Description Of Web Resources Research Articles

Different negations and fuzziness in Web resources and resource description and their mathematical foundation

In Web resources and resource descriptions, many resources and descriptions of resources have different negations and fuzziness. In this paper, we investigate and analyze the cognition and characteristics of “negation” in philosophy, logic, linguistics and other knowledge fields, and based on the distinction between the concept of “contradiction” and the concept of “opposition”, and “clear concept” and “fuzzy concept”, we propose that there are three different kinds of negation in knowledge from the conceptual level, namely “contradictory negation”, “opposite negation” and “intermediary negation”. In order to establish a mathematical basis that can fully reflect the internal relations, properties and laws of these different negations and fuzziness, we propose a “Scoi: sets with contradictory negation, opposite negation and intermediary negation” and “Lcoi&PLcoi: logic with contradictory negation, opposite negation and intermediary negation”, and the operational properties of Scoi and the formal deduction properties of Lcoi have been proven. The soundness theorem, completeness theorem and compactness theorem for Lcoi are proved under the three-valued semantic interpretation of Lcoi, and the soundness and completeness for Lcoi&PLcoi are discussed under the given an infinite-valued semantics interpretation of Lcoi&PLcoi. Furthermore, we also demonstrated that descriptions of objective things such as “x is neither a positive nor a negative integer” and “he is neither rich nor poor” typle of statements are “intermediary negation” in Scoi and Lcoi&PLcoi. It shows that “intermediary negation” in Scoi and Lcoi&PLcoi has objectively.In order to enable RDF(Resource Description Framework) to describe Web resources with different negations and fuzziness, we extend RDF based on Scoi and Lcoi&PLcoi, and propose a resource description framework RDFcoi which can fully handle fuzzy attribute values and their different negations in attributes of resources. How to reflect the uncertainty of the relationship between Web resources and its attribute values, we believe that the relationship between a resource and fuzzy attribute value in one of its attributes should be a composite mapping from the resource domain to the fuzzy attribute value set and then to [0, 1], and a mathematical representation is given in this paper. Thus, RDFcoi differ from other RDF extensions. RDFcoi maintains the basic syntax and normative of RDF, and extends the basic syntax and semantics of RDF. Where, the concepts of RDFcoi triple with three kinds of negation, RDFcoi formula and RDFcoi graph are defined. RDFcoi vocabulary is determined and an interpretation is given. RDFcoi-entailment is defined and its properties are discussed. In addition, we also study the reasoning regimes of RDFcoi. By using the given logical translation method, a well-typed and interoperable RDFcoi graph can be transformed into a set of formulas in Lcoi&PLcoi, thus establishing “interoperability” between RDFcoi and Lcoi&PLcoi. From the perspective of reasoning mechanism of RDFcoi-entailment, RDFcoi reasoning can be carried out in Lcoi&PLcoi, and RDFcoi has the same reasoning mechanism as formal inference in Lcoi&PLcoi.

An Overview of Web Assisted Learning and Teaching of Tamil (WALTT) at the Penn Language Center

This resource description explores a web-assisted learning and teaching approach for Tamil facilitated by the Penn Language Center. Tamil is an ancient language spoken predominantly in the Indian state of Tamil Nadu and Sri Lanka. The language has significant cultural and linguistic relevance. However, due to limited resources and geographical constraints, access to high-quality Tamil language instruction has often been challenging for learners outside Tamil-speaking regions. To address these challenges, the Penn Language Center developed the WALTT program, which integrates web-based technologies into Tamil language learning and teaching. This web resource description aims to examine the structure of the syllabi in detail. Overall, this resource description aims to decode the presence of web-assisted learning resources in the field of Tamil language education, as well as provide valuable resources for the broader community of web-assisted language learning and teaching practitioners and beginners of the language.

Leveraging Genetic Reports and Electronic Health Records for the Prediction of Primary Cancers: Algorithm Development and Validation Study.

