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Knowledge sharing is the main activity of knowledge management and it enables the concept of collective intelligence. Social platform as Web 2.0 allows users to easily share information in online communities. However, most shared information is represented in unstructured format so that it impacts to effective information usage (i.e. low search precision, lack of intelligent way of information usage). Semantic web technology makes it possible to address the above problems by including the meaning to the information. Furthermore, information could be represented in a machine readable format. This offers potential for development of mechanisms to enhance the precision of search and provide information recommendation to users. This paper proposed a knowledge-sharing system used for enabling effective academic paper sharing used in a seminar course. Ontology is a knowledge model for describing user profile and seminar paper information. Its vocabulary was used as metadata for annotating information. Semantic search engine and recommendation engine were implemented to support students on seminar paper usage. The user evaluation found that the system reaches the high level of satisfaction for recommendation capability and system's value added. In addition it also reaches the highest satisfaction for retrieval effectiveness. [ABSTRACT FROM PUBLISHER]

Extensible Business Reporting Language (XBRL) is a standard for the electronic exchange of data between businesses and on the Internet. XBRL Taxonomies are the dictionaries which the language uses. Many different organizations, including regulators, specific industries or even companies, may require their own taxonomies to cover their business reporting needs. They are generally heterogeneous, which severely hampers the interoperability and comparability of the XBRL instances. This paper first gives the representation of a taxonomy using formal ontology. Then we define the concept-level semantic differences between Chinese two taxonomies which is developed by Shanghai Stock Exchange and Shen Zhen Stock Exchange, respectively. There are label and meaning conflictions, which can be resolved through concept mapping. This paper could be a basis to reconcile semantic conflictions among heterogeneous taxonomies. [ABSTRACT FROM PUBLISHER]

The bursting of the dot-com bubble marked a turning point for the web. New kinds of websites sprung up and brought users different experiences, especially in user participation and openness. But academic journals have placed less emphasis on applications of Web 2.0 for business use. This paper tries to develop the business model of these new kinds of websites using ontology approach, which stresses the importance of the social network. Furthermore, this paper suggests using this business model to guide the development of the Web 2.0 website. [ABSTRACT FROM PUBLISHER]

In this paper, the representation method of user interest model is a combination of vector space model with Ontology. It introduces the algorithm of semantic similarity when calculating the term weight, and considers the semantic similarity of words, and then the term weight is updated through the Bayesian network. This paper focuses on the steps of building and updating the user interest model, and verified the validity of the model by experiment. [ABSTRACT FROM PUBLISHER]

In the present paper we describe an ontology-based service platform to efficiently manage and maintain the explosive growth scientist knowledge. Due to their powerful knowledge representation formalism and associated inference mechanisms, ontology-based systems are emerging as a natural choice for the next generation of knowledge management systems [1]. Scientist service platform is designed to support a convenient and rapid way to get accurate and comprehensive information online. For the characteristics of the scientist knowledge and the large scale of the dataset, the proposed platform relies on scientist ontology, and takes Allegro Graph as database and then we explain the process of modeling the ontology-based platform with the dataset of scientist knowledge of Chinese Academy of Sciences. The platform can achieve a better performance than the keyword based information retrieval. [ABSTRACT FROM PUBLISHER]

Phylogenetic analyses can resolve historical relationships among genes, organisms or higher taxa. Understanding such relationships can elucidate a wide range of biological phenomena including the role of adaptation as a driver of diversification, the importance of gene and genome duplications in the evolution gene function, or the evolutionary consequences of biogeographic shifts. The variety of methods of analysis and data types typically employed in phylogenetic analyses can pose challenges for semantic reasoning due to significant representational and computational complexity. These challenges could be ameliorated with the development of an ontology designed to capture and organize the variety of concepts used to describe phylogenetic data, methods of analysis and the results of phylogenetic analyses. In this paper, we discuss the development of PhylOnt — an ontology for phylogenetic analyses, which establishes a foundation for semantics-based workflows including meta-analyses of phylogentic data and trees. PhylOnt is an extensible ontology, which describes the methods employed to estimate trees given a data matrix, models and programs used for phylogenetic analysis and descriptions of phylogenetic trees including branch-length information and support values. The relational vocabulary included in PhylOnt will facilitate the integration of heterogeneous data types derived from both structured and unstructured sources. To illustrate the utility of PhylOnt, we annotated scientific literature to support semantic search. The semantic annotations can subsequently support workflows that requiring the exchange and integration of heterogeneous phylogenetic information. [ABSTRACT FROM PUBLISHER]

