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Successful learning infrastructures and repositories often depend on well-organized content collections for effective dissemination, maintenance and preservation of resources. By combining semantic descriptions already lying or implicit within their descriptive metadata, reasoning-based or semantic searching of these collections can be enabled and produce novel possibilities for content browsing and retrieval. The specifics and necessities of such an approach, however, make it hard to assess and measure its effectiveness. The paper aims to discuss these issues.

Therefore in this paper the authors introduce a concrete methodology toward a pragmatic evaluation of semantic searching in such scenarios, which is exemplified through the semantic search plugin the authors have developed for the popular DSpace repository system.

The results reveal that this approach can be appealing to expert as well as novice users alike, improve the effectiveness of content discovery and enable new retrieval possibilities in comparison to traditional, keyword-based search.

This paper presents applied research efforts to employ semantic searching techniques on digital repositories and to construct a novel methodology for evaluating the outcomes against various perspectives. Although this is original in itself, value lies also within the concrete and measurable results presented, accompanied by an analysis, that would be helpful to assess similar (i.e. semantic query answering and searching) techniques in the particular scenario of digital repositories and libraries and to evaluate corresponding investments. To the knowledge there has been hardly any other evaluation effort in the literature for this particular case; that is, to assess the merit and usage of advanced semantic technologies in digital repositories.

Ontologies are used to formally describe the concepts within a domain in a machine-understandable way. Matching of heterogeneous ontologies is often essential for many applications like semantic annotation, query answering or ontology integration. Some ontologies may include a large number of entities which make the ontology matching process very complex in terms of the search space and execution time requirements. The purpose of this paper is to present a technique for finding degree of similarity between ontologies that trims down the search space by eliminating the ontology concepts that have less likelihood of being matched.

Algorithms are written for finding key concepts, concept matching and relationship matching. WordNet is used for solving synonym problems during the matching process. The technique is evaluated using the reference alignments between ontologies from ontology alignment evaluation initiative benchmark in terms of degree of similarity, Pearson’s correlation coefficient and IR measures precision, recall and F-measure.

Positive correlation between the degree of similarity and degree of similarity (reference alignment) and computed values of precision, recall and F-measure showed that if only key concepts of ontologies are compared, a time and search space efficient ontology matching system can be developed.

On the basis of the present novel approach for ontology matching, it is concluded that using key concepts for ontology matching gives comparable results in reduced time and space.

Institutional repositories (IR) are usually used to archive and manage digital collections including research results, educational material, etc. Learning management systems (LMS) form a popular basis for e-learning and blended learning. This paper aims to study how to integrate IR and LMS to support accessibility of disabled students and students with learning difficulties (dyslexic students) in higher education. Customised ontologies focusing on disabled students can be used to facilitate indexing, and access of items in the repository.

The authors propose a simple methodological approach to establish an integrating system for supporting accessibility. First, the authors review research works related to adaptive learning environments (ALEs) and blended learning, and discuss issues of the interoperability of IR and LMS. Then, based on the review, the authors discuss the use of an integrated ALE for supporting disabled students in the domain of higher technological education. The integrated system is based on IR, LMS and assistive and adaptive technology. The open source software platform DSpace is used to build up the repository applications Use of the web ontology language (OWL) ontologies is also proposed for indexing and accessing the various, heterogeneous items stored in the repository. Various open source LMS (e.g. openeclass) could be used to build up the integrated system. Finally, the authors describe experimentation with a prototype implemented to provide the mentioned capabilities.

The technology is mature enough for building up integrated systems, combining capabilities of IR and LMS, for supporting disabled students. The use of ontologies focused on disabled students could facilitate the use of such integrated systems. Customisation and operation of a platform, for the selection and use of portions of OWL ontologies, could be based on the open source software Protégé. Such a platform forms a basis to create an appropriate ontology suitable for specific domains, e.g. the domain of technological education. Finally, the authors argue that the combined use of the OWL platform and the DSpace repository with open source LMS platforms could support domain experts for creating customised ontologies and facilitating searching.

A new perception of the term integrated system for supporting disabled students in the higher education context is presented. This perception tries to combine the IR technology that supports the self-archiving approach of information, open LMS technology and the user-centred approach to support students and manage the “life of information”.

The purpose of this paper is to present an efficient and scalable Arabic semantic search engine based on a domain-specific ontological graph for Colleges of Applied Science, Sultanate of Oman (CASOnto). It also supports the factorial question answering and uses two types of searching: the keyword-based search and the semantics-based search in both languages Arabic and English. This engine is built on variety of technologies such as resource description framework data and ontological graph. Furthermore, two experimental results are conducted; the first is a comparison among entity-search and the classical-search in the system itself. The second compares the CASOnto with well-known semantic search engines such as Kngine, Wolfram Alpha and Google to measure their performance and efficiency.