BackgroundPrecision oncology has the potential to leverage clinical and genomic data in advancing disease prevention, diagnosis, and treatment. A key research area focuses on the early detection of primary cancers and potential prediction of cancers of unknown primary in order to facilitate optimal treatment decisions.ObjectiveThis study presents a methodology to harmonize phenotypic and genetic data features to classify primary cancer types and predict cancers of unknown primaries.MethodsWe extracted genetic data elements from oncology genetic reports of 1011 patients with cancer and their corresponding phenotypical data from Mayo Clinic’s electronic health records. We modeled both genetic and electronic health record data with HL7 Fast Healthcare Interoperability Resources. The semantic web Resource Description Framework was employed to generate the network-based data representation (ie, patient-phenotypic-genetic network). Based on the Resource Description Framework data graph, Node2vec graph-embedding algorithm was applied to generate features. Multiple machine learning and deep learning backbone models were compared for cancer prediction performance.ResultsWith 6 machine learning tasks designed in the experiment, we demonstrated the proposed method achieved favorable results in classifying primary cancer types (area under the receiver operating characteristic curve [AUROC] 96.56% for all 9 cancer predictions on average based on the cross-validation) and predicting unknown primaries (AUROC 80.77% for all 8 cancer predictions on average for real-patient validation). To demonstrate the interpretability, 17 phenotypic and genetic features that contributed the most to the prediction of each cancer were identified and validated based on a literature review.ConclusionsAccurate prediction of cancer types can be achieved with existing electronic health record data with satisfactory precision. The integration of genetic reports improves prediction, illustrating the translational values of incorporating genetic tests early at the diagnosis stage for patients with cancer.

Rule-Based Actionable Intelligence for Disaster Situation Management

Managing natural disasters is a social responsibility as they might cause a gloomy impact on human life. Efficient and timely alert systems for public and actionable recommendations for decision makers may well decrease the number of casualties. Web semantics strengthen the description of web resources for exploiting them better and making them more meaningful for both human and machine. In this work, the authors propose a semantic rule-based approach for disaster situation management (DSM) to reach the next level of decision-making power and its architecture for providing actionable intelligence in the domain of the earthquake. The system itself is based on a data pre-processing layer, a computation layer, and the middle layer relies on an extensive rule base of experts' advice stored over time and a disaster ontology along with its inherent semantics. The rule-based reasoning approach uses this knowledge base in combination with the expert rule base, written in SWRL rules, to infer recommendations for the response to an earthquake.

Transforming RDF Data into Property Graphs

RDF databases and graph databases are two approaches of data management which are based on modeling, storing and querying data following a graph structure. RDF databases are based on a single graph data model which allows to describe Web resources in terms of their relations and attributes. On the other hand, most graph databases are based on the property graph data model, a type of graph where nodes and edges can contain properties represented as key-value pairs. This paper presents two methods for transforming RDF data into property graphs. The first method defines schema and data transformations as it assumes the existence of an RDF schema. The second method is schema-independent, so it allows to transform any kind of RDF dataset. Both methods are useful to store RDF data into a Graph Data Management System.

Intelligent Synchronous E-Learning Management System Based on Multi-Agents of Linked Data, Ontology, and Semantic Service.

E-learning is a method of distance learning using electronic media, where each learner and instructor have interacted via learning content present on the web. So, e-learning playing a major role in the learning and teaching activities at institutions of higher education worldwide. Web technology for the construction of synchronous e-learning has been successfully developed to provide automatic educational content. The most important developments in this area are the semantic web that provides distributed information with a specific meaning precisely so that e-learning becomes an efficient relevant task that grows in new dynamically changing at the same time with the learning requirements and needs of the learner. This paper has contributed to this goal by establishing a system known as Intelligent Synchronous E-Learning Management System (ISEMS) that based on multi-agent such as semantic service, ontology, and linked data. Each agent represents the basis for organizing content resources, sequence learning, and adaptation. ISEMS shows how the automatic generates of hypertext structures from distributed metadata is utilized from the semantic web resource description formats. The description is based primarily on the content, context, and content structure of the learning material then provides flexible and personalized access to these educational materials.

A Semantic Web Solution for Enhancing The Interoperability of E-learning Systems by Using Next Generation of SCORM Specifications

E-learning is a very dynamic field, constantly growing, which refers to an educa-tional content or learning experiences provided or made by means of digital tech-nologies. The development of this area has a direct impact on the quality of edu-cation and cost reduction.
 E-learning is now dominated by learning management systems such as Moo-dle, to improve the interoperability of these systems; the use of data / resources is a major requirement, this need to push the e-Learning community to seek new approaches to improve this interoperability of systems. On the other hand, se-mantic Web technologies offers standards like RDF allows to describe Web re-sources with their metadata. We propose in this work an approach based on Se-mantic Web technology to improve the interoperability of e-Learning systems like Moodle.