In the past twenty years, online education has grown as a main part of current education. However, the unambiguous and uniform expression of online education is a crucial challenge. Since ontology is becoming a recognized vehicle for knowledge reuse, knowledge sharing and modeling, online education(OE_Onto) is designed by using ABC model as upper ontology in order to solve these problems. OE_Onto can provide the shared knowledge in semantics to achieve the cooperation and reuse between people and the different online education systems. In this paper, OE_Onto is respectively described in five-tuple involving the concepts, relations, functions, axioms and instances. Finally, the experiment of an online education process is completed to validate OE_Onto. [ABSTRACT FROM PUBLISHER]

Event plays an important role in the field of semantic understanding that associates entities. Although traditional ontology is an effective model to represent domain concept hierarchies and semantics, it simply considers event same as static concept or describes it as relationship. This paper proposes a layered event ontology model which emphasis on event and layered the traditional ontology by excluding events from concepts have the static characterization, describing them at different levels respectively, meanwhile tie them by element relationship. It finally suggests the method to construct layered event ontology from existent ontology which is under the traditional model, and provides an example to explain it. [ABSTRACT FROM PUBLISHER]

Virtual communities are becoming importance in many organizations, particularly in the institute of higher learning (IHL). Research communities are common practice in many IHLs, which are form based on similar research interest or research area. With proper internet infrastructure, the research communities formed virtual communities. However, the virtual communities of research communities are lacking in proper management in managing groups and managing groups' knowledge. Ontology is known to support standardization of the terms used to represent knowledge in a particular domain and ontology design is useful to model virtual communities. Finding the right people to collaborate is the main Knowledge Management (KM) perspective that we adopted in our study. But, the current situation of research communities has not met the KM perspective that we adopted due to the limitation of channel in finding the right people to work together. The objectives of the paper are: 1) to classify virtual communities in research communities of IHL and 2) to model virtual communities based on similar research interest using ontology. In order to achieve the objective, we are using ontology to classify and to model virtual communities based on related work. Finally, the outcome of the paper can be used to model virtual communities of another IHL. [ABSTRACT FROM PUBLISHER]

On these issues unified representation of urban transport information using urban transport ontology, it defines the statute and the algebraic operations of semantic fusion in ontology level in order to achieve the fusion of urban traffic information in the semantic completeness and consistency. Thus this paper takes advantage of the semantic completeness of the ontology to build urban traffic ontology model with which we resolve the problems as ontology mergence and equivalence verification in semantic fusion of traffic information integration. Information integration in urban transport can increase the function of semantic fusion, and reduce the amount of data integration of urban traffic information as well enhance the efficiency and integrity of traffic information query for the help, through the practical application of intelligent traffic information integration platform of Changde city, the paper has practically proved that the semantic fusion based on ontology increases the effect and efficiency of the urban traffic information integration, reduces the storage quantity, and improve query efficiency and information completeness. [ABSTRACT FROM PUBLISHER]

Mapping relational database schema to ontology is a critical task for ontology-based heterogeneous database integration. This paper proposes a hybrid approach combining element-level and structure-level techniques to discover the correspondences between relational database schema and ontology. In the process of element-level matching, string-based and Word Net sense-based techniques are combined to calculate the name similarity between elements. This paper analyzes different characteristics exhibited by matching element pairs of various types and describes how to use these characteristics to calculate the structure similarity. A series of experiments indicate that our approach performs better than the previous ones. [ABSTRACT FROM PUBLISHER]

SPARQL is a standard query language for RDF data that are commonly used to represent and store Semantic Web data. There are a lot of SPARQL endpoints to retrieve and see the data by SPARQL queries. Although it greatly helps us query semantic data with ontologies, their diversity of ontologies make it difficult to query the data without understanding of their target ontologies. Although it is possible to use ontology mapping techniques to convert a query based on an ontology to another one based on another ontology, it is assumed to be able to define complete mappings among the two ontologies without any loss of their semantics. In this paper, we present an approach about SPARQL query rewriting when ontology mappings are not complete. We show a way of rewriting a SPARQL query with one ontology to another query on a different ontology when we can specify an ordering based on their mapping reliability. [ABSTRACT FROM PUBLISHER]

