In GNSS (Global Navigation Satellite System) Distributed Simulation Environment (GDSE), the simulation task could be designed with the sharing models on the Internet. However, too much information and relation of model need to be managed in GDSE. Especially if there is a large quantity of sharing models, the model retrieval would be an extremely complex project. For meeting management demand of GDSE and improving the model retrieval efficiency, the characteristics of service simulation model are analysed firstly. A semantic management method of simulation model is proposed, and a model management architecture is designed. Compared with traditional retrieval way, it takes less retrieval time and has a higher accuracy result. The simulation results show that retrieval in the semantic management module has a good ability on understanding user needs, and helps user obtain appropriate model rapidly. It improves the efficiency of simulation tasks design.

To enable the vision of precision medicine, evidence-based medicine is the key element. Understanding the natural history of complex diseases like brain aneurysm and particularly investigating the evidences of its rupture risk factors relies on the existence of semantic-enabled data preparation technology to conduct clinical trials, survival analysis and outcome prediction. For personalized medicine in the field of neurological diseases, it is very important that multiple health organizations coordinate and cooperate to conduct evidence based observational studies. Without the means of automating the process of privacy and semantic-enabled data preparation to conduct observational studies at intra-organizational level would require months to manually prepare the data. Therefore, this paper proposes a semantic and privacy enabled, multi-party data preparation architecture and a four-tiered semantic similarity algorithm. Evaluation shows that proposed algorithm achieves a precision of 79%, high recall at 83% and F-measure of 81%.

As the manual creation of domain models and also of linked data is very costly, the extraction of knowledge from structured and unstructured data has been one of the central research areas in the Semantic Web field in the last two decades. Here, we look specifically at the extraction of formalized knowledge from natural language text, which is the most abundant source of human knowledge available. There are many tools on hand for information and knowledge extraction for English natural language, for written Thai language the situation is different. The goal of this work is to assess the state-of-the-art of research on formal knowledge extraction specifically from Thai language text, and then give suggestions and practical research ideas on how to improve the state-of-the-art. To address the goal, first we distinguish nine knowledge extraction for the Semantic Web tasks defined in literature on knowledge extraction from English text, for example taxonomy extraction, relation extraction, or named entity recognition. For each of the nine tasks, we analyze the publications and tools available for Thai text in the form of a comprehensive literature survey. Additionally to our assessment, we measure the self-assessment by the Thai research community with the help of a questionnaire-based survey on each of the tasks. Furthermore, the structure and size of the Thai community is analyzed using complex literature database queries. Combining all the collected information we finally identify research gaps in knowledge extraction from Thai language. An extensive list of practical research ideas is presented, focusing on concrete suggestions for every knowledge extraction task - which can be implemented and evaluated with reasonable effort. Besides the task-specific hints for improvements of the state-of-the-art, we also include general recommendations on how to raise the efficiency of the respective research community.

The centralized controller of SDN enables a global topology view of the underlying network. It is possible for the SDN controller to achieve globally optimized resource composition and utilization, including optimized end-to-end paths. Currently, resource composition in SDN arena is usually conducted in an imperative manner where composition logics are explicitly specified in high level programming languages. It requires strong programming and OpenFlow backgrounds. This paper proposes declarative path composition, namely Compass, which offers a human-friendly user interface similar to natural language. Borrowing methodologies from Semantic Web, Compass models and stores SDN resources using OWL and RDF, respectively, to foster the virtualized and unified management of the network resources regardless of the concrete controller platform. Besides, path composition is conducted in a declarative manner where the user merely specifies the composition goal in the SPARQL query language instead of explicitly specifying concrete composition details in programming languages. Composed paths are also reused based on similarity matching, to reduce the chance of time-consuming path composition. The experiment results reflect the applicability of Compass in path composition and reuse.

On an inference-enabled Linked Open Data (LOD) endpoint, usually a query execution takes longer than on an LOD endpoint without inference engine due to its processing of reasoning. Although there are two separate kind of approaches, query modification approaches, and ontology modifications have been investigated on the different contexts, there have been discussions about how they can be chosen or combined for various settings. In this paper, for reducing query execution time on an inference-enabled LOD endpoint, we compare these two promising methods: query rewriting and ontology modification, as well as trying to combine them into a cluster of such systems. We employ an evolutionary approach to make such rewriting and modification of queries and ontologies based on the past-processed queries and their results. We show how those two approaches work well on implementing an inference-enabled LOD endpoint by a cluster of SPARQL endpoints.

