A service-oriented architecture for digital libraries

As countless web services are amassing an enormous wealth of information on human behavior, web data is quickly becoming a crucial element of social research. To make their data available for social inquiry, an increasing number of services have set up application programming interfaces (APIs), which promise simple access to web data. The chapter discusses the value of APIs for social research, while also providing an overview of the basic techniques for accessing data from web interfaces. After discussing the benefits and challenges of APIs for the empirical social sciences, the chapter outlines some basic web technologies and data formats, such as XML and JSON, which are commonly encountered when dealing with APIs. The practical use of APIs for social research is elaborated in two sample applications, one on policing in the UK and one on collecting data from the Twitter API.

Marketing scholars increasingly use web scraping and application programming interfaces (APIs) to collect data from the internet. Yet, despite the widespread use of such web data, the idiosyncratic and sometimes insidious challenges in its collection have received limited attention. How can researchers ensure that the data sets generated via web scraping and APIs are valid? While existing resources emphasize technical details of extracting web data, the authors propose a novel methodological framework focused on enhancing its validity. In particular, the framework highlights how addressing validity concerns requires the joint consideration of idiosyncratic technical and legal/ethical questions along the three stages of collecting web data: selecting data sources, designing the data collection, and extracting the data. The authors further review more than 300 articles using web data published in the top five marketing journals and offer a typology of how web data have advanced marketing thought. The article concludes with directions for future research to identify promising web data sources and embrace novel approaches for using web data to capture and describe evolving marketplace realities.

Advent of technologies like semantic web, multi-agent systems, web mining has changed the internet as knowledge provider. Web personalization offers a solution to the information overload problem in current web by providing users a personalized experience, considering their interest, behavior, context and emotions. Semantic web technology is based on use of software agents, ontologies and reasoning to add meaning to web information. An important technology for achieving personalization is the use of independent intelligent software agents. This work reviews, web personalization in the light of semantic web and software agent technology. A comparative study of recent works in the domain of web personalization has been carried out for this purpose. This review highlights ample scope for application of intelligent agents in the web personalization domain for solving many existing issues like personalized content management, user profile learning, modeling and adaptive interactions with users.

Semantic Web is the name of the next generation World Wide Web, that has been recently proposed by Tim Berners-Lee and the World Wide Web Consortium (W3C)1. In this new Web architecture, information and Web services will be easily understandable and usable by both humans and computers. The objective is not to make computers understand the human language, but to define a universal model for the expression of the information and a set of inference rules that machines can easily use in order to process and relate the information as if they really understood it (Berners-Lee, 1998). Though, as the current Web provided sharing of documents among previously incompatible computers, the Semantic Web intends to go beyond, allowing stovepipe systems, hardwired computers, and other devices to share contents embedded in different documents. The most known architecture for Semantic Web is based on a stack of related technologies, each one being a whole research area by itself (Berners-Lee, Hendler, & Lassila. 2001; Pereira & Freire, 2005). Accomplishment of the Semantic Web is considered a great challenge, not only due to the complexity of implementation but also because of the vast applicability in several areas. In spite of this, Semantic Web is still one of the most promising research areas among those which aim to define a new architecture for the Web. Semantic Web goes far beyond previous information retrieval and knowledge representation projects, presenting a non-centralized way to represent and contextualize real-world concepts, unambiguously, for several areas of knowledge. Semantic Web-enabled machines will handle information at our communication level. It is clear that the ability to interpret reality is still very primitive, however, Semantic Web points a way towards machine interaction and learning (Pereira et al., 2005). Semantic Web will integrate, interact with, and bring benefits to most human activities. Its full potential will go beyond the Web to real-world machines, providing increased interaction between machines and with humans—smarter phones, radios, and other electronic devices. Semantic Web will bring a different kind of approach in the understanding of reality by the machines and will constitute a mark in the evolution of human knowledge (Pereira et al., 2005).

The role of the Semantic Web in Web 3.0, the next evolution of the internet, is significant. Web 3.0 is often referred to as the ‘intelligent web’ or the ‘Semantic Web’. The Semantic Web is an essential topic in Web 3.0 because it enables data integration, enhances data search on the World Wide Web, and promotes the development of knowledge graphs that allow computers to derive new insights and generate knowledge. Teaching the Semantic Web to undergraduate students offers numerous benefits. Students gain a deep understanding of cutting-edge web technologies, technical skills in data integration (the ability to develop systems with diverse datasets and linked data), and capabilities in information retrieval and knowledge management (data modelling and linked data querying). Moreover, they are able to design and develop different types of web-based applications such as intelligent data-driven decision-making apps, AI apps, or personalised-recommendation apps. In this article, we present ways to effectively integrate the Semantic Web as a subject in the current Computing Systems curriculum offering at Eastern Institute of Technology (EIT). We also propose a recommended list of theory topics, practical labs and practical projects that should be designed and developed to teach this paper effectively for undergraduate students.