Constructing simulation scenario domain ontology is an important means in the repetitive use and mutual operation of simulation scenario domain modeling. This paper suggests an appropriate SS-Onto construction method on the basis of the advantages possessed by ontology and the studies already done in this field. The paper will serve as a foundation for the further construction of SS-Onto (semi-) automation and the application of ontology. [ABSTRACT FROM PUBLISHER]

Semantic Web as an evolution of Web has led to the introduction of new technologies including XML-based formats of representing data on the Web: resource description framework (RDF) and ontology. Similarity assessment of the entities has a fundamental role in processing and analyzing data represented in ontology. In this paper, we propose a technique for determining semantic similarity between pieces of information defined in ontology based on features describing each piece of information. The presented method allows for considering a specific context into the similarity evaluation. The quantitative characterization of similarity at different levels of abstraction in ontology is provided using elements of fuzzy set theory. We show through experiments that the proposed method compares favorably to other measures in terms of human judgment of similarity. [ABSTRACT FROM PUBLISHER]

Data-to-decision systems must fuse information from heterogeneous sources to infer a high-level understanding of a situation. A high degree of confidence in the inferred knowledge is necessary for appropriate actions to be taken based upon the assessment of a situation. This paper presents an extensible Semantic Web compatible framework that uses rich ontological descriptions for the autonomous and human-aided fusion of heterogeneous sensors and algorithms to create evidence-based hypotheses of a situation under persistent surveillance. Raw data acquired from profiling sensors is combined with the output of visualization and classification algorithms, yielding information with a higher degree of confidence than what would be obtained without the fusion process. The framework can readily accommodate other data sources and algorithms into the fusion process. [ABSTRACT FROM PUBLISHER]

Semantic networks can simulate the human complex frames in reasoning process providing efficient association and inference mechanisms. Ontology can be used to fill the gap between human and computational intelligence for a task domain. For an evolving environment it is important to understand what knowledge is required for a task domain with an adaptive ontology matching. To reflect the evolving knowledge this paper considers ontologies based on folksonomies according to a new concept structure called “Folksodriven” to represent folksonomies. Folksonomies are a set of terms that a group of users tagged content without a controlled vocabulary. A Folksodriven Structure Network (FSN), built from the relations among the Folksodriven tags, is presented as a folksonomy tags suggestions for the user to solve the problems inherent in an uncontrolled vocabulary of the folksonomy. It was observed that the properties of the FSN depend mainly on the nature, distribution, size and the quality of the reinforcing Folksodriven tags (FD tags). So, the studies on the transformational regulation of the FD tags are regarded to be important for an adaptive folksonomies classifications in an evolving environment used by Intelligent Systems. This paper discuss the deformation exhibiting linear behavior on FSN based on folksonomy tags chosen by different user on web site resources, this is a topic which has not been well studied so far. The discussion shows that the linear elastic constitutive equation possesses some leaning for the investigation. A constitutive law on FSN is investigated towards a systematic mathematical analysis on stress analysis and equations of motion for an evolving ontology matching on an environment defined by the users' folksonomy choice. The adaptive ontology matching and the elastodynamics are merged to obtain what we can call the elasto-adaptative-dynamics methodology of the FSN. [ABSTRACT FROM PUBLISHER]

With the development of Web Service Technology, the quantity of the web services published on the Internet is increasing rapidly. Recognizing each web service intelligently becomes the key of efficiently using Internet. And the first step of recognization is to classify the web services accurately. To classify a huge amount of web services becomes a difficulty job. Therefore, in order to support applications of web services more effectively, an automatic web service classification method is needed. In this paper, the common WSDL files are regarded as the study object. Since web service is described by WSDL, the traditional document classification method cannot be applied directly. In the paper, a new method is proposed which applies automatic web service semantic annotation and uses three classification method: Naïve Bayes, SVM and REP Tree, furthermore ensemble learning is applied. According to the experiment done on 951 WSDL files and 19 categories, the accuracy was 87.39%. [ABSTRACT FROM PUBLISHER]

Technology has more often been associated with improvements in many domains. This is particularly true in the medical and health-care industry. This is a field where data collection is performed on a daily basis. With the advent of mobile technology, several methodologies for data collection have been adopted reducing the cost and time expanded to data collection. The focus of this paper is a proposed ontology-based framework that has the ability of building a shared repository of surveys that can be used for data collection. The paper discusses iCollect, a first instantiation of the framework in the form of a survey application built for the Indigenous Health Adaptation to Climate Change (IHACC) project. [ABSTRACT FROM PUBLISHER]

