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The huge volume of biomedical literature provides a nice opportunity and challenge to induce novel knowledge by finding some connections among logical‐related medical concepts This paper aims to propose a semantic‐based knowledge discovery system for mining novel connections from large online digital libraries.

The method takes advantages of the biomedical ontologies, MeSH and UMLS, as the source of semantic knowledge. A prototype system, Biomedical Semantic‐based Knowledge Discovery System (Bio‐SbKDS), is designed to uncover novel hypothesis/connections hidden in the biomedical literature. Using only the starting concept and the initial semantic relation derived from UMLS, Bio‐SbKDS can automatically generate the semantic types as category restrictions for concepts. Using the semantic types and semantic relations of the biomedical concepts, Bio‐SbKDS can identify the relevant concepts collected from Medline in terms of the semantic type and generate the novel hypothesis between these concepts based on the semantic relations.

The system successfully replicates Dr Swanson's famous discoveries: Raynaud disease/fish oil automatically, and generates much less intermediate concepts and spurious connections.

The method takes full advantage of the semantic knowledge of the biomedical concepts, compared with previous approaches, our methods generate much less but more relevant novel hypotheses. Another significant advantage over other traditional approaches is that our method requires much less human intervention in the discovery procedure.

The purpose of this paper is to discuss alternative definitions of and approaches to the semantic web. It aims to clarify the relationship between the semantic web, Web 2.0 and Library 2.0.

The paper is based on a literature review and evaluation of systems with semantic web features. It identifies and describes semantic web projects of relevance to libraries and evaluates the usefulness of JeromeDL and other social semantic digital library systems. It discusses actual and potential applications for libraries and makes recommendations for actions needed by researchers and practitioners.

The paper concludes that the library community has a lot to offer to, and benefit from, the semantic web, but there is limited interest in the library community. It recommends that there be greater collaboration between semantic web researchers and project developers, library management systems providers and the library community. Librarians should get involved in the development of semantic web standards, for example, metadata and taxonomies.

The paper clarifies the distinction between semantic web and Web 2.0 in a digital library environment. It evaluates and predicts future developments for operational systems.

Existing Simultaneous Localization and Mapping (SLAM) algorithms have been relatively well developed. However, when in complex dynamic environments, the movement of the dynamic points on the dynamic objects in the image in the mapping can have an impact on the observation of the system, and thus there will be biases and errors in the position estimation and the creation of map points. The aim of this paper is to achieve more accurate accuracy in SLAM algorithms compared to traditional methods through semantic approaches.

In this paper, the semantic segmentation of dynamic objects is realized based on U-Net semantic segmentation network, followed by motion consistency detection through motion detection method to determine whether the segmented objects are moving in the current scene or not, and combined with the motion compensation method to eliminate dynamic points and compensate for the current local image, so as to make the system robust.

Experiments comparing the effect of detecting dynamic points and removing outliers are conducted on a dynamic data set of Technische Universität München, and the results show that the absolute trajectory accuracy of this paper's method is significantly improved compared with ORB-SLAM3 and DS-SLAM.

In this paper, in the semantic segmentation network part, the segmentation mask is combined with the method of dynamic point detection, elimination and compensation, which reduces the influence of dynamic objects, thus effectively improving the accuracy of localization in dynamic environments.

This chapter aims to help librarians understand the underlying rationale for Resource Description and Access (RDA) and recognize the great potential of the Semantic Web for libraries.

It explains the linked data model and Semantic Web technologies in basic, informative terms, and describes how the Semantic Web is constructed. Semantic Web standards and technologies are discussed in detail, including URI, RDF, and ontologies. The study also traces the development of RDA and some of the major library Semantic Web projects. The authors explore how RDA shapes bibliographical data and prepares it for linked data in the Semantic Web. In addition, this study examines what libraries in the United States and the rest of the world have achieved in implementing RDA since its release.

RDA is the correct approach libraries should take.

This is the first and only chapter that covers the development of RDA in other countries as well as in the United States. It is highly informative for anyone who wishes to understand the RDA and Semantic Web and their relevance to libraries in a short period of time.

The conception of markets as interfaces connecting semi-autonomous systems of producers and customers has led to an extensive use of social network analysis. So far, the network focus has been on connections among people, paying less attention to the crucial role played by connections between cultural elements (e.g., concepts, representations, ideas) in the way markets are formed and sustained. Such connections constitute “semantic networks” and are the focus of the present article. We attend to them by developing a network view of the cultural dimension of markets and apply it in an empirical setting where culture plays a crucial role – luxury watchmaking – to illustrate the impact of market semantic networks on a major outcome: price.

Purpose – To investigate the potential of the semantic web as a source of information about social networks within academia, as well as more widely for webometric investigations.

Methodology – The functionality of five semantic search engines were analyzed to determine their suitability for webometric investigations, with the most suitable, Sindice.com, then being used to investigate the use of Friend of a Friend (FOAF) within UK academic web space.

Findings – In comparison to the web of documents, the semantic web is still a small part of online content. Even the well-established FOAF social vocabulary was not found on the majority of academic web sites, let alone being found to represent the majority of academics, and provided little indication of social networks between institutions. Nonetheless from a webometric perspective the study does show the potential of a semantic web for a far wider range of webometric investigations, and demonstrates that, unlike the traditional web, there are currently useful tools available.

