Web Techniques Research Articles

A Query Search Technique for Semantic Web Based on The Dynamic Distribution of Backjumping Method

In the resource description language of the semantic web, vagueness of Resource Description Framework (RDF) data is playing an important role. The effective querying of the RDF data is increasing importance in semantic web. In this research, the Dynamic Distribution of BackJumping (DDBJ) algorithm is proposed in the fuzzy graph due to the ability of the algorithm to maintain its autonomy in the data. The fuzzy graph is generated from the triplets in the RDF and the vertices, edges are extracted from pattern matching techniques. The vertices and edges are applied in the DDBJ to suggest the query in the semantic web. To analyze the effectiveness of the proposed DDBJ, the two real datasets are used. The proposed DDBJ method has the f-measure of 59 %, while the state-of-art method such as backtracking has achieved 56 %. The result shows that the DDBJ method has the higher performance than existing method in query processing.

A Novel Deep Learning Approach for Semantic Interoperability Between Heteregeneous Multi-Agent Systems

This article focuses on the issue of semantic interoperability in heterogeneous distributed multi-agent systems. Existing middleware technologies offer programming models that strongly combine agents’ learning models and communication models, which can lead to performance weaknesses when the number of agents is very important. Moreover, existing methods in the field of semantic interoperability solve the problem of understanding messages exchanged between distributed agents with heterogeneous ontologies, using several techniques to combine these ontologies. The first category of these methods relies on the fusion principle, others use alignment, and finally, there are those founded on Semantic Web technique. All these methods are limited to abstract concepts and do not deal with concrete concepts such as those represented by images. We propose in this paper a new approach that addresses the problem of semantic interoperability between heterogeneous distributed agents based on two principles: At first, the communication aspect of the agent from the learning aspect is separated. Then, we propose extending semantic interoperability to concrete concepts by combining two techniques: Semantic Web technology, which allows terms representing abstract concepts to be interpreted and deep learning technology, which is introduced as a new method to ensure semantic interoperability in the case of concrete concepts such as images. A detailed description of the proposed approach is provided, showing that it is very useful in solving the disadvantages of existing multi-agent platforms.

Adverse Childhood Experiences Ontology for Mental Health Surveillance, Research, and Evaluation: Advanced Knowledge Representation and Semantic Web Techniques.

BackgroundAdverse Childhood Experiences (ACEs), a set of negative events and processes that a person might encounter during childhood and adolescence, have been proven to be linked to increased risks of a multitude of negative health outcomes and conditions when children reach adulthood and beyond.ObjectiveTo better understand the relationship between ACEs and their relevant risk factors with associated health outcomes and to eventually design and implement preventive interventions, access to an integrated coherent dataset is needed. Therefore, we implemented a formal ontology as a resource to allow the mental health community to facilitate data integration and knowledge modeling and to improve ACEs’ surveillance and research.MethodsWe use advanced knowledge representation and semantic Web tools and techniques to implement the ontology. The current implementation of the ontology is expressed in the description logic ALCRIQ(D), a sublogic of Web Ontology Language (OWL 2).ResultsThe ACEs Ontology has been implemented and made available to the mental health community and the public via the BioPortal repository. Moreover, multiple use-case scenarios have been introduced to showcase and evaluate the usability of the ontology in action. The ontology was created to be used by major actors in the ACEs community with different applications, from the diagnosis of individuals and predicting potential negative outcomes that they might encounter to the prevention of ACEs in a population and designing interventions and policies.ConclusionsThe ACEs Ontology provides a uniform and reusable semantic network and an integrated knowledge structure for mental health practitioners and researchers to improve ACEs’ surveillance and evaluation.

Implementation of data mining techniques in web of things

Web of Things has been becoming quickly because of late progressions in interchanges and sensor advances. Interfacing an each question together through web looks extremely trouble some, however inside a casing of time Internet of Things will definitely change our life. The tremendous information caught by the web of Things are considered of high business and also social qualities and removing concealed data from crude information, different information mining calculation can be connected to web of things information. In this paper, We study efficient audit of different information mining models and also its application in web of Thing field alongside its benefits and negative marks.

Large-scale distributed semantic augmented reality services – A performance evaluation

This paper presents an architecture for large-scale distributed AR services and an experimental evaluation of the main component – Semantic Augmented Reality Middleware. The architecture is based on a client-server design, which supports semantic modeling and building contextual AR presentations for a large number of users. The architecture is based on the Service Oriented Architecture (SOA) paradigm that enables building distributed systems that provide application functionality as services to either end-user applications or other services. The proposed architecture consists of two client-side components and multiple server-side distributed service applications – semantic middleware and AR service providers. The presented findings are promising and demonstrate that application of semantic web techniques can be a useful solution to implement large-scale contextual distributed AR environments.

