Semantic Web Content Research Articles

Retraction Note: A Sly Salvage of Semantic Web Content with Insistence of Low Precision and Low Recall

Retraction Note: A Sly Salvage of Semantic Web Content with Insistence of Low Precision and Low Recall

Question answering system for semantic web: a review

Searching huge and heterogeneous contents from the web has become an increasingly challenging task with the contemporary growth of semantic web. In order to make the vision of semantic web a reality, some user-friendly interfaces are needed. Because of the complexity of natural language, question answering systems over semantic web presents many challenges. This work presents a survey on question answering (QA), which has emerged as an important tool in recent years to exploit the opportunities offered by semantic information on the web. It provides a comprehensive view by analysing the history of QA research field developed in the past, from open domain question answering system to the latest semantic question answering solutions. We explored the potential of question answering techniques to go beyond the current state of the art to support users in reusing and querying the semantic web contents. In the initial part of article, the classification of question answering systems (QAS), based on types of questions has been discussed. The later part discusses the common challenges of QA over semantic web, solutions, and suggested recommendations for future systems.

Augmented EHR: Enrichment of EHR with Contents from Semantic Web Sources

This work presents methods to combine data from the Semantic Web into existing EHRs, leading to an augmented EHR. An existing EHR extract is augmented by combining it with additional information from external sources, typically linked data sources. The starting point is a standardized EHR extract described by an archetype. The method consists of combining specific data from the original EHR with contents from the external information source by building a semantic representation, which is used to query the external source. The results are converted into a standardized EHR extract according to an archetype. This work sets the foundations to transform Semantic Web contents into normalized EHR extracts. Finally, to exemplify the approach, the work includes a practical use case in which the summarized EHR is augmented with drug–drug interactions and disease-related treatment information.

RETRACTED ARTICLE: A Sly Salvage of Semantic Web Content with Insistence of Low Precision and Low Recall

Semantic Web content extracting are the augmentation of the present web where the data is given in the better importance and allowing users to work close by close by utilizing the assets of web. Due to expanding of web content at speedier rate, it is difficult to adapt up to new procedures of separating to create the exact data in light of the user query. Later, number of scientists has taking a shot at enhancing the aftereffects of Web content mining by abusing Web Semantic Structure and can be executed in the web expresses through machine-processable data to bolsters the errands of users. But these methods bring about low precision [unseemly data from different inquiry result (equivocal)] and getting of off base data (i.e. low recall) with high surfing time. Therefore, the paper proposes the Semantic Web content mining utilizing two ways to deal with dodge low precision and low recall: the one is Kalman–Bucy filters, to channel the comparable information; the next is adaptive Helmholtz machine, to get the exact data in light of the user query. The proposed Kalman–Bucy filtering approach with adaptive Helmholtz machine execution is be assessed and contrasted with existing methodologies regarding retrieval accuracy and surfing time by dual predicted output and the optimized information retrieval rather than probability.

Semantic Web mining for Content-Based Online Shopping Recommender Systems

Currently, for content-based recommendations, semantic analysis of text from webpages seems to be a major problem. In this research, we present a semantic web content mining approach for recommender systems in online shopping. The methodology is based on two major phases. The first phase is the semantic preprocessing of textual data using the combination of a developed ontology and an existing ontology. The second phase uses the Naïve Bayes algorithm to make the recommendations. The output of the system is evaluated using precision, recall and f-measure. The results from the system showed that the semantic preprocessing improved the recommendation accuracy of the recommender system by 5.2% over the existing approach. Also, the developed system is able to provide a platform for content-based recommendation in online shopping. This system has an edge over the existing recommender approaches because it is able to analyze the textual contents of users feedback on a product in order to provide the necessary product recommendation.

An efficient clustering approach for fair semantic web content retrieval via tri-level ontology construction model with hybrid dragonfly algorithm

Web pages are heterogeneous and complex and there exists complicated associations within one web pages and linking to the others. The high interactions between terms in pages demonstrate vague and ambiguous meanings. Efficient and effective clustering methods are needed to discover latent and coherent meanings in context are necessary. This paper proposes an efficient clustering approach for fair semantic web content retrieval based on tri-level ontology construction model with hybrid dragonfly algorithm. Initially the query processing phase, by making use of systematic adaptive hierarchy method (SAHM) efficient ontology selection process is carried out by means of matching keywords retrieved form user query. Secondly, fuzzy sensitive near-neighbour influence (FSNI) based clustering approach relied on the ontology driven fuzzy linguistic measure, applied to estimate the uncertainty that may be relevant to the semantic content which belongs to the user quires. The proposed FSNI clustering approach with HDA algorithm performance is be evaluated and compared with existing clustering approaches in terms of retrieval accuracy and surfing time.

