Information Semantics and Antinomies

Semantics has been a major challenge when applying the process mining (PM) technique to real-time business processes. The several theoretical and practical efforts to bridge the semantic gap has spanned the advanced notion of the semantic-based process mining (SPM). Fundamentally, the SPM devotes its methods to the idea of making use of existing (semantic) technologies to support the analysis of PM techniques. In principle, the semantic-based process mining method is applied through the acquisition and representation of abstract knowledge about the domain processes in question. To this effect, this paper demonstrates how the semantic concepts and process modelling (reasoning) methods are used to improve the outcomes of PM techniques from the syntactic to a more conceptual level. To do this, the study proposes an SPM-based framework that shows to be intelligent with a high level of semantic reasoning aptitudes. Technically, this paper introduces a process mining approach that uses information (semantics) about different activities that can be found in any given process to make inferences and generate rules or patterns through the method for annotation, semantic reasoning, and conceptual assertions. In turn, the method is theoretically applied to enrich the informative values of the resultant models. Also, the study conducts and systematically reviews the current tools and methods that are used to support the outcomes of the process mining as well as evaluates the results of the different methods to determine the levels of impact and its implications for process mining.

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An Information Semantics Approach for Knowledge Management and Interoperability for the Global Earth Observation System of Systems

IEEE Systems Journal ◽

10.1109/jsyst.2008.925975 ◽

2008 ◽

Vol 2 (3) ◽

pp. 358-365 ◽

Cited By ~ 16

Author(s):

S.S. Durbha ◽

R.L. King ◽

N.H. Younan

Keyword(s):

Knowledge Management ◽

System Of Systems ◽

Earth Observation ◽

Observation System ◽

Earth Observation System ◽

Information Semantics

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Information semantics of defuzzification in fuzzy control

Proceedings of 1995 IEEE International Conference on Fuzzy Systems. The International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium ◽

10.1109/fuzzy.1995.409974 ◽

2002 ◽

Cited By ~ 1

Author(s):

A. Ramer ◽

Y. Maeda ◽

L.T. Koczy

Keyword(s):

Fuzzy Control ◽

Information Semantics

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The Nature of Information, Semantics, and Effectiveness for Artificial Intelligence and Cognition

10.31219/osf.io/dehkj ◽

2021 ◽

Author(s):

Kerry Brown

Keyword(s):

Emergent Properties ◽

Eliminative Materialism ◽

Symbolic Reasoning ◽

Theoretical System ◽

Nature Of Information ◽

The Hard Problem ◽

The Mind ◽

Information Semantics ◽

Embedding Methods ◽

Semantic Processes

This manuscript puts forward claims to help address foundational gaps in understanding Cognition and Artificial General Intelligence (AGI), including the nature of Emergence, Semantics, and Information. This includes criteria for assessing true understanding in AI models. How symbolic reasoning conceptualizes phenomena is described. Without a subsymbolic perceptual level to generate concepts, there is no symbol grounding. Grounding requires dynamics outside of its own symbolization. Grounding forms the set of symbols used at the conceptual level. It is claimed that this role explains Semantics. This approach naturally leads to established research on Conceptual Spaces and has implications for Semantic Vector Spaces learned via Neural Embedding methods. It also has implications for Information Theories. A claim is made that Semantic Processes form Shannon-like microstates and macrostates, while Effective Processes constrain Semantic Processes. Unlike existing Semantic Information Theories, Semantic Processes are pre-informational. The claims provide perspective on the Mind. It is natural to conflate percepts with the modified version necessarily created when conceptualizing through explication. The ‘Hard Problem of Consciousness’ is related to this Percept/Concept distinction. Concepts are always subject to Eliminative Materialism. The nonconceptual properties of Percepts cannot be eliminated. Intrinsic are Extrinsic Emergence are distinguished. It is common to assume extrinsic emergent properties are intrinsic to the systems evoking them. This presents a challenge for proving intrinsic emergence in AI. However, criteria are proposed for claiming a theoretical system intrinsically processes information and grounds symbols. By leveraging the functional properties of Grounding, the criteria can be considered for actual systems.

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