Linked Open Data (LOD) at Schema-Level and knowledge described in Chinese is an important part of the LOD project. Previous work generally ignored the rules of word-order sensitivity and polysemy in Chinese or could not deal with the out-of-vocabulary (OOV) mapping task. There is still no efficient system for large-scale Chinese ontology mapping. In order to solve the problem, this study proposes a novel TongYiCiCiLin (TYCCL) and Sequence Alignment-based Chinese Ontology Mapping model, which is called TongSACOM, to evaluate Chinese concept similarity in LOD environment. Firstly, an improved TYCCL-based similarity algorithm is proposed to compute the similarity between atomic Chinese concepts that have been included in TYCCL. Secondly, a global sequence-alignment and improved TYCCL-based combined algorithm is proposed to evaluate the similarity between Chinese OOV. Finally, comparing the TongSACOM to other typical similarity computing algorithms, and the results prove that it has higher overall performance and usability. This study may have important practical significance for promoting Chinese knowledge sharing, reusing, interoperation and it can be widely applied in the related area of Chinese information processing.

Linked data endpoints are online query gateways to semantically annotated linked data sources. In order to query these data sources, SPARQL query language is used as a standard. Although a linked data endpoint (i.e. SPARQL endpoint) is a basic Web service, it provides a platform for federated online querying and data linking methods. For linked data consumers, SPARQL endpoint availability and discovery are crucial for live querying and semantic information retrieval. Current studies show that availability of linked datasets is very low, while the locations of linked data endpoints change frequently. There are linked data respsitories that collect and list the available linked data endpoints or resources. It is observed that around half of the endpoints listed in existing repositories are not accessible (temporarily or permanently offline). These endpoint URLs are shared through repository websites, such as Datahub.io, however, they are weakly maintained and revised only by their publishers. In this study, a novel metacrawling method is proposed for discovering and monitoring linked data sources on the Web. We implemented the method in a prototype system, named SPARQL Endpoints Discovery (SpEnD). SpEnD starts with a “search keyword” discovery process for finding relevant keywords for the linked data domain and specifically SPARQL endpoints. Then, the collected search keywords are utilized to find linked data sources via popular search engines (Google, Bing, Yahoo, Yandex). By using this method, most of the currently listed SPARQL endpoints in existing endpoint repositories, as well as a significant number of new SPARQL endpoints, have been discovered. We analyze our findings in comparison to Datahub collection in detail.

The automation of traceability links or traceability matrices is important to many software development paradigms. In turn, the efficiency and effectiveness of the recovery of traceability links in the distributed software development is becoming increasingly vital due to complexity of project developments, as this include continuous change in requirements, geographically dispersed project teams, and the complexity of managing the elements of a project - time, money, scope, and people. Therefore, the traceability links among the requirements artifacts, which fulfill business objectives, is also critical to reduce the risk and ensures project‘s success. This paper proposes Autonomous Decentralized Semantic based Traceability Link Recovery (AD-STLR) architecture. According to best of our knowledge this is the first architectural approach that uses an autonomous decentralized concept, DBpedia knowledge-base, Babelnet 2.5 multilingual dictionary and semantic network, for finding similarity among different project artifacts and the automation of traceability links recovery.

Mapping instances to the Linked Open Data (LOD) cloud plays an important role for enriching information of instances, since the LOD cloud contains abundant amounts of interlinked instances describing the instances. Consequently, many techniques have been introduced for mapping instances to a LOD data set; however, most of them merely focus on tackling with the problem of heterogeneity. Unfortunately, the problem of the large number of LOD data sets has yet to be addressed. Owing to the number of LOD data sets, mapping an instance to a LOD data set is not sufficient because an identical instance might not exist in that data set. In this article, we therefore introduce a heuristic expansion based framework for mapping instances to LOD data sets. The key idea of the framework is to gradually expand the search space from one data set to another data set in order to discover identical instances. In experiments, the framework could successfully map instances to the LOD data sets by increasing the coverage to 90.36%. Experimental results also indicate that the heuristic function in the framework could efficiently limit the expansion space to a reasonable space. Based upon the limited expansion space, the framework could effectively reduce the number of candidate pairs to 9.73% of the baseline without affecting any performances.