The design and implementation of the system comprises the following phases, namely, designing inference, storing, indexing, searching, query processing and the user’s friendly interface, where it is designed based on a specific domain of the IBRI CAS (College of Applied Science) to highlight the academic and nonacademic departments. Furthermore, it is ontological inferred data stored in the tuple data base (TDB) and MySQL to handle the keyword-based search as well as entity-based search. The indexing and searching processes are built based on the Lucene for the keyword search, while TDB is used for the entity search. Query processing is a very important component in the search engines that helps to improve the user’s search results and make the system efficient and scalable. CASOnto handles the Arabic issues such as spelling correction, query completion, stop words’ removal and diacritics removal. It also supports the analysis of the factorial question answering.

In this paper, an efficient and scalable Arabic semantic search engine is proposed. The results show that the semantic search that built on the SPARQL is better than the classical search in both simple and complex queries. Clearly, the accuracy of semantic search equals to 100 per cent in both types of queries. On the other hand, the comparison of CASOnto with the Wolfram Alpha, Kngine and Google refers to better results by CASOnto. Consequently, it seems that our proposed engine retrieved better and efficient results than other engines. Thus, it is built according to the ontological domain-specific, highly scalable performance and handles the complex queries well by understanding the context behind the query.

The proposed engine is built on a specific domain (CAS Ibri – Oman), and in the future vision, it will highlight the nonfactorial question answering and expand the domain of CASOnto to involve more integrated different domains.

The main contribution of this paper is to build an efficient and scalable Arabic semantic search engine. Because of the widespread use of search engines, a new dimension of challenge is created to keep up with the evolution of the semantic Web. Whereas, catering to the needs of users has become a matter of paramount importance in the light of artificial intelligence and technological development to access the accurate and the efficient information in less possible time. However, the research challenges still in its infancy due to lack of research engine that supports the Arabic language. It could be traced back to the complexity of the Arabic language morphological and grammar rules.

Semantic Web is an extension of the World Wide Web by tagging content with “meaning”. In general, question answering systems based on semantic Web face a number of difficult issues. This paper aims to design an experimental environment with custom rules and scalable data sets and evaluate the performance of a proposed optimized backward chaining ontology reasoning system. This study also compares the experimental results with other ontology reasoning systems to show the performance and scalability of this ontology reasoning system.

The authors proposed a semantic question answering system. This system has been built using ontological knowledge base including optimized backward chaining ontology reasoning system and custom rules. With custom rules, the proposed semantic question answering system will be able to answer questions that contain qualitative descriptors such as “groundbreaking” resesarch and “tenurable at university x”. Scalability has been one of the difficult issues faced by an optimized backward chaining ontology reasoning system and semantic question answering system. To evaluate the proposed ontology reasoning system, first, the authors design a number of innovative custom rule sets and corresponding query sets. The innovative custom rule sets and query sets will contribute to the future research on evaluating ontology reasoning systems as well. Then they design an experimental environment including ontologies and scalable data sets and metrics. Furthermore, they evaluate the performance of the proposed optimized backward chaining reasoning system on supporting custom rules. The evaluation results have been compared with other ontology reasoning systems as well.

The proposed innovative custom rules and query sets can be effectively employed for evaluating ontology reasoning systems. The evaluation results show that the scalability of the proposed backward chaining ontology reasoning system is better than in-memory reasoning systems. The proposed semantic question answering system can be integrated in sematic Web applications to solve scalability issues. For light weight applications, such as mobile applications, in-memory reasoning systems will be a better choice.

This paper fulfils an identified need for a study on evaluating an ontology reasoning system on supporting custom rules with and without external storage.

The semantic and structural heterogeneity of large Extensible Markup Language (XML) digital libraries emphasizes the need of supporting approximate queries, i.e. queries where the matching conditions are relaxed so as to retrieve results that possibly partially satisfy the user's requests. The paper aims to propose a flexible query answering framework which efficiently supports complex approximate queries on XML data.

To reduce the number of relaxations applicable to a query, the paper relies on the specification of user preferences about the types of approximations allowed. A specifically devised index structure which efficiently supports both semantic and structural approximations, according to the specified user preferences, is proposed. Also, a ranking model to quantify approximations in the results is presented.

Personalized queries, on one hand, effectively narrow the space of query reformulations, on the other hand, enhance the user query capabilities with a great deal of flexibility and control over requests. As to the quality of results, the retrieval process considerably benefits because of the presence of user preferences in the queries. Experiments demonstrate the effectiveness and the efficiency of the proposal, as well as its scalability.

Future developments concern the evaluation of the effectiveness of personalization on queries through additional examinations of the effects of the variability of parameters expressing user preferences.

The paper is intended for the research community and proposes a novel query model which incorporates user preferences about query relaxations on large heterogeneous XML data collections.

This paper seeks to evaluate to what extent Google retrieved correct answers to queries inferred from factual and topical requests in a digital Ask‐a‐Librarian service.