Extending A Model for Ontology-Based Arabic-English Machine Translation (NAN)

The acceleration in telecommunication needs leads to many groups of research, especially in communication facilitating and Machine Translation fields. While people contact with others having different languages and cultures, they need to have instant translations. However, the available instant translators are still providing somewhat bad Arabic-English Translations, for instance when translating books or articles, the meaning is not totally accurate. Therefore, using the semantic web techniques to deal with the homographs and homonyms semantically, the aim of this research is to extend a model for the ontology-based Arabic-English Machine Translation, named NAN, which simulate the human way in translation. The experimental results show that NAN translation is approximately more similar to the Human Translation than the other instant translators. The resulted translation will help getting the translated texts in the target language somewhat correctly and semantically more similar to human translations for the Non-Arabic Natives and the Non-English natives.

Exploring the applicability of the Semantic Web for discovering and navigating Australian Indigenous knowledge resources

ABSTRACTSemantic Web ontology files can be flexibly programmed to delineate metadata relationships in machine-readable formats to create relational pathways for discovering resources both on and off the Internet. There is a global community of Semantic Web developers and users across a broad multi-disciplinary range of interests who create and share extensible open-source ontologies. In this article, the author will explore the functionality of Semantic Web techniques for representing the ontologies of relatedness through kinship that typically underpin Australian Indigenous knowledge systems, and investigate their potentials for meeting persistent demands among leading Australian Indigenous collections creators and users to be able to search and discover their hereditary knowledge resources in ways that reflect and reinforce their enduring cultural values, ways of knowing and rights-management concerns.

Interactive map matching and its visualisation: techniques and system

The trajectory data of taxies is an important kind of traffic data. Many traffic applications need to perform processing and analysis on trajectory data. Visualising trajectory data of vehicles on road maps is an important measure of reflecting and demonstrating the trend of traffic variation, where map matching from trajectory data to road network plays the most crucial role in such a visualisation process. We design and implement a novel interactive visualised map matching system in this paper, namely MMatchingVis, which provides multiple front-end functions including road selection, data extraction, map matching algorithm selection and result display, based on web techniques and Baidu Map. MMatchingVis employs the JStorm platform for trajectory data processing. We evaluate MMatchingVis' map matching results with the trajectory dataset collected from 6,599 taxies in Kunming, and evaluation results show that MMatchingVis could efficiently process and analyse trajectory data, support multiple user interaction models, and provide fine-grained visualisation presentation.

Interactive map matching and its visualisation: techniques and system

The trajectory data of taxies is an important kind of traffic data. Many traffic applications need to perform processing and analysis on trajectory data. Visualising trajectory data of vehicles on road maps is an important measure of reflecting and demonstrating the trend of traffic variation, where map matching from trajectory data to road network plays the most crucial role in such a visualisation process. We design and implement a novel interactive visualised map matching system in this paper, namely MMatchingVis, which provides multiple front-end functions including road selection, data extraction, map matching algorithm selection and result display, based on web techniques and Baidu Map. MMatchingVis employs the JStorm platform for trajectory data processing. We evaluate MMatchingVis' map matching results with the trajectory dataset collected from 6,599 taxies in Kunming, and evaluation results show that MMatchingVis could efficiently process and analyse trajectory data, support multiple user interaction models, and provide fine-grained visualisation presentation.

Extracting Semantic-Based Video Game Characters Information from Social Media Platforms

Character generation in video games currently relies on game developers manually creating game characters which costs in time, effort and resources. Social media, in the form of blogs, microblogs, forums, wikis, social networks and review sites contain rich information about characters in video games that are not exploited for character generation. However, such information contained in various social media applications are disconnected from one another and are not structured or enriched that can be utilised for character generation. Semantic Web techniques provide ways of linking and enriching information contained in disconnected datasets. This enriched information can be used to build complete character models for generating new characters in video games. Moreover, a video game character knowledge graph can be constructed out of the semantically-enriched information that can be used not only for character generation in video games, but also in any application that requires information about video game characters. In this paper, we present our approach for exploiting social media platforms to create semantically-enriched character models. In particular, we present our Game Character Ontology (GCO) – a light-weight vocabulary for describing character information in video games – and our methodology for extracting and describing (using our ontology) game character information from social media platforms.

How to Improve Total Data Quality Management (TDQM) Using the Semantic Technology

Total data quality management is considered one of the most popular research areas in the last decade, where improving the quality plays the main role in the targeted achievement for any enterprise. Also, there is a lot of emerging information technologies which aid in improving the quality in different aspects. Through this research we will focus on how to improve the quality of data using the most recent technology in IT fields which is named Semantic Web technology by conducting set of hypotheses which aim to prove whether there is a the relation between the improving of web data and the usage of semantic web techniques or not. Also, we will focus on how to improve the performance of data query processing specially when having huge amount of dataset using the big data technologies

Does Meditation Alter Brain Responses to Negative Stimuli? A Systematic Review.