One of the possibilities for managing XML data is the use of relational databases. However, the structure of XML documents can vary immensely, even when the data describe concepts from the same application domain. Such heterogeneity makes the mapping process from XML to a relational schema harder. To solve this issue, we present an ontology-based approach for generating and for representing equivalences between XML data and relational schema. The proposal is part of the X2Rel framework, an environment for storing, querying, and managing heterogeneous XML data in relational databases. [ABSTRACT FROM PUBLISHER]

Ontology is the backbone of the semantic web and can overcome semantic barriers. A domain ontology provides a common understanding of the knowledge of a particular domain. Information Science, meanwhile, is an interdisciplinary science that is yet to be defined. It is necessary to develop an Ontology of Information Science (OIS) to represent the knowledge in the field. This paper presents a representation of specific domain knowledge by providing a definition, scope, and boundaries of Information Science (IS). The OIS has fourteen facets: actors, method, practice, studies, mediate, kinds, domains, resources, legislation, philosophy and theories, societal, tools, time and space. The methodology followed is Methontology, which is based on the IEEE standard for the development of a software life-cycle process. The paper then discusses the OIS ontology, particularly its structure. [ABSTRACT FROM PUBLISHER]

With the increasing needs for the world wide enterprises to integrate, share and visualize data from various heterogeneous, autonomous and distributed sources data and Web data covering a given domain, the development of integration and reconciliation solutions becomes a challenging issue. The existing studies on data integration and reconciliation of results have been developed in an isolated way and did not consider the strong integration between these two processes. On one hand, ontologies were largely used for building automatic integration systems due to their ability to reduce schematic and semantic heterogeneities that may exist among sources. On the other hand, reconciliation of results is performed either by considering that all sources use the same identifier for an instance or by means of statistical methods that identify affinities between concepts. These reconciliation solutions are not usually suitable for real-world sensitive-applications where exact results are required and where each source may use a different identifier for the same concept. In this paper, we propose a methodology that simultaneously integrate source data and reconciliate their instances based on ontologies enriched with functional dependencies (FD) in a mediation architecture. The presence of FD gives more autonomy to sources when choosing their primary keys and facilitates the result reconciliation. This methodology is experimented using the Lehigh University Benchmark (LUBM) dataset to show its scalability and the quality of the reconciliation result phase. [ABSTRACT FROM PUBLISHER]

At present, there are a large number of data stored in spreadsheets. With the development of semantic web and ontology technology, importing the spreadsheet data into semantic web is helpful for knowledge sharing and acquisition. Existing conversion methods always convert the spreadsheet data into RDF, which cannot be integrated into constructed ontology base. This paper proposed an open architecture called Anyt2OWL for the conversion from various traditional formats to OWL based on ontology schema. Following Any2OWL, the method of converting spreadsheet to OWL which can be imported into ontology base is implemented. In addition, a declarative language is designed to express the mappings between spreadsheet and ontology. [ABSTRACT FROM PUBLISHER]

Peer review is the most important aspect of the project evaluation. The review comments of evaluation experts are important foundation for scientific decision-making. Therefore, how to select proper peer review experts is the key issue. This paper proposes an approach to select experts in a semantic way. Based on the semantic web technologies, an architecture is presented with an expert knowledge base and an expert semantic model. After the analysis of possible social relations between project applicants and evaluation experts, the expert semantic model is illustrated. Finally, a semantic query tool prototype is designed to show the effectiveness of the proposed approach. This tool will help to improve scientificalness, fairness and normativeness of project evaluations. [ABSTRACT FROM PUBLISHER]

It is very common that various people share same name or a person has various names, which causes person name ambiguities. Existing methods group documents containing person names into various clusters and each cluster refers to an individual, while which target individual a person name occurring in a document refers to is still a challenged problem. In this paper, we firstly construct a person ontology with lots of person instances. Then, an approach of Chinese person name disambiguation based on the person ontology is presented: (1) Extract relevant personal features of a person name contained in a Chinese document, and (2) An improved person ontology tree matching algorithm is suggested to calculate similarities between the individual the person name represents and one of person instances sharing same name, rank the similarities and the top one is returned. To verify the validity of the proposed approach, three experiments are conducted on real web documents. Experimental results show that the proposed approach not only can effectively distinguish Chinese person name, but also can determine an individual for the person name. [ABSTRACT FROM PUBLISHER]

