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Information
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What Is Information? (2020)
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What Is Information? (2020)

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Theory and Methodology".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 7177

Special Issue Editor

Dr. Mark Burgin

E-Mail Website
Guest Editor
Department of Mathematics, University of California, Box 951555, Los Angeles, CA 90095, USA
Interests: information theory; communication theory and technology; algorithmic information; information science; theory of knowledge; information processing systems and technology; theory of algorithms, automata and computation; complexity; knowledge management; theory of technology; cognition and epistemology; software engineering; schema theory
Special Issues, Collections and Topics in MDPI journals

Published Papers (2 papers)

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22 pages, 1518 KiB  
Article
Numerical Markov Logic Network: A Scalable Probabilistic Framework for Hybrid Knowledge Inference
by Ping Zhong, Zhanhuai Li, Qun Chen, Boyi Hou and Murtadha Ahmed
Information 2021, 12(3), 124; https://doi.org/10.3390/info12030124 - 15 Mar 2021
Cited by 2 | Viewed by 2100
Abstract
In recent years, the Markov Logic Network (MLN) has emerged as a powerful tool for knowledge-based inference due to its ability to combine first-order logic inference and probabilistic reasoning. Unfortunately, current MLN solutions cannot efficiently support knowledge inference involving arithmetic expressions, which is [...] Read more.
In recent years, the Markov Logic Network (MLN) has emerged as a powerful tool for knowledge-based inference due to its ability to combine first-order logic inference and probabilistic reasoning. Unfortunately, current MLN solutions cannot efficiently support knowledge inference involving arithmetic expressions, which is required to model the interaction between logic relations and numerical values in many real applications. In this paper, we propose a probabilistic inference framework, called the Numerical Markov Logic Network (NMLN), to enable efficient inference of hybrid knowledge involving both logic and arithmetic expressions. We first introduce the hybrid knowledge rules, then define an inference model, and finally, present a technique based on convex optimization for efficient inference. Built on decomposable exp-loss function, the proposed inference model can process hybrid knowledge rules more effectively and efficiently than the existing MLN approaches. Finally, we empirically evaluate the performance of the proposed approach on real data. Our experiments show that compared to the state-of-the-art MLN solution, it can achieve better prediction accuracy while significantly reducing inference time. Full article
(This article belongs to the Special Issue What Is Information? (2020))
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22 pages, 1458 KiB  
Article
A Dialogue Concerning the Essence and Role of Information in the World System
by Mark Burgin and Jaime F. Cárdenas-García
Information 2020, 11(9), 406; https://doi.org/10.3390/info11090406 - 21 Aug 2020
Cited by 7 | Viewed by 4207
Abstract
The goal of this paper is to represent two approaches to the phenomenon of information, explicating its nature and essence. In this context, Mark Burgin demonstrates how the general theory of information (GTI) describes and elucidates the phenomenon of information by explaining the [...] Read more.
The goal of this paper is to represent two approaches to the phenomenon of information, explicating its nature and essence. In this context, Mark Burgin demonstrates how the general theory of information (GTI) describes and elucidates the phenomenon of information by explaining the axiomatic foundations for information studies and presenting the comprising mathematical theory of information. The perspective promoted by Jaime F. Cárdenas-García is based on Gregory Bateson’s description of information as “difference which makes a difference” and involves the process of info-autopoiesis as a sensory commensurable, self-referential feedback process. Full article
(This article belongs to the Special Issue What Is Information? (2020))
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