The role of collaborative tagging and ontologies in emerging semantic of web resources

The social web interactions have extended the sharing and the growth of web resources on the web. The collaborative web services (folksonomies) enable users to assign their freely chosen keywords (tags) to describe web resources. The advent of folksonomy has evolved the role of web users from consumers to contributors of information. Thus, users attribute their descriptive tags to annotate, organize and classify web resources of interests. Folksonomy became popular with the emergence of collaborative tagging. It offers a practical classification of web resources via the attributed tags. Nonetheless, the freely chosen tags weaken the semantic description of web resources. Folksonomy can give rise to a poor classification system based on ambiguous and inconsistent tags. Therefore, it is essential to pertinently describe the semantic of web resources to enhance their classification, findability and discoverability. The proposed approach represents a combined semantic enrichment strategy that explores collaborative tagging towards describing each web resource using different types of descriptive metadata, namely relevant folksonomy tags, content-based main keywords and matching ontology terms. The experimental evaluation has shown relevant results attesting the efficiency of our proposal. The alignment of social tagging with the ontology will not only enhances the classification of web resources but also constructs their semantic clustering. This emergent semantic will establish new challenges to improve the context-aware recommender systems of web resources in different real-world applications (healthcare, social education and cultural heritage).

Vocabulary Persona by Using Ontologies

Semantic Web add to all these resources that allow computer systems to understand the meaning by accessing collections of information and inference rules that can be used to drive reasoning automated to better satisfy user requirements. Standard description of Web resources proposed by the W3C, as the name implies, RDF (Resource Description Framework) is a meta-data used to guide the description of resources, to make it more structured information necessary for engines research and, more generally, to all necessary computer automated tool for analyzing web pages. The web is a new web sematique or all Web resources are described by metadata, which allows machines better use of these resources. Considering as a foundation specification FOAF (Friend Of A Friend), we use semantic structures (RDFa) to create an ontology and technologies in which it is implemented.Create a conceptual model (eg, an ontology) for personas and their uses in the context of human- computer interaction we will present some screenshots of execution of application.

A scalable approach for statistical learning in semantic graphs

Increasingly, data is published in the form of semantic graphs. The most notable example is the Linked Open Data LOD initiative where an increasing number of data sources are published in the Semantic Web's Resource Description Framework and where the various data sources are linked to reference one another. In this paper we apply machine learning to semantic graph data and argue that scalability and robustness can be achieved via an urn-based statistical sampling scheme. We apply the urn model to the SUNS framework which is based on multivariate prediction. We argue that multivariate prediction approaches are most suitable for dealing with the resulting high-dimensional sparse data matrix. Within the statistical framework, the approach scales up to large domains and is able to deal with highly sparse relationship data. We summarize experimental results using a friend-of-a-friend data set and a data set derived from DBpedia. In more detail, we describe novel experiments on disease gene prioritization using LOD data sources. The experiments confirm the ease-of-use, the scalability and the good performance of the approach.

Perspectives in semantic adaptive social web

The Social Web is now a successful reality with its quickly growing number of users and applications. Also the Semantic Web, which started with the objective of describing Web resources in a machine-processable way, is now outgrowing the research labs and is being massively exploited in many websites, incorporating high-quality user-generated content and semantic annotations. The primary goal of this special section is to showcase some recent research at the intersection of the Social Web and the Semantic Web that explores the benefits that adaptation and personalization have to offer in the Web of the future, the so-called Social Adaptive Semantic Web. We have selected two articles out of fourteen submissions based on the quality of the articles and we present the main lessons learned from the overall analysis of these submissions.

Representation Modeling Persona by using Ontologies: Vocabulary Persona

Semantic Web is then to add to all these resources semantics that allow computer systems to "understand" the meaning by accessing structured collections of information and inference rules that can be used to drive reasoning automated to better satisfy user requirements. Standard description of Web resources proposed by the W3C, as the name implies, RDF (Resource Description Framework) is a meta-data used to guide the description of resources, to make it more "structured" information necessary for engines research and, more generally, to all necessary computer automated tool for analyzing web pages. The web is a new web sematique or all Web resources are described by metadata, which allows machines better use of these resources. Considering as a foundation specification FOAF (Friend Of A Friend), we use semantic structures (RDFa) to create an ontology and technologies in which it is implemented.Create a conceptual model (eg, an ontology) for personas and their uses in the context of human-computer interaction we will present some screenshots of execution of application.

