The researches on the soft computing techniques for knowledge management and web applications have been spread widely to be critical areas, such as intelligent techniques in soft computing, web intelligence, ontology, intelligent agent, real-world automatic control robots, and so on. This special issue contains seven papers that consider different aspects of research on soft computing for knowledge management and web applications. These papers cover clustering analysis, financial forecast, Chinese textual entailment, preventive care system with learning communication robots, linguistic-based entity matching in linked data, ontologies in engineering, and topic detection.

The first paper, “Particle swarm optimization algorithm with environmental factors for clustering analysis,” by Song et al., presents an environment factor-inspired particle swarm optimization algorithm (EPSO) for clustering analysis. The experimental results indicate that EPSO performs better than state-of-the-art clustering algorithms in most cases. The second paper, “Heuristic procedures for improving the predictability of a genetic programming financial forecasting algorithm” of Kampouridis and Otero, presents an evolutionary dynamic data investment evaluator (EDDIE) to improve the predictability of a genetic programming financial forecasting algorithm by incorporating heuristics in the search. Results show that the introduction of heuristics allows the proposed method to outperform previous algorithms. The third paper, “Exploring lexical, syntactic, and semantic features for Chinese textual entailment in NTCIR RITE evaluation tasks,” by Huang and Liu, computes linguistic information at the lexical, syntactic levels for recognizing inference in text (RITE) tasks for both traditional and simplified Chinese in NTCIR-9 and NTCIR-10. The authors use techniques, for example, syntactic parsing and features like counts of common words to judge the entailment relationships of two statements. The extended work shows interesting results and should encourage further discussions.

The fourth paper, “An empirical study on evaluating basic characteristics and adaptability to users of a preventive care system with learning communication robots (PreCareCom),” by Kitakoshi et al., proposed a preventive care system to encourage a sense of familiarity with the robots (agents) for elderly users based on the concept of human–agent interaction. Additionally, the reinforcement learning methods are used to adopt the proposed PrevCareCom to its users in terms of familiarity, quality, and quantity of the preventive care exercises. Several experiments show that the PrevCareCom could provide appropriate exercise loads for the uses’ care prevention. The fifth paper, “Feature-driven linguistic-based entity matching in liked data with application in pharmacy,” by Zadeh et al., used fuzzy-based technologies to address the problem of processing web information. In particular, the authors adopt a linguistic representation model to determine alternatives that match a given reference with the highest possible degree, and they also applied their approach in the domain of pharmacy. The obtained results show the advantage of using the proposed method.

The sixth paper, “Ontologies in engineering: the OntoDB/OntoQL platform,” by Ait-Ameur et al., developed a specialized standard ontology language named PLIB associated with the OntoDB/OntoQL platform to manage ontological engineering data within a database. The authors use several examples to show the effectiveness of the proposed ontology language. The seventh paper, “A semantic frame-based intelligent agent for topic detection,” by Chang et al., proposes a semantic frame-based topic detection (SFTD) to effectively detect the topic of a document by exploiting the syntactic structures, semantic association, and the context within the text. Experimental results show that SFTD is comparable to other well-known topic detection methods.

As guest editors of this special issue, we thank the authors for their contributions. We also would like to thank M.-H. Wang, member of the Ontology Application and Software Engineering (OASE) Laboratory at the National University of Tainan (NUTN), Taiwan, and Taiwanese Association for Artificial Intelligence (TAAI) for their support of this special issue. We are most grateful to the referees for spending their valuable time in reviewing the manuscripts and providing kind cooperation and help. Finally, we greatly appreciate Professors Antonio Di Nola (Editor-in-Chief) and Vincenzo Loia (Co-Editor-in-Chief) of Springer Soft Computing Journal, for providing us with the opportunity to edit and publish this special issue, as well as for their valuable instructions in the editorial process.