Semantic Web services is considered as the concept bringing the web to its full potential by combining the best features of web services, agent technology, grid and the Semantic web. In this paper we present a semantic Web Services platform based on software agent (JASW), which introduces Semantic web and software agent and takes the automated discovery, selection, orchestration and invocation of Web services as application background. In the platform, we adopt Semantic web to accurately describe Web services, and use software agent to implement discovery, selection, and orchestration of Web services.

To realize the Semantic Web, it will be necessary to make existing database content available for emerging Semantic Web applications, such as web agents and services, which use ontologies to formally define the semantics of their data. Our research in the design and implementation of an ontology-based system, OntoGrate, addresses the critical and challenging problem of supporting human experts in multiple domains to interactively integrate information that is heterogenous in both structure and semantics. Databases, knowledge bases, the World Wide Web, and the emerging Semantic Web are some of the resources for which scalable integration remains a challenge. To integrate databases into the Semantic Web, we use Semantic Web ontologies to incorporate database schemas. An expressive first order ontology language, Web-PDDL, is used to define the structure, semantics, and mappings of data resources. A powerful inference engine, OntoEngine, can be used for query answering and data translation. In this paper, besides introducing new ideas in the OntoGrate system, we will elaborate on two case studies for which our system works well.

EUROLAN, which has been held biennially since 1993, is one of the most significant European summer schools in the area of natural language processing. Each of the EUROLAN sessions has focused on an area of timely interest to researchers in the field; this year's EUROLAN involved students in tutorials and hands-on sessions concerned with semantic web technologies as applied to language processing, ontology creation and use, and consideration of the semantic web's potential and limitations.

Semantic mashup of biomedical data

IntroductionDrug utilization is currently assessed through traditional data sources such as big electronic medical records (EMRs) databases, surveys, and medication sales. Social media and internet data have been reported to provide more accessible and more timely access to medications' utilization.ObjectiveThis review aims at providing evidence comparing web data on drug utilization to other sources before the COVID-19 pandemic.MethodsWe searched Medline, EMBASE, Web of Science, and Scopus until November 25th, 2019, using a predefined search strategy. Two independent reviewers conducted screening and data extraction.ResultsOf 6,563 (64%) deduplicated publications retrieved, 14 (0.2%) were included. All studies showed positive associations between drug utilization information from web and comparison data using very different methods. A total of nine (64%) studies found positive linear correlations in drug utilization between web and comparison data. Five studies reported association using other methods: One study reported similar drug popularity rankings using both data sources. Two studies developed prediction models for future drug consumption, including both web and comparison data, and two studies conducted ecological analyses but did not quantitatively compare data sources. According to the STROBE, RECORD, and RECORD-PE checklists, overall reporting quality was mediocre. Many items were left blank as they were out of scope for the type of study investigated.ConclusionOur results demonstrate the potential of web data for assessing drug utilization, although the field is still in a nascent period of investigation. Ultimately, social media and internet search data could be used to get a quick preliminary quantification of drug use in real time. Additional studies on the topic should use more standardized methodologies on different sets of drugs in order to confirm these findings. In addition, currently available checklists for study quality of reporting would need to be adapted to these new sources of scientific information.

PurposeThe purpose of this paper is to explore useful Semantic Web technologies and ontology-based applications for digital libraries. It also investigates the perceptions of university librarians and academicians in Pakistan about Semantic Web technologies and their use in digital libraries.Design/methodology/approachAn exploratory research design based on Delphi research strategy was conducted to answer the research questions. Interviews were conducted with a purposive sample of 50 key informants including university librarians and academicians to explore their perceptions about Semantic Web technologies and their use in digital libraries. Thematic analysis of interview data was conducted to obtain results.FindingsThe results of this paper showed that DuraCloud, Semantic information mashup, OntoEdit and resource description framework (RDF) are the various Semantic Web applications which are useful for digital libraries to develop semantic relationships among digital contents and increase their accessibility in the web environment. Findings revealed that Semantic Web provides precise results and meets user information needs in an effective way. Results also showed that next-generation digital libraries use context-awareness technology, intelligent agent software and detecting sensors to analyze user information needs and provide dynamic information services. This paper recommended that librarians should embrace the use of emerging web technologies in libraries and offer library services through the medium of the web.Practical implicationsThis paper envisaged the future of digital library services and Semantic Web applications that can be used to re-structure metadata of digital library. This paper has practical implications for librarians to consider the useful applications of Semantic Web for digital library and enhance the interoperability of metadata among heterogeneous information systems. Practically, results obtained from this paper are highly useful for library schools and LIS teachers to up-date their curriculum by incorporating new contents related to web languages and Semantic Web applications for digital libraries.Originality/valueThis paper identifies various Semantic Web applications which are useful for developing Semantic Digital Libraries.