Leveraging Linked Open Data to Automatically Answer Arabic Questions

The interchangeably connected Web technologies and the advancements that accompany the semantic web content’s leaps, have raised many challenges in the results’ retrieval process especially for the Arabic Language. This research targets an important, yet insufficiently precedent, area in using Linked Open Data (LOD) for Automatic Question Answering systems in the Arabic Language. The significance of work presented, comes from its ability to overcome many challenges in querying Arabic content. Some of these challenges are: (a) bridging the gap between natural language and linked data by mapping users’ queries to a standard semantic web query language such as SPARQL, (b) facilitating multilingual access to semantic data, and (c) maintaining the quality of data. Another challenging aspect was the lack of related work and publicly available resources for Arabic Question Answering Systems over Linked Data, despite the vastly growing Arabic corpus on the web. This paper presents a novel approach that targets Automatic Arabic Questions’ Answering Systems whilst bypassing many featured challenges in the field. A hybrid approach that evaluates the effectiveness of using LOD to automatically answer Arabic questions is developed. The approach is developed to map users’ questions in Modern Standard Arabic, to a standard query language for LOD (i.e. SPARQL) through: (i) extracting entities from questions and linking them over the web using Named-Entity Recognition and Disambiguation (NER/NED), and (ii) extracting properties among extracted named entities using a dependency parsing approach integrated with Wikidata ontology. To evaluate our proposed system, an Arabic questions dataset was created including: (a) Question body in Arabic language, (b) Question type, (c) SPARQL Query formulation, and (d) Question answer. Evaluation results are promising with a Precision of 84%, a Recall of 81.3%, and an F-Measure of 82.8%.

SOCCOMAS: a FAIR web content management system that uses knowledge graphs and that is based on semantic programming.

We introduce Semantic Ontology-Controlled application for web Content Management Systems (SOCCOMAS), a development framework for FAIR (‘findable’, ‘accessible’, ‘interoperable’, ‘reusable’) Semantic Web Content Management Systems (S-WCMSs). Each S-WCMS run by SOCCOMAS has its contents managed through a corresponding knowledge base that stores all data and metadata in the form of semantic knowledge graphs in a Jena tuple store. Automated procedures track provenance, user contributions and detailed change history. Each S-WCMS is accessible via both a graphical user interface (GUI), utilizing the JavaScript framework AngularJS, and a SPARQL endpoint. As a consequence, all data and metadata are maximally findable, accessible, interoperable and reusable and comply with the FAIR Guiding Principles. The source code of SOCCOMAS is written using the Semantic Programming Ontology (SPrO). SPrO consists of commands, attributes and variables, with which one can describe an S-WCMS. We used SPrO to describe all the features and workflows typically required by any S-WCMS and documented these descriptions in a SOCCOMAS source code ontology (SC-Basic). SC-Basic specifies a set of default features, such as provenance tracking and publication life cycle with versioning, which will be available in all S-WCMS run by SOCCOMAS. All features and workflows specific to a particular S-WCMS, however, must be described within an instance source code ontology (INST-SCO), defining, e.g. the function and composition of the GUI, with all its user interactions, the underlying data schemes and representations and all its workflow processes. The combination of descriptions in SC-Basic and a given INST-SCO specify the behavior of an S-WCMS. SOCCOMAS controls this S-WCMS through the Java-based middleware that accompanies SPrO, which functions as an interpreter. Because of the ontology-controlled design, SOCCOMAS allows easy customization with a minimum of technical programming background required, thereby seamlessly integrating conventional web page technologies with semantic web technologies. SOCCOMAS and the Java Interpreter are available from (https://github.com/SemanticProgramming).

