2480439325792]
We introduce and motivate theoretically the principles of informed meta-learning enabling automated and controllable inductive bias selection.
We empirically demonstrate the potential benefits and limitations of informed meta-learning in improving data efficiency and generalisation.
arXiv Detail & Related papers (2024-02-25T15:08:37Z) - Computing Rule-Based Explanations of Machine Learning Classifiers using
Knowledge Graphs [62.997667081978825]
We use knowledge graphs as the underlying framework providing the terminology for representing explanations for the operation of a machine learning classifier.
In particular, we introduce a novel method for extracting and representing black-box explanations of its operation, in the form of first-order logic rules expressed in the terminology of the knowledge graph.
arXiv Detail & Related papers (2022-02-08T16:21:49Z) - Recent Advances of Continual Learning in Computer Vision: An Overview [16.451358332033532]
Continual learning is similar to the human learning process with the ability of learning, fusing, and accumulating new knowledge coming at different time steps.
We present a comprehensive review of the recent progress of continual learning in computer vision.
arXiv Detail & Related papers (2021-09-23T13:30:18Z) - Dynamic Knowledge Graphs as Semantic Memory Model for Industrial Robots [0.7863638253070437]
We present a model for semantic memory that allows machines to collect information and experiences to become more proficient with time.
After a semantic analysis of the data, information is stored in a knowledge graph which is used to comprehend instructions, expressed in natural language, and execute the required tasks in a deterministic manner.
arXiv Detail & Related papers (2021-01-04T17:15:30Z) - All About Knowledge Graphs for Actions [82.39684757372075]
We propose a better understanding of knowledge graphs (KGs) that can be utilized for zero-shot and few-shot action recognition.
We study three different construction mechanisms for KGs: action embeddings, action-object embeddings, visual embeddings.
We present extensive analysis of the impact of different KGs on different experimental setups.
arXiv Detail & Related papers (2020-08-28T01:44:01Z) - 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) - Neuro-symbolic Architectures for Context Understanding [59.899606495602406]
We propose the use of hybrid AI methodology as a framework for combining the strengths of data-driven and knowledge-driven approaches.
Specifically, we inherit the concept of neuro-symbolism as a way of using knowledge-bases to guide the learning progress of deep neural networks.
arXiv Detail & Related papers (2020-03-09T15:04:07Z) - A Survey of Knowledge Representation in Service Robotics [10.220366465518262]
We focus on knowledge representations and how knowledge is typically gathered, represented, and reproduced to solve problems.
In accordance with the definition of knowledge representations, we discuss the key distinction between such representations and useful learning models.
We discuss key principles that should be considered when designing an effective knowledge representation.
arXiv Detail & Related papers (2018-07-05T22:18:08Z)
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.