Metadata: Towards Machine-Enabled Intelligence

World Wide Web has revolutionized the means of data availability, but with its current structure model , it is becoming increasingly difficult to retrieve relevant information, with reasonable precision and recall, using the major search engines. However, with use of metadata, combined with the use of improved searching techniques, helps to enhance relevant information retrieval .The design of structured, descriptions of Web resources enables greater search precision and a more accurate relevance ranking of retrieved information .One such efforts towards standardization is , Dublin Core standard, which has been developed as Metadata Standard and also other standards which enhances retrieval of a wide range of information resources. This paper discuses the importance of metadata, various metadata schemas and elements, and the need of standardization of Metadata. This paper further discusses how the metadata can be generated using various tools which assist intelligent agents for efficient retrieval.

Cataloging of Internet Resources: Problems and Solutions (Experience of the Web Resources Catalog’s Creation in the Samara Regional Universal Research Library)

The paper deals with important questions of cataloguing of web resources. It summarizes certain experience of creating bibliographical data base in the environment of «МАРК-SQL» computer-aided library IT system achieved in the course of realizing the project «Multi-resource on-line support in the fields of culture, education and national health» which got the grant of President of the Russian Federation in 2009. The problems connected with selection and the description of web resources by catalog maintenance in an actual condition are considered, the ways of their decision are offered.

Tag-based fuzzy sets for criteria evaluation in on-line selection processes

Searching for a suitable item on the web becomes a tedious and time-consuming task. The success of that process depends on how well items are described, as well as how well a system is able to mimic a human way of performing selection. This is especially important when the user identifies a variety of criteria with different levels of priority. One of interesting developments of web 2.0 is tagging. Tagging is a process of describing web resources of any type for the identification purposes preformed by ordinary users. All items are annotated by anyone who wants to provide descriptions or comments about items. Each item together with all tags describing it is called a tag-cloud. In the paper we propose a novel approach of converting information embedded in tag-clouds into fuzzy representations of tags. Those fuzzy representations allow us to determine the strength of tag-item connections, and identify a single resource that satisfies a specific tag to the highest degree. We use this idea to represent users’ feedback as tag-based fuzzy sets which we combine with a lexicographical-based multi-criteria decision-making mechanism in order to build the selection engine for a knowledge-based recommender system.

Special Issue: Web 2.0, Semantics, Knowledge and Grid

With the massive participations of worldwide developers and users as well as enabling collaboration and social networks, Web 2.0 is significantly influencing the development of the Web, Semantic Web and Grid by aiming at a powerful and harmonious interconnection environment. The study of the future Web concerns the exploration of the laws among large-scale and expanding resources such as the formation of community and effective knowledge sharing, the semantics facilitating various interactions (between human, between machines and between human and machine) the scalable infrastructure and applications. This special issue is to reflect the up-to-date progresses of research on the future Web. It contains eight papers selected mainly from the third International Conference on Semantics, Knowledge and Grid 1, which emphasizes on promoting cross-area research. Web mining and community discovery play an important role in supporting intelligent Web applications. Zhang and Xu 2 introduce a set of Web mining methods based on various available information on the Web, including Web document contents, hyperlink analysis, user access logs and semantic analysis. Several key Web clustering algorithms are studied, including a latent linkage information algorithm for finding relevant pages and a Web page clustering algorithm based on the defined concept of Web page correlation. Pierce et al. 3 introduce the application of Web 2.0 in the following scenarios: client-side JavaScript libraries for building and composing Grid services, integrating server-side portlets with ‘rich client’ AJAX tools and Web services for analyzing a global positioning system's data, building and analyzing folksonomies of scientific user communities through social bookmarking, and applying microformats and GeoRSS to problems in scientific metadata description and delivery. The future Web and Grid need to combine different models and techniques to provide users with more accessible services and resources. Dillon et al. 4 discuss three important computing paradigms including service-oriented computing, Grid computing and Web 2.0. An abstracted distributed computing model is introduced and a Grid architecture GRIDspace is discussed. Semantic Web plays an important role in making the description of Web resources more machine-understandable, which can be used as the basic data model and query model to support heterogeneous resource management on Semantic Grid. Babik and Hluchy present an architecture to support efficient resource description, query processing and logic reasoning based on a subset of Semantic Web language 5. The system combines a tableau algorithm with a resolution-based reasoning method to support scalable queries on large-scale instances with complex schema for resource management on Semantic Grid. Ontology technologies are promising and enabling tools for building an intelligent Web. Ungrangsi et al. propose an ontology retrieval framework for the information sharing on the next-generation Web 6. A similarity ranking method is proposed to evaluate the semantic query on ontology modeled by XML declarative description language. Logic reasoning is an important tool to utilize knowledge information. Li et al. propose a case-based reasoning method with adaptive learning rules 7. Resource Space model and Semantic Link Network model are used for efficient resource management and reuse. By dynamically updating the reasoning rules, the reasoning results are improved. Semantics-rich queries are also important for databases that are to manage heterogeneous data objects. Li et al. 8 presents a semantic model for multimedia database systems. A semantic schema description framework called MediaView is provided for users to describe the data model of a multimedia database with more formal semantics. The MediaView model allows users to design a dynamic and evolving object-oriented schema for multimedia data objects, upon which a multimedia recipe database is developed. The future Web faces many problems from architectural solution to scalable and extensible data model. Sun et al. propose a system architectural framework with a semantic object model for massive information and knowledge sharing on the future Web 9. It incorporates peer-to-peer (P2P) computing techniques into the system platform where an object-oriented semantic model is provided for users to describe resources in a more flexible and extensible way. The basic system architecture and related semantic object model are suggested as the scalable semantic model for the future Web. We trust this special issue will make a significant contribution to the development of the future interconnection environment as described in 10. Finally, we would like to take this opportunity to thank the chief editor, publisher, authors and reviewers whose strong support made this special issue possible.