AN EFFICIENT CLUSTERING APPROACH FOR FAIR SEMANTIC WEB CONTENT RETRIEVAL VIA TRI-LEVEL ONTOLOGY CONSTRUCTION MODEL WITH HYBRID DRAGONFLY ALGORITHM

Web pages are heterogeneous and complex and there exists complicated associations within one web pages and linking to the others. The high interactions between terms in pages demonstrate vague and ambiguous meanings. Efficient and effective clustering methods are needed to discover latent and coherent meanings in context are necessary. This paper proposes an efficient clustering approach for fair semantic web content retrieval based on tri-level ontology construction model with hybrid dragonfly algorithm. Initially the query processing phase, by making use of systematic adaptive hierarchy method (SAHM) efficient ontology selection process is carried out by means of matching keywords retrieved form user query. Secondly, fuzzy sensitive near-neighbour influence (FSNI) based clustering approach relied on the ontology driven fuzzy linguistic measure, applied to estimate the uncertainty that may be relevant to the semantic content which belongs to the user quires. The proposed FSNI clustering approach with HDA algorithm performance is be evaluated and compared with existing clustering approaches in terms of retrieval accuracy and surfing time.

Entry Life-Cycle with automatic Change-History & Provenance Tracking in collaborative Semantic Web Content Management Systems as implemented in SOCCOMAS

SOCCOMAS is a ready-to-use Semantic Ontology-Controlled Content Management System (http://escience.biowikifarm.net/wiki/SOCCOMAS). Each web content management system (WCMS) run by SOCCOMAS is controlled by a set of ontologies and an accompanying Java-based middleware with the data housed in a Jena tuple store. The ontologies describe the behavior of the WCMS, including all of its input forms, input controls, data schemes and workflow processes (Fig. 1). Data is organized into different types of data entries, which represent collections of data referring to a particular material entity, for instance an individual specimen. SOCCOMAS implements a suite of general processes, which can be used to manage and organize all data entry types. One category of processes manages the life-cycle of a data entry, including all required for changing between the following possible entry states: current draft version; backup draft version; recycle bin draft version; deleted draft version; current published version; previously published version. current draft version; backup draft version; recycle bin draft version; deleted draft version; current published version; previously published version. The processes also allow a user to create a revised draft based on the current published version. Another category of processes automatically tracks the overall provenance (i.e. creator, authors, creation and publication date, contributers, relation between different versions, etc.) for each particular data entry. Additionally, on a significantly finer level of granularity, SOCCOMAS also tracks in a detailed change-history log all changes made to a particular data record at the level of individual input fields. All information (data, provenance metadata, change-history metadata) is stored based on Resource Description Framework (RDF) compliant data schemes into different named graphs (i.e. a URI under which triple statements are stored in the tuple store). All recorded information can be accessed through a SPARQL endpoint. All data entries are Linked Open Data and thus provide access to an HTML representation of the data for visualization in a web-browser or as a machine-readable RDF file. The ontology-controlled design of SOCCOMAS allows administrators to easily customize already existing templates for input forms of data entries, define new templates for new types of data entries, and define underlying RDF-compliant data schemes and apply them to each relevant input field. SOCCOMAS provides an engine for running and developing semantic WCMSs, where only ontology editing, but no middleware and front end programming, are required for adapting the WCMS to one's own specific requirements.

An Analysis of RDF View Maintenance using JENA

Objectives: This work provides an analysis of View maintenance of RDF structures for various data sizes using Jena. Methods/Statistical Analysis: We perform insert and deletion operations in files of three sizes and investigate how these operations are effected by the size of the file which is being worked upon. Five different experiments were conducted on the data files to provide a thorough analysis. Findings: The results from the experiments indicate that time taken for Insert operation increased proportionally however time remained the same for delete operations performed on the RDF data. It was also determined that first runs of any code in Jena took the longest time while time for operations reduced as more runs of the operations were made. Application/Improvements: The work provides a qualitative analysis of the updating of Semantic Web Content on the web.

Development of health information search engine based on metadata and ontology.

ObjectivesThe aim of the study was to develop a metadata and ontology-based health information search engine ensuring semantic interoperability to collect and provide health information using different application programs.MethodsHealth information metadata ontology was developed using a distributed semantic Web content publishing model based on vocabularies used to index the contents generated by the information producers as well as those used to search the contents by the users. Vocabulary for health information ontology was mapped to the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), and a list of about 1,500 terms was proposed. The metadata schema used in this study was developed by adding an element describing the target audience to the Dublin Core Metadata Element Set.ResultsA metadata schema and an ontology ensuring interoperability of health information available on the internet were developed. The metadata and ontology-based health information search engine developed in this study produced a better search result compared to existing search engines.ConclusionsHealth information search engine based on metadata and ontology will provide reliable health information to both information producer and information consumers.

