Search results

In this work, a new general framework is proposed to guide navigation over a collaborative concept network, in order to discover paths between concepts. Finding semantic chains between concepts over a semantic network is an issue of great interest for many applications, such as explanation generation and query expansion. Collaborative concept networks over the web tend to have features such as large dimensions, high connectivity degree, dynamically evolution over the time, which represent special challenges for efficient graph search methods, since they result in huge memory requirements, high branching factors, unknown dimensions and high cost for accessing nodes. The paper aims to discuss these issues.

The proposed framework is based on the novel notion of heuristic semantic walk (HSW). In the HSW framework, a semantic proximity measure among concepts, reflecting the collective knowledge embedded in search engines or other statistical sources, is used as a heuristic in order to guide the search in the collaborative network. Different search strategies, information sources and proximity measures, can be used to adapt HSW to the collaborative semantic network under consideration.

Experiments held on the Wikipedia network and Bing search engine on a range of different semantic measures show that the proposed HSW approach with weighted randomized walk strategy outperforms state-of-the-art search methods.

To the best of the authors' knowledge, the proposed HSW model is the first approach which uses search engine-based proximity measures as heuristic for semantic search.

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.

The purpose is to investigate a position for engaged scholarship bridging the gulf between theorizing and practice in a social system perspective using Design Thinking for assisting the emergence of a semantic reservoir in a polycentric network “in spe”.

The paper combines social systems theory with the concept of engaged scholarship based on Design Thinking, and illustrates how such a research position might be applied to problems of polycentric networks as a theoretical/methodological case.

The paper concludes on a possible role for an engaged scholarship as a midwife assisting the emergence of a shared semantic reservoir that is needed to make commitments and couplings possible to become a polycentric network. Design Thinking is explained as a structured way to irritate (disturb) other systems, and the role of a shared semantic reservoir for a polycentric network “in spe” is accounted for.

Bridging the gulf between theorizing and practice in management theory is under-explored, and social systems theory underlines the immanent rigor-relevance gap, which this paper suggests a way not to overcome, but to bridge. The discussion of the rigor-relevance gap is revisited. Also, the critical process for a shared semantic reservoir to emerge in the formation of poly-centric networks is underexplored and so are its role for coupling of networks. The conceptual understanding thereof is also contributed to.

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.

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.

In small group settings, is it the position in social networks or the content of communication that constitutes a leader? Studies focussing on the content suggest that leadership consists in creating and promoting meanings, whereas studies focussing on the connections stress that it is the network position that ‘makes a leader’. These two dimensions of leadership communication style have not been compared yet. To fill this gap, this chapter applies an emerging approach – socio-semantic network analysis – to jointly consider the content of, and the connections, in leaders' communication. Using a multisource dataset, we empirically study the social network positions (social network analysis) and the content of communication (semantic network analysis) of three leaders in a creative collective. Our findings reveal that different styles of leadership make diverse use of the content and the connections in a small group. The academic and practical implications are outlined.

To illustrate the need for social network metadata within semantic metadata.

Surveys properties of social networks and the semantic web, suggests that social network analysis applies to semantic content, argues that semantic content is more searchable if social network metadata is merged with semantic web metadata.

The use of social network metadata will alter semantical searches from being random with respect to source to direct with respect to source, which will increase the accuracy of search results.

Suggests that existing XML schemas for semantic web content be modified.

Introduction and overview of a new issue.

Foundational to the concept of the semantic social network; will be useful as an introduction to future work.

As the technology evolves, the ways in which supply chain is coordinated improve. During a careful study on the intelligent wireless web (IWW) and its services for future applications, its great potentials for the implementation of a mobile real‐time system for supply chain coordination were realized. This paper seeks to introduce a development process for the IWW. In addition, it aims to explain the concept of mobile real‐time supply chain coordination, and propose and describe a practical model for this subject matter based on the most recent technologies including the IWW and agents.

Objectives were achieved through a thorough study on the IWW, agent technology, and the ways of applying them for mobile real‐time coordination in supply processes. As a method to conduct the research, first, the paper made out what the IWW services are and how one may develop them. Since mobile real‐time coordination is an absolutely innovative concept, the study prepared a comprehensive understanding of it and then, a practical framework was sketched and explained to implement the suggested system. The approach to this topic was a realistic one and an attempt was made to include all the prerequisites and details for the intended system.

In the course of the work, it was found that the IWW and other corresponding technologies have the greatest potentials ever available for the realization of a mobile real‐time supply chain coordination system and most of the chapters illustrate the claim.

Mobile real‐time coordination and its use in supply chains is something new. The development process for IWW proposed here is totally practicable and no other implementation scenario for the application of the IWW in mobile real‐time coordination has been suggested yet.

This paper aims to semantically linking scientific research events implied by scientific and technical literature to support information analysis and information service applications. Literature research is an important method to acquire scientific and technical information which is important for research, development and innovation of science and technology. It is difficult but urgently required to acquire accurate, timely, rapid, short and comprehensive information from the large-scale and fast-growing literature, especially in the big data era. Existing literature-based information retrieval systems focus on basic data organization, and they are far from meeting the needs of information analytics. It becomes urgent to organize and analyze scientific research events related to scientific and technical literature for forecasting development trend of science and technology.

Scientific literature such as a paper or a patent is represented as a scientific research event, which contains elements including when, where, who, what, how and why. Metadata of literature is used to formulate scientific research events that are implied in introduction and related work sections of literature. Named entities and research objects such as methods, materials and algorithms can be extracted from texts of literature by using text analysis. The authors semantically link scientific research events, entities and objects, and then, they construct the event space for supporting scientific and technical information analysis.

This paper represents scientific literature as events, which are coarse-grained units comparing with entities and relations in current information organizations. Events and semantic relations among them together formulate a semantic link network, which could support event-centric information browsing, search and recommendation.

The proposed model is a theoretical model, and it needs to verify the efficiency in further experimental application research. The evaluation and applications of semantic link network of scientific research events are further research issues.

This paper regards scientific literature as scientific research events and proposes an approach to semantically link events into a network with multiple-typed entities and relations. According to the needs of scientific and technical information analysis, scientific research events are organized into event cubes which are distributed in a three-dimensioned space for easy-to-understand and information visualization.