Implications – Having established that there are appropriate tools available for webometric investigations of the semantic web, and acknowledging the potential of the semantic web for far more detailed webometric investigations, there is a need for additional studies to determine the specific strengths and limitations of the tools that are available, and investigate those areas where webometric investigations can provide the most useful insights.

Originality/value – The research applies established webometric methodologies to the social semantic web, demonstrating the potential of a whole new area for future webometric investigation.

Today’s in-demand skills may not be needed tomorrow. As companies are adopting a new group of technologies, they are in huge need of information technology (IT) professionals who can fill various IT positions with a mixture of technical and problem-solving skills. This study aims to adopt a sematic analysis approach to explore how the US Information Systems (IS) programs meet the challenges of emerging IT topics.

This study considers the application of a hybrid semantic analysis approach to the analysis of IS higher education programs in the USA. It proposes a semantic analysis framework and a semantic analysis algorithm to analyze and evaluate the context of the IS programs. To be more specific, the study uses digital transformation as a case study to examine the readiness of the IS programs in the USA to meet the challenges of digital transformation. First, this study developed a knowledge pool of 15 principles and 98 keywords from an extensive literature review on digital transformation. Second, this study collects 4,093 IS courses from 315 IS programs in the USA and 493,216 scientific publication records from the Web of Science Core Collection.

Using the knowledge pool and two collected data sets, the semantic analysis algorithm was implemented to compute a semantic similarity score (DxScore) between an IS course’s context and digital transformation. To present the credibility of the research results of this paper, the state ranking using the similarity scores and the state employment ranking were compared. The research results can be used by IS educators in the future in the process of updating the IS curricula. Regarding IT professionals in the industry, the results can provide insights into the training of their current/future employees.

This study explores the status of the IS programs in the USA by proposing a semantic analysis framework, using digital transformation as a case study to illustrate the application of the proposed semantic analysis framework, and developing a knowledge pool, a corpus and a course information collection.

The article extends the distinction of semantic from syntactic labour to comprehend all forms of mental labour. It answers a critique from de Fremery and Buckland, which required envisaging mental labour as a differentiated spectrum.

The paper adopts a discursive approach. It first reviews the significance and extensive diffusion of the distinction of semantic from syntactic labour. Second, it integrates semantic and syntactic labour along a vertical dimension within mental labour, indicating analogies in principle with, and differences in application from, the inherited distinction of intellectual from clerical labour. Third, it develops semantic labour to the very highest level, on a consistent principle of differentiation from syntactic labour. Finally, it reintegrates the understanding developed of semantic labour with syntactic labour, confirming that they can fully and informatively occupy mental labour.

The article further validates the distinction of semantic from syntactic labour. It enables to address Norbert Wiener's classic challenge of appropriately distributing activity between human and computer.

The article transforms work in progress into knowledge for diffusion.

It has practical implications for determining what tasks to delegate to computational technology.

The paper has social implications for the understanding of appropriate human and machine computational tasks and our own distinctive humanness.

The paper is highly original. Although based on preceding research, from the late 20th century, it is the first separately published full account of semantic and syntactic labour.

This research aimed to visualize and analyze the co-word network and thematic clusters of the intellectual structure in the field of linked data during 1900–2021.

This applied research employed a descriptive and analytical method, scientometric indicators, co-word techniques, and social network analysis. VOSviewer, SPSS, Python programming, and UCINet software were used for data analysis and network structure visualization.

The top ranks of the Web of Science (WOS) subject categorization belonged to various fields of computer science. Besides, the USA was the most prolific country. The keyword ontology had the highest frequency of co-occurrence. Ontology and semantic were the most frequent co-word pairs. In terms of the network structure, nine major topic clusters were identified based on co-occurrence, and 29 thematic clusters were identified based on hierarchical clustering. Comparisons between the two clustering techniques indicated that three clusters, namely semantic bioinformatics, knowledge representation, and semantic tools were in common. The most mature and mainstream thematic clusters were natural language processing techniques to boost modeling and visualization, context-aware knowledge discovery, probabilistic latent semantic analysis (PLSA), semantic tools, latent semantic indexing, web ontology language (OWL) syntax, and ontology-based deep learning.

This study adopted various techniques such as co-word analysis, social network analysis network structure visualization, and hierarchical clustering to represent a suitable, visual, methodical, and comprehensive perspective into linked data.

This paper aims to offer a comprehensive examination of the various solutions currently accessible for addressing the challenge of semantic interoperability in cyber physical systems (CPS). CPS is a new generation of systems composed of physical assets with computation capabilities, connected with software systems in a network, exchanging data collected from the physical asset, models (physics-based, data-driven, . . .) and services (reconfiguration, monitoring, . . .). The physical asset and its software system are connected, and they exchange data to be interpreted in a certain context. The heterogeneous nature of the collected data together with different types of models rise interoperability problems. Modeling the digital space of the CPS and integrating information models that support cyber physical interoperability together are required.

This paper aims to identify the most relevant points in the development of semantic models and machine learning solutions to the interoperability problem, and how these solutions are implemented in CPS. The research analyzes recent papers related to the topic of semantic interoperability in Industry 4.0 (I4.0) systems.

Semantic models are key enabler technologies that provide a common understanding of data, and they can be used to solve interoperability problems in Industry by using a common vocabulary when defining these models.

This paper provides an overview of the different available solutions to the semantic interoperability problem in CPS.