Construction of fuzzy ontologies from fuzzy XML models

The success and proliferation of the Semantic Web depends heavily on construction of Web ontologies. However, classical ontology construction approaches are not sufficient for handling imprecise and uncertain information that is commonly found in many application domains. Therefore, great efforts on construction of fuzzy ontologies have been made in recent years. In particular, XML is imposing itself as a standard for representing and exchanging information on the Web, topics related to the modeling of fuzzy data have become very interesting in the XML data context. Therefore, constructing fuzzy ontologies from fuzzy XML data resources may make the existing fuzzy XML data upgrade to Semantic Web contents, and the constructed fuzzy ontologies may be useful for improving some fuzzy XML applications.This paper proposes a formal approach and an automated tool for constructing fuzzy ontologies from fuzzy XML data resources. Firstly, we propose a formal definition of fuzzy XML models (including the document structure fuzzy DTDs and the document content fuzzy XML documents). On this basis, we propose a formal approach for constructing fuzzy ontologies from fuzzy XML models, i.e., transforming a fuzzy XML model (including fuzzy DTD and fuzzy XML document) into a fuzzy ontology. Also, we give the proof of correctness of the construction approach, and provide a detailed construction example. Furthermore, we implement a prototype tool called FXML2FOnto, which can automatically construct fuzzy ontologies from fuzzy XML models. Finally, in order to show that the constructed fuzzy ontologies may be useful for improving some fuzzy XML applications, we focus on investigating how to reason on fuzzy XML models (e.g., conformance, inclusion, and equivalence) based on the constructed fuzzy ontologies, and it turns out that the reasoning tasks of fuzzy XML models can be checked by means of the reasoning mechanism of fuzzy ontologies.

Automatic Ontology-based Annotation of Food, Nutrition and Health Arabic Web Content

To have a successful semantic Web, it is critically required to have sufficient amount of relevant semantic and high-quality Web content. One way to produce such content is through the semantic annotation of the Web sources. Semantic annotation is the process of adding machine-readable content to the natural language textual content of the Web sources. Annotating Web content in Arabic language has received less attention compared to Latin Languages especially for content related to specific domains such as food, nutrition and health. Considering the huge amount of emerging Web content, semantic annotation of their contents by hand is neither practicable nor scalable. In this paper, we present an automatic annotation of the Arabic Web resources related to food, nutrition and health domains. The proposed method makes use of developed Arabic OWL ontologies related to those domains. It uses linguistic patterns to discover relevant relationships between the named entities in the Arabic Web resources. The extracted information is then associated to the corresponding concepts and object properties of the developed ontology to produce the RDF metadata for the corresponding Web resources. Empirical evaluations of the proposed method show promising precision and recall. As a contribution, the produced RDF triples could be utilized by semantic Web searching application to retrieve intelligent and relevant answers to end user's quires.

Semantic Data Extraction from Infobox Wikipedia Template

Wikis are established means for collaborative authoring, versioning and publishing of textual articles. The Wikipedia for example, succeeded in creating the by far largest encyclopedia just on the basis of a wiki. Wikis are created by wiki software and are often used to create collaborative works. One of the key challenges of computer science is answering rich queries. Several approaches have been proposed on how to extend wikis to allow the creation of structured and semantically enriched content. Semantic web allows of creation of such web. Also, Semantic web contents help us to answer rich queries. One of the new applications in semantic web is DBpedia. DBpedia project focus on creating semantically enriched structured information of Wikipedia. In this article, we describe and clarify the DBpedia project. We test the project to get structured data as triples from some Wikipedia resources. We clarify examples of car resource and Berlin resource. The output data is in RDF (Resource Description Framework) triple format which is the basic technology used for building the semantic web. We can answer rich queries by making use of semantic web structure. General Terms Information retrieval; semantic web.

