Search results

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.

The purpose of this paper is to summarize the current state of the existing research on the application of semantic technologies in bibliographic databases by providing answers to a set of research questions resulting from a systematic literature review.

The present study consists of conducting a systematic literature review of research works related to the application of semantic technologies in bibliographic databases. A manual keyword search is performed in known academic databases. As a result, a total of 78 literature sources are identified as related to the topic and included in the review. From the selected literature sources, information is extracted, which is then summarized and analyzed according to previously defined research questions and finally reported. Besides, a framework is defined to classify literature sources found and collected as a result of the study. The main criteria, according to which the classification is performed, are the used semantic technology and the research problem for which semantic technologies are applied in bibliographic databases. The classification of the publications is verified by each author independently of others.

The conducted systematic scientific review establishes that the evolution of semantic technologies sets a period of increased interest in the researchers, as a result of which the advantages of using them for bibliographic descriptions are examined and practically confirmed. After defining semantic models for bibliographic descriptions and approaches to transform existing bibliographic data into their correspondence, the research interest is directed at their comparison, collation; enrichment to facilitate search and retrieval of useful information. Possible perspectives for future research are outlined, which mainly relate to the complete use of the created data sets and their transformation into knowledge repositories.

Despite the increasing importance of the semantic technologies in various areas, including the bibliographic databases, there is a lack of comprehensive literature review and classification of literature sources relevant to this topic. The detailed study proposed in the present paper supports introducing with the existing experience in the application of semantic technologies in bibliographic databases, as well as facilitates the discovery of trends and guidelines for future research.

With the rapid development of materials informatics and the Semantic Web, the semantic-driven solution has emerged to improve traditional query technology, which is hard to discover implicit knowledge from materials data. However, it is a nontrivial thing for materials scientists to construct a semantic query, and the query results are usually presented in RDF/XML format which is not convenient for users to understand. This paper aims to propose an approach to construct semantic query and visualize the query results for metallic materials domain.

The authors design a query builder to generate SPARQL query statements automatically based on domain ontology and query conditions inputted by users. Moreover, a semantic visualization model is defined based on the materials science tetrahedron to support the visualization of query results in an intuitive, dynamic and interactive way.

Based on the Semantic Web technology, the authors design an automatic semantic query builder to help domain experts write the normative semantic query statements quickly and simply, as well as a prototype (named MatViz) is developed to visually show query results, which could help experts discover implicit knowledge from materials data. Moreover, the experiments demonstrate that the proposed system in this paper can rapidly and effectively return visualized query results over the metallic materials data set.

This paper mainly discusses an approach to support semantic query and visualization of metallic materials data. The implementation of MatViz will be a meaningful work for the research of metal materials data integration.

This paper describes a software architecture that automatically adds semantic capabilities to data services. The proposed architecture, called OntoGenesis, is able to semantically enrich data services, so that they can dynamically provide both semantic descriptions and data representations.

The enrichment approach is designed to intercept the requests from data services. Therefore, a domain ontology is constructed and evolved in accordance with the syntactic representations provided by such services in order to define the data concepts. In addition, a property matching mechanism is proposed to exploit the potential data intersection observed in data service representations and external data sources so as to enhance the domain ontology with new equivalences triples. Finally, the enrichment approach is capable of deriving on demand a semantic description and data representations that link to the domain ontology concepts.

Experiments were performed using real-world datasets, such as DBpedia, GeoNames as well as open government data. The obtained results show the applicability of the proposed architecture and that it can boost the development of semantic data services. Moreover, the matching approach achieved better performance when compared with other existing approaches found in the literature.

This work only considers services designed as data providers, i.e., services that provide an interface for accessing data sources. In addition, our approach assumes that both data services and external sources – used to enhance the domain ontology – have some potential of data intersection. Such assumption only requires that services and external sources share particular property values.

Unlike most of the approaches found in the literature, the architecture proposed in this paper is meant to semantically enrich data services in such way that human intervention is minimal. Furthermore, an automata-based index is also presented as a novel method that significantly improves the performance of the property matching mechanism.

To overcome the limitations of purely statistical approaches to data protection, the purpose of this paper is to propose Semantic Disclosure Control (SeDC): an inherently semantic privacy protection paradigm that, by relying on state of the art semantic technologies, rethinks privacy and data protection in terms of the meaning of the data.

The need for data protection mechanisms able to manage data from a semantic perspective is discussed and the limitations of statistical approaches are highlighted. Then, SeDC is presented by detailing how it can be enforced to detect and protect sensitive data.

So far, data privacy has been tackled from a statistical perspective; that is, available solutions focus just on the distribution of the data values. This contrasts with the semantic way by which humans understand and manage (sensitive) data. As a result, current solutions present limitations both in preventing disclosure risks and in preserving the semantics (utility) of the protected data.

SeDC captures more general, realistic and intuitive notions of privacy and information disclosure than purely statistical methods. As a result, it is better suited to protect heterogenous and unstructured data, which are the most common in current data release scenarios. Moreover, SeDC preserves the semantics of the protected data better than statistical approaches, which is crucial when using protected data for research.

Individuals are increasingly aware of the privacy threats that the uncontrolled collection and exploitation of their personal data may produce. In this respect, SeDC offers an intuitive notion of privacy protection that users can easily understand. It also naturally captures the (non-quantitative) privacy notions stated in current legislations on personal data protection.

On the contrary to statistical approaches to data protection, SeDC assesses disclosure risks and enforces data protection from a semantic perspective. As a result, it offers more general, intuitive, robust and utility-preserving protection of data, regardless their type and structure.

