Semantic Resource Description Research Articles

Allocation of applications to Fog resources via semantic clustering techniques: with scenarios from intelligent transportation systems

The fast development in IoT and Cloud technologies has propelled the emergence of a variety of computing paradigms, among which Fog and Edge computing are salient computing technologies. Such new paradigms are opening up new opportunities to implement novel application scenarios, not possible before, by supporting features of mobility, edge intelligence and end-user support. This, however, comes with new computing challenges. One such challenge is the allocation of applications to Fog and Edge nodes. Indeed, for some application scenarios larger computing capacity might be needed. Therefore, due to co-existence of computing devices of different computing granularity, techniques for grouping up and clustering resources into virtual nodes of larger computing capacity are required. In this paper we present some clustering techniques for creating virtual computing nodes from Fog/Edge nodes by combining semantic description of resources with semantic clustering techniques. Then, we use such clusters for optimal allocation (via heuristics and Liner Programming) of applications to virtual computing nodes. Simulation results are reported to support the feasibility of the model and efficacy of the proposed approach. First Fit Heuristic Algorithm (FFHA) outperformed ILP method for medium and large size instances. Likewise, FFHA performed more consistently than ILP on various experimental setting. Finally, the results showed that the proposed clustering techniques deliver relatively fast response times, while enabling the service of a larger number of applications, with more demanding requirements.

A New Semantic Resource Description Framework for Medical Diagnosis based on Gene Ontology

Semantic-Based medical informatics system is an interdisciplinary deflect of bioinformatics research, where a dealing to knowledge discovery of proper diagnosis of disease leads to more attention about health control before health complications. To do this work is in the aspects of various ways like preserving and accessing patient’s medical records for further information retrieval from pre-recorded datasets. Now some research has mostly used to medical metrics method combined with statistical information and social network analysis citation approach to analyze. Due to this limited citation information force to develop a new dimension of the semantic web-based medical diagnosis system. In this study the approach of implementing autonomic gene computing in medical informatics, in this framework a new semantic resource description network for medical diagnostics based on Gene Ontology, this way the diagnosis context capture its levels of symptoms. Every patient has their unique symptoms which are kept and fetched by semantic web applications, In this framework we used gene-based symptom indication for disease diagnosis, and merge with drug prescription leads to more suitable results that can be useful for physicians and medical practitioner, as to achieve efficient solutions in the new direction to make life saving analysis from the database. Being a very effective and enhanced web application tool can be applied in the medical field to make it useful for the society. The web interface for drugs prescription accepts the diseases and symptoms from the domain updated in the database and successfully displays prescribed drugs for each disease.

A Semantic Web query Optimization Using Resource Description Framework

Abstract Semantic web mining can be defined as the problem of automatic extraction of information from the database and produce a structured output to the user queries. Most works in knowledge base system have focused on using axioms applying enrichment method, but was not efficient in detecting the document topic and hence resulted with the expanded queries. Although there is a straight forward approach for automatic extraction from a larger set of information, the problem of recognizing the second or higher order associations between the documents on the web still remains a challenging issue. Specifically, we present three folds architecture to process a class of well designed user queries based on Reuters-21578 Text Categorization Collection Data Set, called as Semantic Resource Description with Compound Protocol (SRD-CP). The first fold constructs a Semantic Pattern Tree based on the Resource Description Framework (RDF) query language for detecting the document topic name. The RDF query language uses compound structure to produce the result to the expanded user queries. The second fold is the design of compound structure in SRD-CP framework that consists of HTTP protocol and constrained application protocol to handle the complex query processing. Finally, the third fold includes the definition of word co-occurrences using the association frequency form with the help of the constructed SPT. Run Associate Apriori (RAA) algorithm is used to identify the second or higher order association between the web documents. The RAA algorithm in SRD-CP framework recognizes the association between web related documents and user fetched queries. SRD-CP framework is empirically evaluated using the dataset and is shown to be significantly more efficient in terms of document association level computation rate, processing time and user result retrieval rate.

Semantic indexing modelling of resources within a distributed system

This paper is concerning issues regarding the semantic indexing of resources in a peerto- peer network. An important requirement is a good quality of the discovery (i.e. the selection of results very related to a specific request) and a short response time. Keys used for indexing and the corresponding URLs of indexed resources are stored in a Distributed Hash Table (DHT) scattered among the different peers of a community. A key of indexing is a semantic description of resources and refers to elements belonging to ontologies of domain and to knowledge bases. We propose a model of indexing patterns in order to generate keys. It is used to guide users for producing assertions about their resources. It induces navigation inside ontologies presented in graphical user interfaces in a friendly and easy to use way. With different examples, we present the main cases of key generation that define the indexing context of a resource. We also describe experiments performed in test environments simulating various situations.

DL-LINK: A CONCEPTUAL CLUSTERING ALGORITHM FOR INDEXING DESCRIPTION LOGICS KNOWLEDGE BASES

Efficient resource retrieval is a crucial issue, particularly when semantic resource descriptions are considered which enable the exploitation of reasoning services during the retrieval process. In this context, resources are commonly retrieved by checking if each available resource description satisfies the given query. This approach becomes inefficient with the increase of available resources. We propose a method for improving the retrieval process by constructing a tree index through a new conceptual clustering method for resources expressed as class definitions or as instances of classes in ontology languages. The available resource descriptions are located at the leaf nodes of the index, while inner nodes represent intensional descriptions (generalizations) of their child nodes. The retrieval is performed by following the tree branches whose nodes satisfy the query. Query answering time may be improved as the number of retrieval steps may be O(log n) in the best case.

Ontology Instance Matching based MPEG-7 Resource Integration

Heterogeneous multimedia contents are annotated by a sharable formal conceptualization, often called ontology, and these contents, regardless of their media, become sharable resources/instances. Integration of the sharable resources and acquisition of diverse knowledge is getting researchers’ attention at a rapid pace. In this regard, MPEG-7 standard convertible to semantic Resource Description Framework (RDF) evolves for containing structured data and knowledge sources. In this paper, the authors propose an efficient approach to integrate the multimedia resources annotated by the standard of MPEG-7 schema using ontology instance matching techniques. MPEG-7 resources are usually specified explicitly by their surrounding MPEG-7 schema entities, e.g., concepts and properties, in conjunction with other linked resources. Therefore, resource integration needed schema matching as well. In this approach, the authors obtained the schema matching using their scalable ontology alignment algorithm and collected the semantically linked resources, referred to as the Semantic Link Cloud (SLC) collectively for each of the resources. Techniques were addressed to solve several data heterogeneity: value transformation, structural transformation and logical transformation. These experiments show the strength and efficiency of the proposed matching approach.

Contextualized query sampling to discover semantic resource descriptions on the web

Resource description extracted by query-sampling method can be applied to determine which database sources a certain query should be firstly sent to. In this paper, we propose a contextualized query-sampling method to extract the resources which are most relevant to up-to-date context. Practically, the proposed approach is adopted to personal crawler systems (the so-called focused crawlers), which can support the corresponding user’s web navigation tasks in real-time. By taking into account the user context (e.g., intentions or interests), the crawler can build the queries to evaluate candidate information sources. As a result, we can discover semantic associations (i) between user context and the sources, and (ii) between all pairs of the sources. These associations are applied to rank the sources, and transform the queries for the other sources. For evaluating the performance of contextualized query sampling on 53 information sources, we compared the ranking lists recommended by the proposed method with user feedbacks (i.e., ideal ranks), and also computed the precision of discovered subsumptions as semantic associations between the sources.