Knowledge Representation and Management: Benefits and Challenges of the Semantic Web for the Fields of KRM and NLP

To summarize excellent current research in the field of knowledge representation and management (KRM). A synopsis of the articles selected for the IMIA Yearbook 2011 is provided and an attempt to highlight the current trends in the field is sketched. This last decade, with the extension of the text-based web towards a semantic-structured web, NLP techniques have experienced a renewed interest in knowledge extraction. This trend is corroborated through the five papers selected for the KRM section of the Yearbook 2011. They all depict outstanding studies that exploit NLP technologies whenever possible in order to accurately extract meaningful information from various biomedical textual sources. Bringing semantic structure to the meaningful content of textual web pages affords the user with cooperative sharing and intelligent finding of electronic data. As exemplified by the best paper selection, more and more advanced biomedical applications aim at exploiting the meaningful richness of free-text documents in order to generate semantic metadata and recently to learn and populate domain ontologies. These later are becoming a key piece as they allow portraying the semantics of the Semantic Web content. Maintaining their consistency with documents and semantic annotations that refer to them is a crucial challenge of the Semantic Web for the coming years.

Mining query-logs towards learning useful kick-off ontologies: an incentive to semantic web content creation

The aim of the paper is to present an ontology learning method that automatically (unsupervised) builds useful kick-off ontologies from query logs. We introduce tasks related to the proposed method in all phases of an ontology engineering lifecycle, extending human-centred and collaborative engineering methodologies for devising continuously evolving ontologies such as the human centred ontology engineering methodology (HCOME). Users, both knowledge workers of an organisational knowledge management setting and WWW users, are provided with a useful kick-off ontology that is automatically built from users' search interests in order to meet ontology engineering and ontology-based application needs. The paper provides evidences that this new approach plays a significant role as an incentive in the semantic content creation bottleneck.

User-driven generation of semantic web content in Enterprise 2.0: a case study

The use of semantic web technologies in enterprise environments has been subject to lots of research and development since the end of the 1990s, with topics ranging from semantic middleware infrastructures to ontology-based knowledge management systems. In this paper, we focus on the particular issue of user-driven generation of semantic web content in Enterprise 2.0 environments, and demonstrate how we extended several already-deployed user-driven applications (such as blogs, wikis and tagging systems) to produce and use semantic annotations in such context. Especially, we discuss the technical aspects involved in the process as well as the incentives that have been provided to involve users in producing such data. In addition, we present how users reacted and accepted the tools and what are the lessons that we learnt during that project.

Is Question Answering fit for the Semantic Web?: A survey

Abstract. With the recent rapid growth of the Semantic Web (SW), the processes of searching and querying content that is both massive in scale and heterogeneous have become increasingly challenging. User-friendly interfaces, which can support end users in querying and exploring this novel and diverse, structured information space, are needed to make the vision of the SW a reality. We present a survey on ontology-based Question Answering (QA), which has emerged in recent years to exploit the opportunities offered by structured semantic information on the Web. First, we provide a comprehensive perspective by analyzing the general background and history of the QA research field, from influential works from the artificial intelligence and database communities developed in the 70s and later decades, through open domain QA stimulated by the QA track in TREC since 1999, to the latest commercial semantic QA solutions, before tacking the current state of the art in open userfriendly interfaces for the SW. Second, we examine the potential of this technology to go beyond the current state of the art to support end-users in reusing and querying the SW content. We conclude our review with an outlook for this novel research area, focusing in particular on the R&D directions that need to be pursued to realize the goal of efficient and competent retrieval and integration of answers from large scale, heterogeneous, and continuously evolving semantic sources.

The knowledge reengineering bottleneck

Knowledge engineering upholds a longstanding tradition that emphasises methodological issues associated with the acquisition and representation of knowledge in some (formal) language. This focus on methodology implies an ex ante approach: “think before you act”. The rapid increase of linked data poses new challenges for knowledge engineering, and the Semantic Web project as a whole. Although the dream of unhindered “knowledge reuse” is a technical reality, it has come at the cost of control. Semantic web content can no longer be assumed to have been produced in a controlled task-independent environment. When reused, Semantic Web content needs to be remoulded, refiltered and recurated for a new task. Traditional ex ante methodologies do not provide any guidelines for this ex post knowledge reengineering; forcing developers to resort to ad hoc measures and manual labour: the knowledge reengineering bottleneck.