The growing trends in Internet usage for data and knowledge sharing calls for dynamic classification of web contents, particularly at the edges of the Internet. Rather than considering Linked Data as an integral part of Big Data, we propose Autonomous Decentralized Semantic-based Content Classifier (ADSCC) for dynamic classification of unstructured web contents, using Linked Data and web metadata in Content Delivery Network (CDN). The proposed framework ensures efficient categorization of URLs (even overlapping categories) by dynamically mapping the changing user-defined categories to ontologies' category/classes. This dynamic classification is performed by the proposed system that mainly involves three main algorithms/modules: Dynamic Mapping algorithm, Autonomous coordination-based Inference algorithm, and Context-based disambiguation. Evaluation results show that the proposed system achieves (on average), the precision, recall and F-measure within the 93-97% range.

The objective of this paper is to recognize and classify the poses of idols in still images on the web. The poses found in Japanese idol photos are often complicated and their classification is highly challenging. Although advances in computer vision research have made huge contributions to image recognition, it is not enough to estimate human poses accurately. We thus propose a method that refines result of human pose estimation by Pose Guide Ontology (PGO) and a set of energy functions. PGO, which we introduce in this paper, contains useful background knowledge, such as semantic hierarchies and constraints related to the positional relationship between body parts. Energy functions compute the right positions of body parts based on knowledge of the human body. Through experiments, we also refine PGO iteratively for further improvement of classification accuracy. We demonstrate pose classification into 8 classes on a dataset containing 400 idol images on the web. Result of experiments shows the efficiency of PGO and the energy functions; the F-measure of classification is 15% higher than the non-refined results. In addition to this, we confirm the validity of the energy functions.

In recent years, there has been a significant growth in the importance of data mining of graph-structured data due to this technology's rapid increase in both scale and application areas. Many previous studies have investigated decision tree learning on Semantic Web-based linked data to uncover implicit knowledge. In the present paper, we suggest a new random forest algorithm for linked data to overcome the underlying limitations of the decision tree algorithm, such as local optimal decisions and generalization error. Moreover, we designed a parallel processing environment for random forest learning to manage large-size linked data and increase the efficiency of multiple tree generation. For this purpose, we modified the previous candidate feature searching method of the decision tree algorithm for linked data to reduce the feature searching space of random forest learning and developed feature selection methods that are adjusted to linked data. Using a distributed index-based search engine, we designed a parallel random forest learning system for linked data to generate random forests in parallel. Our proposed system enables users to simultaneously generate multiple decision trees from distributed stored linked data. To evaluate the performance of the proposed algorithm, we performed experiments to compare the classification accuracy when using the single decision tree algorithm. The experimental results revealed that our random forest algorithm is more accurate than the single decision tree algorithm.

The Semantic Web uses RDF/RDFS, which can enable a machine to understand web data without human interference. But most web data is not available in RDF/RDFS documents because most web data is still stored in databases. It is much more favorable to use stored data in a database to build the Semantic Web. This paper proposes an enhanced relational RDF/RDFS interoperable data model (ER2iDM) and a transformation procedure from relational data model (RDM) to RDF/RDFS based on ER2iDM. The ER2iDM is a data model that plays the role of an inter-mediator between RDM and RDF/RDFS during a transformation procedure. The data and schema information in the database are migrated to the ER2iDM according to the proposed translation procedures without incurring loss of meaning of the entities, relationships, and data. The RDF/RDFS generation tool makes a RDF/RDFS XML document automatically from the ER2iDM. The proposed ER2iDM and transformation procedure provides detailed guidelines for transformation from RDM to RDF/RDFS unlike existing studies; therefore, we can more efficiently build up the Semantic Web using database stored data.

The Linking Open Data (LOD) cloud is a collection of linked Resource Description Framework (RDF) data with over 31 billion RDF triples. Accessing linked data is a challenging task because each data set in the LOD cloud has a specific ontology schema, and familiarity with the ontology schema used is required in order to query various linked data sets. However, manually checking each data set is time-consuming, especially when many data sets from various domains are used. This difficulty can be overcome without user interaction by using an automatic method that integrates different ontology schema. In this paper, we propose a Mid-Ontology learning approach that can automatically construct a simple ontology, linking related ontology predicates (class or property) in different data sets. Our Mid-Ontology learning approach consists of three main phases: data collection, predicate grouping, and Mid-Ontology construction. Experiments show that our Mid-Ontology learning approach successfully integrates diverse ontology schema with a high quality, and effectively retrieves related information with the constructed Mid-Ontology.