Navigating one million tags

AbstractWeb 2.0 users engage in collaborative tagging and associate words with the users' own and with other users' content. The result is an additional layer with descriptions of web resources. The information contained in the tag‐resource associations can certainly be useful. However, we argue that current collaborative tagging systems do not support users well in navigating and exploring tag spaces, because tags are treated as independent. We suggest that different ways of displaying tags to users are needed and describe our approach to supporting users in navigating tag spaces. We illustrate our ideas with a prototype user interface.

Terminology for Neuroscience Data Discovery: Multi-tree Syntax and Investigator-Derived Semantics

The Neuroscience Information Framework (NIF), developed for the NIH Blueprint for Neuroscience Research and available at http://nif.nih.gov and http://neurogateway.org , is built upon a set of coordinated terminology components enabling data and web-resource description and selection. Core NIF terminologies use a straightforward syntax designed for ease of use and for navigation by familiar web interfaces, and readily exportable to aid development of relational-model databases for neuroscience data sharing. Datasets, data analysis tools, web resources, and other entities are characterized by multiple descriptors, each addressing core concepts, including data type, acquisition technique, neuroanatomy, and cell class. Terms for each concept are organized in a tree structure, providing is-a and has-a relations. Broad general terms near each root span the category or concept and spawn more detailed entries for specificity. Related but distinct concepts (e.g., brain area and depth) are specified by separate trees, for easier navigation than would be required by graph representation. Semantics enabling NIF data discovery were selected at one or more workshops by investigators expert in particular systems (vision, olfaction, behavioral neuroscience, neurodevelopment), brain areas (cerebellum, thalamus, hippocampus), preparations (molluscs, fly), diseases (neurodegenerative disease), or techniques (microscopy, computation and modeling, neurogenetics). Workshop-derived integrated term lists are available Open Source at http://brainml.org ; a complete list of participants is at http://brainml.org/workshops.

A Study on Web Archives Design: The Description and the Format Approach

This paper focuses on the long-term preservation of the web resource. The authors try to explore web archives strategy from two viewpoints which are metadata format and the structure of contents. In the analysis of the metadata format, review several web archives programs in the world and analyze their rule of organizing information. Secondly, study the archival principle of provenance and its application in controlling levels of archives description. In addition, analyze multilevel structure of web resources to explore the possibility of developing a practical model for multilevel description of web resources. Finally, make use of Topic Maps to deconstruct web pages and build up the relational model of topics to achieve the goal of preservation and providing continues access to web resources.

Can a system make novice users experts? Analysis of metadata created by novices and experts with varying levels of assistance

Accurate description of web resources is one path to the development of the Semantic Web. Two major problems for metadata generation are lack of expertise and the number of web resources. An automated or semi-automated system could encourage creation of metadata for web resources. This work is focused on the design and assessment of a semi-automated Metadata-Generation System (MGS) using four classes of descriptive elements - bibliographic, semantic, keywords and structure. The study tries to assess how the level of knowledge or skill impacts the quality of the metadata generated and, based on the results, it suggests the kind of information automatic metadata generation tools should suggest to users.