Background: Despite several attempts to review and explain how meditation alters the brain and facilitates emotion regulation, the extent to which meditation and emotion regulation strategies share the same neural mechanisms remains unclear.Objective: We aim to understand the influence of meditation on the neural processing of negative emotional stimuli in participants who underwent meditation interventions (naive meditators) and long-term meditators.Methodology: A systematic review was conducted using standardized search operators that included the presence of terms related to emotion, meditation and neuro-imaging techniques in PsycInfo, PubMed, Scopus, and Web of Science databases.Results: Searches identified 882 papers, of which 11 were eligible for inclusion. Results showed a predominance of greater prefrontal/frontal activity related to meditation, which might indicate the increased recruitment of cognitive/attentional control resources in naïve and long-term meditators. This increased frontal activity was also observed when participants were asked to simply react to negative stimuli. Findings from emotion-related areas were scarce but suggested increased insular activity in meditators, potentially indicating that meditation might be associated with greater bodily awareness.Conclusions: Meditation practice prompts regulatory mechanisms when participants face aversive stimuli, even without an explicit request. Moreover, some studies reported increased insular activity in meditators, consistent with the hypothesis that meditation helps foster an interoceptive awareness of bodily and emotional states.

Balloon remodeling-assisted Woven EndoBridge technique: description and feasibility for complex bifurcation aneurysms

BackgroundWoven EndoBridge (WEB) deployment remains challenging in aneurysms with a complex shape or orientation.ObjectiveTo show that embolization of wide-neck bifurcation aneurysms using the WEB device balloon remodeling-assisted technique is a...

Online Genomes: Problematizing the disruptiveness of direct‐to‐consumer genetic tests

AbstractDirect‐to‐consumer genetic tests (DTCGT) provide individualized risk profiles for common diseases through social web techniques. Academic debates have predominantly denounced them as a potentially harmful commercialization of genetics and focused on how laypeople might (mis)understand and (mis)use this information. DTCGT has also stimulated a heterogeneous set of methods and theoretical perspectives in the social sciences, which have drawn on but also complicated a purely medical understanding.DTCGT is unanimously deemed disruptive, albeit for a variety of reasons. The different underlying notions of disruptiveness correspond to an implicit sociology of information resonating with a main sociological framework, namely, rational actor theory, governmentality studies, and ecological approaches to digital practices and infrastructures. Accordingly, three corpora are identified. The first conceives of disruption in terms of medical impact on prospective patients. The second proposes a biopolitical interpretation by framing DTCGT as vectors of new governmentality based on making individuals responsible for disease prediction and prevention. The third scrutinizes users' engagements with data and infrastructures at the intersection of informatics and genomics and locates DTCGT within the broader process of blossoming health digitalization. Accordingly, the disruptive issues at stake mainly concern political and economic aspects related to data sharing and crowdsourcing.

TEMPORAL GIS AND SPATIOTEMPORAL DATA SOURCES

The recent technological advances in geospatial data collection have created massive data sets with better spatial and temporal resolution than ever. To properly deal with these data sets, geographical information systems (GIS) must evolve to represent, access, analyze and visualize big spatiotemporal data in an efficient and integrated way. In this paper, we highlight challenges in temporal GIS development and present a proposal to overcome one of them: how to access spatiotemporal data sets from distinct kinds of data sources. Our approach uses Semantic Web techniques and is based on a data model that takes observations as basic units to represent spatiotemporal information from different application domains. We define a RDF vocabulary for describing data sources that store or provide spatiotemporal observations.

The radiation oncology ontology (ROO): Publishing linked data in radiation oncology using semantic web and ontology techniques

Personalized medicine is expected to yield improved health outcomes. Data mining over massive volumes of patients' clinical data is an appealing, low-cost and noninvasive approach toward personalization. Machine learning algorithms could be trained over clinical "big data" to build prediction models for personalized therapy. To reach this goal, a scalable "big data" architecture for the medical domain becomes essential, based on data standardization to transform clinical data into FAIR (Findable, Accessible, Interoperable and Reusable) data. Using Ontologies and Semantic Web technologies, we attempt to reach mentioned goal. We developed an ontology to be used in the field of radiation oncology to map clinical data from relational databases. We combined ontology with semantic Web techniques to publish mapped data and easily query them using SPARQL. The Radiation Oncology Ontology (ROO) contains 1,183 classes and 211 properties between classes to represent clinical data (and their relationships) in the radiation oncology domain following FAIR principles. We combined the ontology with Semantic Web technologies showing how to efficiently and easily integrate and query data from different (relational database) sources without a priori knowledge of their structures. When clinical FAIR data sources are combined (linked data) using mentioned technologies, new relationships between entities are created and discovered, representing a dynamic body of knowledge that is continuously accessible and increasing.