The development of ontology at the instance level requires the extraction of the terms defining the instances from various data sources. These instances then are linked to the concepts of the ontology, and relationships are created between these instances for the next step. However, before establishing links among data, ontology engineers must classify terms or instances from a web document into an ontology concept. The tool for help ontology engineer in this task is called ontology population. The present research is not suitable for ontology development applications, such as long time processing or analyzing large or noisy data sets. OntoPop system introduces a methodology to solve these problems, which comprises two parts. First, we select meaningful features from syntactic relations, which can produce more significant features than any other method. Second, we differentiate feature meaning and reduce noise based on latent semantic analysis. Experimental evaluation demonstrates that the OntoPop works well, significantly out-performing the accuracy of 49.64%, a learning accuracy of 76.93%, and executes time of 5.46 second/instance.

This paper proposes a new semantics that characterizes the time and/or situation dependencies of properties, together with the ontological notion of existential rigidity. For this purpose, we present order-sorted tempo-situational logic (OSTSL) with rigid/anti-rigid sorts and an existential predicate. In this logic, rigid/anti-rigid sorted terms enable the expressions for sortal properties, and temporal and situational operators suitably represent the ontological axioms of existential rigidity and time and/or situation dependencies. A specific semantics of OSTSL adheres to the temporal and situational behaviors of properties based on existential rigidity. As a result, the semantics guarantees that the ontological axioms of properties expressed by sorted tempo-situational formulas are logically valid.

Metadata registry (MDR) is based on the international standard ISO/IEC 11179. The committee of ISO/IEC JTC 1/SC 32, which had standardized the MDR, has started to improvise the MDR, and the improvised version is named extended MDR (XMDR). However, the XMDR does not fully support the ontology concept, and no method is available for mapping ontology registrations onto registries. To overcome the limitations of the outdated XMDR, this paper proposes an extended XMDR (XMDR+) framework. The XMDR+ framework provides a method for mapping of ontology registrations between the metadata registry and ontologies. To improve the functions of the XMDR, we have proposed herein a framework that is capable of defining a model that manages the relations not only among ontological concepts but also among instances, and guarantees the management and storage of their relationships for supporting valid relations of the ontologies.

An expectation for more intelligent Web is recently being reflected through the new research field called Semantic Web. In this paper, related with Semantic Web security, we introduce an RDF triple based access control model having explicit authorization propagation by inheritance and implicit authorization propagation by inference. Especially, we explain an authorization conflict problem between the explicit and the implicit authorization propagation, which is an important concept in access control for Semantic Web. We also propose a novel conflict detection algorithm using graph labeling techniques in order to efficiently find authorization conflicts. Some experimental results show that the proposed detection algorithm has much better performance than the existing detection algorithm when data size and number of specified authorizations become larger.

We propose an enhanced method for translating Topic Maps to RDF/RDF Schema, to realize the Semantic Web. A critical issue for the Semantic Web is to efficiently and precisely describe Web information resources, i.e., Web metadata. Two representative standards, Topic Maps and RDF have been used for Web metadata. RDF-based standardization and implementation of the Semantic Web have been actively performed. Since the Semantic Web must accept and understand all Web information resources that are represented with the other methods, Topic Maps-to-RDF translation has become an issue. Even though many Topic Maps to RDF translation methods have been devised, they still have several problems (e.g. semantic loss, complex expression, etc.). Our translation method provides an improved solution to these problems. This method shows lower semantic loss than the previous methods due to extract both explicit semantics and implicit semantics. Compared to the previous methods, our method reduces the encoding complexity of resulting RDF. In addition, in terms of reversibility, the proposed method regenerates all Topic Maps constructs in an original source when is reverse translated.

In order to timely response to a user query at run-time, next generation Semantic Web applications demand a robust mechanism to dynamically select one or more existing ontologies available on the Web and combine them automatically if needed. Although existing ontology retrieval systems return a lengthy list of resultant ontologies, they cannot identify which ones can completely meet the query requirements nor determine a minimum set of resultant ontologies that can jointly satisfy the requirements if no single ontology is available to satisfy them. Therefore, this paper presents an ontology retrieval system, namely combiSQORE, which can return single or combinative ontologies that completely satisfy a submitted query when the available ontology database is adequate to answer such query. In addition, the proposed system ranks the returned results based on their semantic similarities to the given query and their modification (integration) costs. The experimental results show that combiSQORE system yields practical combinative ontologies and useful rankings.