Comprehensible Artificial Intelligence on Knowledge Graphs: A survey
- URL: http://arxiv.org/abs/2404.03499v1
- Date: Thu, 4 Apr 2024 14:57:32 GMT
- Title: Comprehensible Artificial Intelligence on Knowledge Graphs: A survey
- Authors: Simon Schramm, Christoph Wehner, Ute Schmid,
- Abstract summary: This survey gives a short history to Comprehensible Artificial Intelligence on Knowledge Graphs.
We argue that the concept Explainable Artificial Intelligence is overloaded and overlapping with Interpretable Machine Learning.
This leads to the introduction of a novel taxonomy for Comprehensible Artificial Intelligence on Knowledge Graphs.
- Score: 2.3408308015481665
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Artificial Intelligence applications gradually move outside the safe walls of research labs and invade our daily lives. This is also true for Machine Learning methods on Knowledge Graphs, which has led to a steady increase in their application since the beginning of the 21st century. However, in many applications, users require an explanation of the Artificial Intelligences decision. This led to increased demand for Comprehensible Artificial Intelligence. Knowledge Graphs epitomize fertile soil for Comprehensible Artificial Intelligence, due to their ability to display connected data, i.e. knowledge, in a human- as well as machine-readable way. This survey gives a short history to Comprehensible Artificial Intelligence on Knowledge Graphs. Furthermore, we contribute by arguing that the concept Explainable Artificial Intelligence is overloaded and overlapping with Interpretable Machine Learning. By introducing the parent concept Comprehensible Artificial Intelligence, we provide a clear-cut distinction of both concepts while accounting for their similarities. Thus, we provide in this survey a case for Comprehensible Artificial Intelligence on Knowledge Graphs consisting of Interpretable Machine Learning on Knowledge Graphs and Explainable Artificial Intelligence on Knowledge Graphs. This leads to the introduction of a novel taxonomy for Comprehensible Artificial Intelligence on Knowledge Graphs. In addition, a comprehensive overview of the research on Comprehensible Artificial Intelligence on Knowledge Graphs is presented and put into the context of the taxonomy. Finally, research gaps in the field of Comprehensible Artificial Intelligence on Knowledge Graphs are identified for future research.
Related papers
- Automated Explanation Selection for Scientific Discovery [0.0]
We propose a cycle of scientific discovery that combines machine learning with automated reasoning for the generation and the selection of explanations.
We present a taxonomy of explanation selection problems that draws on insights from sociology and cognitive science.
arXiv Detail & Related papers (2024-07-24T17:41:32Z) - Visual Knowledge in the Big Model Era: Retrospect and Prospect [63.282425615863]
Visual knowledge is a new form of knowledge representation that can encapsulate visual concepts and their relations in a succinct, comprehensive, and interpretable manner.
As the knowledge about the visual world has been identified as an indispensable component of human cognition and intelligence, visual knowledge is poised to have a pivotal role in establishing machine intelligence.
arXiv Detail & Related papers (2024-04-05T07:31:24Z) - AI-as-exploration: Navigating intelligence space [0.05657375260432172]
I articulate the contours of a rather neglected but central scientific role that AI has to play.
The basic thrust of AI-as-exploration is that of creating and studying systems that can reveal candidate building blocks of intelligence.
arXiv Detail & Related papers (2024-01-15T21:06:20Z) - AI for Mathematics: A Cognitive Science Perspective [86.02346372284292]
Mathematics is one of the most powerful conceptual systems developed and used by the human species.
Rapid progress in AI, particularly propelled by advances in large language models (LLMs), has sparked renewed, widespread interest in building such systems.
arXiv Detail & Related papers (2023-10-19T02:00:31Z) - A Review on Objective-Driven Artificial Intelligence [0.0]
Humans have an innate ability to understand context, nuances, and subtle cues in communication.
Humans possess a vast repository of common-sense knowledge that helps us make logical inferences and predictions about the world.
Machines lack this innate understanding and often struggle with making sense of situations that humans find trivial.
arXiv Detail & Related papers (2023-08-20T02:07:42Z) - Learning by Applying: A General Framework for Mathematical Reasoning via
Enhancing Explicit Knowledge Learning [47.96987739801807]
We propose a framework to enhance existing models (backbones) in a principled way by explicit knowledge learning.
In LeAp, we perform knowledge learning in a novel problem-knowledge-expression paradigm.
We show that LeAp improves all backbones' performances, learns accurate knowledge, and achieves a more interpretable reasoning process.
arXiv Detail & Related papers (2023-02-11T15:15:41Z) - Inductive Biases for Deep Learning of Higher-Level Cognition [108.89281493851358]
A fascinating hypothesis is that human and animal intelligence could be explained by a few principles.
This work considers a larger list, focusing on those which concern mostly higher-level and sequential conscious processing.
The objective of clarifying these particular principles is that they could potentially help us build AI systems benefiting from humans' abilities.
arXiv Detail & Related papers (2020-11-30T18:29:25Z) - Empowering Things with Intelligence: A Survey of the Progress,
Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
We show how AI can empower the IoT to make it faster, smarter, greener, and safer.
First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving.
Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
arXiv Detail & Related papers (2020-11-17T13:14:28Z) - Computing Machinery and Knowledge [0.0]
The paper argues that it is possible for an AI agent to know and examines this from both current state-of-the-art in artificial intelligence as well as from the perspective of what the future AI development might bring in terms of superintelligent AI agents.
arXiv Detail & Related papers (2020-10-31T09:27:53Z) - Intelligence Primer [0.0]
Intelligence is a fundamental part of all living things, as well as the foundation for Artificial Intelligence.
In this primer we explore the ideas associated with intelligence and, by doing so, understand the implications and constraints.
We call this a Life, the Universe, and Everything primer, after the famous science fiction book by Douglas Adams.
arXiv Detail & Related papers (2020-08-13T15:47:04Z) - Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of
AI/AGI Using Multiple Intelligences and Learning Styles [95.58955174499371]
We describe various aspects of multiple human intelligences and learning styles, which may impact on a variety of AI problem domains.
Future AI systems will be able not only to communicate with human users and each other, but also to efficiently exchange knowledge and wisdom.
arXiv Detail & Related papers (2020-08-07T21:00:13Z)
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.