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 purpose of this paper is to present a showcase of semantic time series processing which demonstrates how this technology can improve time series processing and community building by the use of a dedicated language.

The authors have developed a new semantic time series processing language and prepared showcases to demonstrate its functionality. The assumption is an environmental setting with data measurements from different sensors to be distributed to different groups of interest. The data are represented as time series for water and air quality, while the user groups are, among others, the environmental agency, companies from the industrial sector and legal authorities.

A language for time series processing and several tools to enrich the time series with meta‐data and for community building have been implemented in Python and Java. Also a GUI for demonstration purposes has been developed in PyQt4. In addition, an ontology for validation has been designed and a knowledge base for data storage and inference was set up. Some important features are: dynamic integration of ontologies, time series annotation, and semantic filtering.

This paper focuses on the showcases of time series semantic language (TSSL), but also covers technical aspects and user interface issues. The authors are planning to develop TSSL further and evaluate it within further research projects and validation scenarios.

The research has a high practical impact on time series processing and provides new data sources for semantic web applications. It can also be used in social web platforms (especially for researchers) to provide a time series centric tagging and processing framework.

The paper presents an extended version of the paper presented at iiWAS2012.

The aim of this paper is to empirically examine how to best incorporate such contextual data, such as location or the semantic place of mobile users, into mobile user behavior models. Acquiring such data has become technically easier than ever. The proper utilization of these data leads, hypothetically, to better understanding of mobile user behavior and, consequently, to enhanced mobile services.

The paper systematically compares, under multiple experimental settings, the predictive performances of models built with three different approaches (pre-filtering, contextual modeling and post-filtering) used for incorporating contextual data into user behavior models. The comparisons focus on by which approach additional semantic place information can be best utilized for making the most accurate inferences on mobile user behavior. Real-life smartphone usage data are utilized in the analysis.

The results demonstrate that none of the considered approaches uniformly dominate the others across all experimental settings. However, they show circumstance specific differences that need to be aligned with practical use cases for the best performance.

Identifying the most suitable approaches for utilizing the semantic place (and other contextual) data is an important practical problem for electronic service providers, whose offerings are increasingly moving to the mobile domain and thus need to respond to the demands of mobility.

The paper constitutes an initial step toward understanding and systematically evaluating different approaches for incorporating semantic place data into modeling mobile user behavior. Practitioners in the mobile service domain can apply the initial results and academics build upon them with more diverse experimental settings.

The growing volumes of semantic data available in the web result in the need for handling the information overload phenomenon. The potential of this amount of data is enormous but in most cases it is very difficult for users to visualize, explore and use this data, especially for lay-users without experience with Semantic Web technologies. The paper aims to discuss these issues.

The Visual Information-Seeking Mantra “Overview first, zoom and filter, then details-on-demand” proposed by Shneiderman describes how data should be presented in different stages to achieve an effective exploration. The overview is the first user task when dealing with a data set. The objective is that the user is capable of getting an idea about the overall structure of the data set. Different information architecture (IA) components supporting the overview tasks have been developed, so they are automatically generated from semantic data, and evaluated with end-users.

The chosen IA components are well known to web users, as they are present in most web pages: navigation bars, site maps and site indexes. The authors complement them with Treemaps, a visualization technique for displaying hierarchical data. These components have been developed following an iterative User-Centered Design methodology. Evaluations with end-users have shown that they get easily used to them despite the fact that they are generated automatically from structured data, without requiring knowledge about the underlying semantic technologies, and that the different overview components complement each other as they focus on different information search needs.

Obtaining semantic data sets overviews cannot be easily done with the current semantic web browsers. Overviews become difficult to achieve with large heterogeneous data sets, which is typical in the Semantic Web, because traditional IA techniques do not easily scale to large data sets. There is little or no support to obtain overview information quickly and easily at the beginning of the exploration of a new data set. This can be a serious limitation when exploring a data set for the first time, especially for lay-users. The proposal is to reuse and adapt existing IA components to provide this overview to users and show that they can be generated automatically from the thesaurus and ontologies that structure semantic data while providing a comparable user experience to traditional web sites.

Trajectory is the path a moving object takes in space. To understand the trajectory movement patters, data mining is used. However, pattern analysis needs semantics to be understood. Therefore, the purpose of this paper is to enrich trajectories with semantic annotations, such as the name of the location where the trajectory has stopped, so that the paper is able to attain quality decisions.

An experiment was conducted to explain that the use of raw trajectories alone is not enough for the decision-making process and detailed pattern extraction.

The findings of the paper indicates that some fundamental patterns and knowledge discovery is only obtainable by understanding the semantics underlying the position of each point.

The unavailability of data are a limitation of the paper, which would limit its generalizability. Additionally, the lack of availability of tools for automatically adding semantics to clusters posed as a limitation of the paper.

The paper encourages governments as well as businesses to analyze movement data using data mining techniques, in light of the surrounding semantics. This will allow, for example, solving traffic congestions, since by understanding the movement patterns, the traffic authority could make decisions in order to avoid such congestions. Moreover, it could also help tourism authorities, at national levels, to know tourist movement patterns and support these patterns with the required logistical support. Additionally, for businesses, mobile operators could dynamically enhance their services, voice and data, by knowing the semantically enriched patterns of movement.

The paper contributes to the already rare literature on trajectory mining, enhanced with semantics. Mainstream literature focusses on either trajectory mining or semantics, therefore the paper claims that the approach is novel and is needed as well. By integrating mining outcomes with semantic annotation, the paper contributes to the body of knowledge and introduces, with lab evidence, the new approach.