The field of Semantic Computing was coined by Phillip Sheu in 2007 when he launched the first IEEE International Conference on Semantic Computing (ICSC) [1] and the International Journal of Semantic Computing (IJSC). As stated in the inaugural issue of IJSC [2], “…, the field Semantic Computing addresses the computing technologies, and their interactions, that may be used to extract or process the Contents and Semantics of active services and passive data that are unstructured, semi-structured as well as structured,… Semantic Computing extends Semantic Web (in the narrow sense of ontology-based augmentation of web pages) both in breadth (to include multimedia and services as well as structured data that may or may not be web-based) and depth (to address the access, use, synthesis, integration as well as analysis of data and services). Semantic Computing bridges, and integrates, technologies such as soft-ware engineering, user interface, natural-language processing, artificial intelligence, programming language, grid computing and pervasive computing, among others, into a complete theme.”

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The article defines Semantic Computing as a multi-layered architecture designed to process, integrate, and utilize semantic information. It describes an architecture of four layers: Semantic Analysis, which interprets signals such as pixels and words to extract meaning; Semantic Integration, which unifies contents and semantics from diverse sources; Applications, which leverage these contents and semantics to solve problems and may also provide services to other applications; and Semantic Interface, which enables users to access and manipulate semantic content across sources. The architecture was later revised into a five-layer model [3], where the original Applications layer was divided into Semantic Services — which address specific problems through web search, question answering, content-based multimedia retrieval, and semantic synthesis—and Service Integration, which coordinates multiple semantic services to offer more comprehensive and interoperable solutions. 

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The book Semantic Computing and AI [4] summarizes key milestones in research, addressing fundamental theories, emerging trends, and real-world applications of two closely connected disciplines of computer science: semantic computing and artificial intelligence (AI).

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The rapid evolution of computing has led to transformative advancements in semantic computing and AI simultaneously. Both fields have driven innovation in data processing, knowledge representation, and intelligent decision-making. This book presents a carefully curated selection of articles from the International Journal of Semantic Computing (IJSC), spanning from 2007 to 2025, capturing the journey in which the two disciplines became more and more connected, while expanding our understanding of machine intelligence.

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The intersection of semantic computing and AI has become increasingly significant, offering sophisticated solutions to complex computational problems and enhancing the way machines interpret and interact with human knowledge. This book serves as a valuable resource for researchers, practitioners, and students seeking to understand the evolution and impact of semantic computing and AI.

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The latest development of Semantic Computing includes GPS (Generative Problem Solving) [5] that has the capability of solving computational problems and generating new problems. While GPS and generative AI deal with the act of “generation,” GPS extends the notion of “generative” to include the generation of problems, data, and use cases to facilitate problem solving. In contrast, generative AI targets the creation of novel outputs that mimic or extend existing data patterns extracted from texts and multimodal data. GPS solves new computational problems by systematically creating them and solving them step by step, as well as increasing transparency so that users can follow the reasoning path. With user-in-the-loop, GPS may significantly boost machine’s learning curve and solution quality.

Despite the impressive natural language understanding capabilities of Large Language Models (LLMs), significant challenges remain in their development, particularly their limitations in solving complex problems that require logical reasoning or computational algorithms. LLMs currently lack the capability to explore the full range of possible problems and fail to ensure the correctness of solutions by providing concrete, step-by-step reasoning processes. GPS aims to bridge this gap by empowering LLMs with advanced problem-solving capabilities.

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1. Message from General Chairs". International Conference on Semantic Computing (ICSC 2007). 2007. pp. xiv. doi:10.1109/ICSC.2007.4. ISBN 978-0-7695-2997-4.

2. Sheu, Phillip C.-Y. (2007). "Editorial preface". International Journal of Semantic Computing. 01 (1): 1 9. doi:10.1142/S1793351X07000068. ISSN 1793-351X.

3. Sheu, Phillio C.-Y.; Ramaoorthy, C. V. (2009). "Problems, Solutions, and Semantic Computing". International Journal of Semantic Computing. 03 (3): 383–394. doi:10.1142/s1793351x09000781. ISSN 1793-351X.

4. Florian Schimanke, Mustafa Sert, and Chung-Hsien (eds), Semantic Computing and AI, World Scientiic Publishing, 2025

5. Hsiang-Shun Shih, Chengheng Lyu, Goli Vaisi, Phillip C-Y Sheu, "Generative Problem Solving," Proceedings, International Conference on Semantic Computing (ICSC 2025) (PDF)