The World Wide Web (WWW) has provided us with a plethora of information. However, given its unstructured format, this information is useful mainly to humans and cannot be effectively interpreted by machines. The Semantic Web provides information in computer understandable structures (e.g., RDF), but the amount of information on the Semantic Web is limited compared to the amount of information available on the Web. The problem of generating a bridge between the Web and Semantic Web has recently gained a lot of attention. In this paper, we propose a Concept Extractor and Relationship Identifier (CE-RI) system, which acts as a bridge between Web and Semantic Web by providing a "semantic" way of presenting the search results to the user. The Concept Extractor (CE) component of our system makes use of the power of existing search engines coupled with the elegance of PageRank to extract high quality concepts related to the given query. The Relationship Identifier (RI) component finds relationships between the extracted concepts and the given query and presents them to the user in the form of a graph. It also stores the generated results formally, in the form of RDF triples, to facilitate better inferences as compared to traditional search engines. We evaluate our system by comparing its components CE and RI with other similar "state of the art" concept detection and relationship identification systems, respectively. The results produced by our system are either similar or better than those generated by other systems.

The semantic web: yet another hip?

Since archaeology in particular and humanities in general are interdisciplinary disciplines, there is an imperious need to enhance the accessibility and harmonise data integration, given their varied resource types (books, archives, scientific data, GIS, 3D models etc) and their different conceptual and technological structures and standards. A factor that further reduces accessibility and query performances is related to storing, such as physical location of data, language, and interface. The advent of Semantic Web technology represents an important advance in creating networks of archaeological knowledge based on various resources available on-line. While the valuable use of CIDOC-CRM (Comite International pour la Documentation -Conceptual Reference Model) ontology for specific CH (Cultural Heritage) domains (e.g. museums, libraries, etc) has been partially demonstrated, the interdisciplinary implementations are limited. In this article, we explore the potential of the CIDOC-CRM for a cross-domain implementation of CH data integration. We conducted tha mappings of different on-line resources related to Stonehenge to CIDOC-CRM, and evaluated them in the MAD database, a web-based application that manages natively XML-based datasets. The result ensures the validity of mapping mechanism and the semantic integration of CH data from different sources.

웹에 분산된 모든 윈 페이지는 구조가 서로 다르다. 시멘틱 웹 환경은 이형적인 구조를 갖는 웹 페이지들의 메타데이터 바탕으로 시멘틱 검색이 가능하다. 그러나 일반적으로 사용자의 요구에 따른 시멘틱 김색은 상황에 따라 엄청난 수의 검색 결과를 내놓는다. 따라서 검색 결과에 대해 각 사용자에 맞는 검색 결과 순위를 적용할 필요가 있다. Culture Finder는 시멘틱 웹 검색 에이전트들이 개인화 된 문화 정보를 검색할 수 있도록 도움을 준다. Culture Finder는 웹에 존재하는 각 웹 페이지에 대한 메타 데이터를 작성하고, 시멘틱 검색을 이행하며 사용자 프로파일을 기반으로 삼아 검색 결과에 대한 순위 점수를 계산한다. Culture Finder에는 개인화 된 시멘틱 검색을 효율적으로 실행하기 위해 중요한 5가지 기법이 적용되었다. 사용자의 검색 행위로부터 사용자 프로파일을 생성하기 위한 기계 학습기법, 시멘틱 웹 검색 에이전트를 위한 효율적인 시멘틱 검색 기법, 사용자 질의의 효과적인 파악을 위한 질의 분석 기법, 각 사용자에게 적합한 검색 결과를 제공하기 위한 순위 적용 기술, 메타데이터를 생성하기 위한 상위 온톨로지 표현 방법, 본 논문에서는 Culture Finder의 구조를 통해서 시멘틱 개인화 검색에 대한 기법을 제안한다. The semantic web environment promise semantic search of heterogeneous data from distributed web page. Semantic search would resuit in an overwhelming number of results for users is increased, therefore elevating the need for appropriate personalized ranking schemes. Culture Finder helps semantic web agents obtain personalized culture information. It extracts meta data for each web page(culture news, culture performance, culture exhibition), perform semantic search and compute result ranking point to base user profile. In order to work efficient, Culture Finder uses five major technique: Machine learning technique for generating user profile from user search behavior and meta data repository, an efficient semantic search system for semantic web agent, query analysis for representing query and query result, personalized ranking method to provide suitable search result to user, upper ontology for generating meta data. In this paper, we also present the structure used in the Culture Finder to support personalized search service.