Event driven and semantic based approach for data processing on IoT gateway devices

Internet of things (IoT) applications rely on networks composed of a set of heterogeneous sensors and smart devices, which have the capability to constantly monitor the surroundings and gather data. This heterogeneity is reflected in raw data collected by such type of systems. Additionally, these data are continuously streaming; thus leading to huge volumes of heterogeneous data, which are further transferred to centralized platforms for processing. Consequently, two main challenges have arisen. First, the heterogeneity aspect of IoT data makes high-level IoT applications’ task of interpreting such data and detecting events in the real world more complex. Second, sending sensory data to a centralized platform leads to some issues, such as extensive consumption of IoT devices’ limited resources, network traffic overloading, and latency, which might negatively impact the response time especially in systems that were designed to handle critical situations. In this paper, we propose a decentralized approach for IoT data processing, by delegating this task to distributed edge devices (Gateways) taking into consideration their limited resources and network bandwidth. To accomplish this, we proposed a two-layer data processing approach that employs a hyped model encompassed of complex event processing (CEP) and semantic web (SW) techniques. While the first is proposed for performing aggregation and classification tasks, we use the latter for performing semantic filtering and annotation tasks. We have evaluated the feasibility of our approach to process sensory data in the context of Air Quality Monitoring scenario using an experimentation involving established ontologies. Several benchmarks are considered such as overall runtime, data size, and response time.

HOW MUCH ARE CHARITY, FUNDRAISING, NGO AND NON-PROFITS CURRENTLY PAYING THEIR NEW STAFF?

This article explores current salary levels and some other related questions using public recruitment data from the CharityJobs website. According to CharityJob, the site is the United Kingdom's busiest one for charity, fundraising, NGO and not for profit jobs. Data collection took place between 4 September and 20 November 2016 with some basic techniques of web scraping (web harvesting or web data extraction), which is a computer software technique of extracting information from websites. All the process is documented at: https://rpubs.com/EduardoWF/charityjobs. The source code in RMarkdown is available for download following GNU General Public License. Everything was prepared with the open-source and freely-accesible statistical computing software R (R version 3.2.0 - http://cran.r-project.org/) and the IDE RStudio (Version 0.99.441 - http://www.rstudio.com/). In addition to presenting these powerful tools and data-exploring techniques, I hope that this article can help the public, specially applicants and workers in civil-society organisations to get an update on salaries and trends in the sector. The jobs analysed here are mostly UK-based ones and published by UK-based organisations. Therefore, the results are not meant to represent the entire sector worldwide. I still hope though that this analysis can provide some positive contribuition to the evolution of work of civil-society organisation in both the southern and the northern hemispheres. This post is based on public data, is my sole responsibility and can in no way be taken to reflect the views of CharityJobs' staff.

Modern information technologies in organization of geophysical studies

The features of geo-electrical surveying on permanent geophysical stations and in field conditions are described in this article. Modern approaches to geophysical study with IT-technology application, which require an automatic or semiautomatic data recording, express analysis of current experimental results, in a on-line or off-line mode, automatic data acquisition and transmission by GSM net are represented. Described approaches are realized using ftp-server with appropriate settings on a base of standard server’s tools i. e. LAMPP (Linux, Apache, MySQL, PHP, Python) and web techniques: HTML, CGI, SSI, JavaScript, DHTML, CSS. The described approaches were tested on the permanent geophysical station “Nyzhne Selyshche” (Zakarpatska oblast) for a natural electrical field variations monitoring study and during in field study of the shallow layers resistivity by the transient electromagnetic method (TEM). The express analysis of experimental TEM data is shown and illustrated by screenshots demonstrating the operation of program modules developed and handled by OS Windows. Such application can also be used for any other data as where the mobile Internet is available as well as the off-line mode is possible too.

Knowledge retrieval of historic concepts using semantic web

This paper presents a comprehensive survey on knowledge retrieval of historical concepts using semantic web technologies. The scope of this discussion encompasses research material in the field of computing and humanities including but not limited to Cultural Heritage, Museum and Digital Library research. Semantic web techniques used to describe, store, search, query and retrieve historic concepts such as RDF, RDFs, OWL, and Ontologies are discussed along with other extensively researched methods from domains like Natural Language Processing and Computer Vision. The discussion elaborates on Ontological-enabled information retrieval and the concept of new knowledge discovery. This work presents a comprehensive set of problems identified in the field of information retrieval and their solutions through the semantic web along with an insight in the future directions and the open-ended questions in the domain of retrieval of historic concept.