Due to the increasing amount of data Available online, the World Wide Web has becoming one of the most valuable resources for information retrievals and knowledge discovery. Web mining technologies (usually divided into Content mining, Structure mining and Usage mining) are the right solutions for knowledge discovery on WWW. In fact the work depends on two essential issues: One is the knowledge itself, which means analyze what's the required information; the other is how does machine know the requirement well, which means to realize a feasible method for computation and the complex semantic measurement. This paper aimed to discuss three aspects of knowledge we defined: Content, structure and Prior. It means knowledge discovery on WWW should consider content features, structure relations and Priors from background simultaneously. A practice of Wiki as ontology also proposed in this paper. The multiuser writing system will bring chance as large corpus, we applied the linked data for construction of a dynamic semantic network. And which can be used in short text computation such as query expansion and so on. For the consideration of swarm intelligence the key issues and lessons are given in this paper, linked data such as wiki will provide chances and challenges for computability in the future. [ABSTRACT FROM PUBLISHER]

This paper describes a two year case study in the engineering and deployment of a Clinical Information System (CIS) called Palliative Care Information System (PAL-IS) for managing and monitoring team-based community care of palliative patients. The case study followed the methodology, architecture and ontology proposed in previous work to address workflow, behavioral and technology issues for CIS that support collaborative, mobile, and accessible healthcare. Both PAL-IS and the methodology used in its development are evaluated. The results give fresh insight into interoperability issues which complicate CIS design. They also highlight the importance of reporting requirements as a major driver for investment in CIS and a critical factor in CIS design. [ABSTRACT FROM PUBLISHER]

With its rich history and the rapid economic development in China, Chinese has becoming a language drawing an increased research interest in different domains. The big difference between Chinese and other languages (mainly the European languages) leads to new challenges regarding existing methodologies. In this paper, we propose an ontology-based qualitative methodology for analyzing the Chinese language, aiming to construct machine readable knowledge from original texts, carrying out mining and reasoning on the data, and providing meaningful inference results. Our approach is exploiting characteristics of the language, which allow us to extract certain elements which are fed to the analytical process. A software prototype has been implemented. Experimental cases are presented and they indicate that the approach can produce reasonable results. [ABSTRACT FROM PUBLISHER]

“Software Engineering (SE) is the application of a systematic, disciplined, quantifiable approach to the development, operation, and maintenance of software, and the study of these approaches; that is, the application of engineering to software.” [1] However, there are still late software projects that contain bugs. These issues are made worse through implementation affecting the design, and the repetitions involved in software coding. Furthermore, making changes to these software systems after initial development grows in complexity as time goes on. A software engineering methodology called Ontology Driven Software Engineering (ODSE) uses the artificial intelligence concept of an ontology for each stage in SE. The use of the ontology in different stages varies in usefulness and in ease. Many of the articles covered in this paper look at ontologies that are used primarily in the requirement and design phase. However, when it comes to implementation, they are only used as reference material for the developers who are coding. This project was created to provide a way for developers to use the ontology in a more integrated manner. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a context-based hybrid semantic matching framework for e-mentoring system. The framework adopts ontology technologies to define the attributes of mentors and mentees, the matching results, and satisfaction values of the mentoring, and to recommend adequate mentors via semantic reasoning. In the developed ontology named mentoring-matching ontology (M2O), the pairs of mentor-mentee and the satisfaction values of mentoring are managed as cases. To increase the satisfaction of matching, the framework performs rule-based filtering and two kinds of semantic matching to find appropriate mentees and the most similar mentor with the new mentee. We implemented a mobile e-mentoring system named Mint Story with the proposed matching framework. Also, we show the superiority of our framework in the evaluation section. [ABSTRACT FROM PUBLISHER]

One of the major trends of modern data and information fusion technology is to take into account not only the fact of multiple sources but also the available context of a domain. An example case of the above statement is when data to be fused are represented in an object database and can be enriched by an expert with the domain ontology which provides a domain context to each instance of the data. The paper proposes context-driven data and information fusion technology. The latter comprises ontology-driven generation and aggregation of features as well as a two-step filtering of features: based on discriminative power at the first step and on a measure of causality at the second one. A peculiarity of the resulting solution is that every class of decision is specified by a specific set of features represented in terms of predicates that are statements about feature properties. The technology is implemented and validated in several applications of real life scale. In the paper, the technology is demonstrated by the example of applying it to the implementation of personalized intelligent MS Outlook e-mail assistant. [ABSTRACT FROM PUBLISHER]