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 of Semantic Computing addresses computing technologies, and their interactions, that can 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 the 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, and analysis of data and services). Semantic Computing bridges and integrates technologies such as software engineering, user interfaces, natural language processing, artificial intelligence, programming languages, grid computing, and pervasive computing into a unified framework.”

The article defines Semantic Computing as a multi-layered architecture designed to process, integrate, and utilize semantic information. It describes a four-layer architecture: Semantic Analysis, which interprets signals such as pixels and words to extract meaning; Semantic Integration, which unifies content and semantics from diverse sources; Applications, which leverage these contents and semantics to solve problems and may also provide services to other applications; and the Semantic Interface, which enables users to access and manipulate semantic content across sources.

This architecture was later revised into a five-layer model [3], in which 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 provide more comprehensive and interoperable solutions.

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 in computer science: semantic computing and artificial intelligence (AI).

The rapid evolution of computing has led to transformative advancements in both semantic computing and AI. These fields have driven innovation in data processing, knowledge representation, and intelligent decision-making. This book presents a carefully curated selection of articles from IJSC spanning 2007 to 2025, capturing the progression through which the two disciplines have become increasingly interconnected while expanding our understanding of machine intelligence.

The intersection of semantic computing and AI has become increasingly significant, offering sophisticated solutions to complex computational problems and enhancing how 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 these fields.

Recent developments in Semantic Computing include Generative Problem Solving (GPS) [5], which enables the solution of computational problems as well as the generation of new problems. While both GPS and generative AI involve “generation,” GPS extends this concept to include the creation of problems, data, and use cases to facilitate problem solving. In contrast, generative AI focuses on producing novel outputs that mimic or extend patterns learned from textual and multimodal data. GPS approaches problem solving by systematically generating and solving problems step by step, increasing transparency so that users can follow the reasoning process. With a human-in-the-loop, GPS may significantly enhance machine learning efficiency and solution quality.

Despite the impressive natural language understanding capabilities of large language models (LLMs), significant challenges remain. In particular, LLMs have limitations in solving complex problems that require rigorous logical reasoning or computational algorithms. They often lack the ability to explore the full space of possible problems and to ensure solution correctness through explicit, step-by-step reasoning. GPS aims to bridge this gap by enhancing 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)