In total, 100 factual and 100 topical questions were picked from a digital reference service run by public libraries. The inferred queries simulated average web queries. They were expressed as separate keywords and as questions. The top ten retrieval results were observed for each answer. The inspection was stopped when the first correct answer was identified.

Google retrieved correct answers to 42 percent of the topical questions and 29 percent of factual questions by keyword queries. The performance of queries in question form was considerably weaker. The results concerning the characteristics of queries and retrieval effectiveness are also presented. Evaluations indicate that the public library reference services answered at least 55 percent of the questions correctly. Thus Google did not outperform the Ask‐a‐Librarian service.

The study introduces a new way of evaluating search engines by comparing their performance with other related services such as an Ask‐a‐Librarian service.

The purpose of this paper is to show the potential of self‐organized semantic storage services. The semantic web has provided a vision of how to build the applications of the future. A software component dedicated to the storage and retrieval of semantic information is an important but generic part of these applications. Apart from mere functionality, these storage components also have to provide good performance regarding the non‐functional requirements scalability, adaptability and robustness. Distributing the task of storing and querying semantic information onto multiple computers is a way of achieving this performance. However, the distribution of a task onto a set of computers connected using a communication network is not trivial. One solution is self‐organized technologies, where no central entity coordinates the system's operation.

Based on the available literature on large‐scale semantic storage systems, the paper analyzes the underlying distribution algorithm, with special focus on the properties of semantic information and corresponding queries. The paper compares the approaches and identify their shortcomings.

All analyzed approaches and their underlying technologies were unable to distribute large amounts of semantic information and queries in a generic way while still being able to react on changing network infrastructure. Nonetheless, as each concept represented a unique trade‐off between these goals, the paper points out how self‐organization is crucial to perform well at least in a subset of them.

The contribution of this paper is a literature review aimed at showing the potential of self‐organized semantic storage services. A case is made for self‐organization in a distributed storage system as the key to excellence in the relevant non‐functional requirements: scalability, adaptability and robustness.

Massive amounts of data, manifesting in various forms, are being produced on the Web every minute and becoming the new standard. Exploring these information sources distributed in different Web segments in a unified way is becoming a core task for a variety of users’ and companies’ scenarios. However, knowledge creation and exploration from distributed Web data sources is a challenging task. Several data integration conflicts need to be resolved and the knowledge needs to be visualized in an intuitive manner. The purpose of this paper is to extend the authors’ previous integration works to address semantic knowledge exploration of enterprise data combined with heterogeneous social and linked Web data sources.

The authors synthesize information in the form of a knowledge graph to resolve interoperability conflicts at integration time. They begin by describing KGMap, a mapping model for leveraging knowledge graphs to bridge heterogeneous relational, social and linked web data sources. The mapping model relies on semantic similarity measures to connect the knowledge graph schema with the sources' metadata elements. Then, based on KGMap, this paper proposes KeyFSI, a keyword-based semantic search engine. KeyFSI provides a responsive faceted navigating Web user interface designed to facilitate the exploration and visualization of embedded data behind the knowledge graph. The authors implemented their approach for a business enterprise data exploration scenario where inputs are retrieved on the fly from a local customer relationship management database combined with the DBpedia endpoint and the Facebook Web application programming interface (API).

The authors conducted an empirical study to test the effectiveness of their approach using different similarity measures. The observed results showed better efficiency when using a semantic similarity measure. In addition, a usability evaluation was conducted to compare KeyFSI features with recent knowledge exploration systems. The obtained results demonstrate the added value and usability of the contributed approach.

Most state-of-the-art interfaces allow users to browse one Web segment at a time. The originality of this paper lies in proposing a cost-effective virtual on-demand knowledge creation approach, a method that enables organizations to explore valuable knowledge across multiple Web segments simultaneously. In addition, the responsive components implemented in KeyFSI allow the interface to adequately handle the uncertainty imposed by the nature of Web information, thereby providing a better user experience.

Question answering (QA) answers the questions asked by people in the form of natural language. In the QA, due to the subjectivity of users, the questions they query have different expressions, which increases the difficulty of text retrieval. Therefore, the purpose of this paper is to explore new query rewriting method for QA that integrates multiple related questions (RQs) to form an optimal question. Moreover, it is important to generate a new dataset of the original query (OQ) with multiple RQs.

This study collects a new dataset SQuAD_extend by crawling the QA community and uses word-graph to model the collected OQs. Next, Beam search finds the best path to get the best question. To deeply represent the features of the question, pretrained model BERT is used to model sentences.

The experimental results show three outstanding findings. (1) The quality of the answers is better after adding the RQs of the OQs. (2) The word-graph that is used to model the problem and choose the optimal path is conducive to finding the best question. (3) Finally, BERT can deeply characterize the semantics of the exact problem.

The proposed method can use word-graph to construct multiple questions and select the optimal path for rewriting the question, and the quality of answers is better than the baseline. In practice, the research results can help guide users to clarify their query intentions and finally achieve the best answer.