Knowledge Grid Research Articles

Agricultural Knowledge Grid Construction

In order to eliminate the ambiguity in semantic understandings during the retrieval of the users, as well as mining the relationship between the concept of agricultural knowledge, the association of knowledge among heterogeneous databases needs to be set up, which enable users to discover useful knowledge clues, and gradually form solution for the ultimate question. Based on the characteristics of agricultural knowledge and the achievements of knowledge grid research, with a combination of traditional agricultural thesauri and ontology technology, the agricultural knowledge grid was constructed with the resource layer, semantic layer and user level. It has been applied for semantic extension on retrieval, knowledge links, and knowledge reasoning diagnosis. It gains some achievements, which provide technical support and experience for the deeply agricultural knowledge services. DOI: http://dx.doi.org/10.11591/telkomnika.v11i9.3272

Programming knowledge discovery workflows in service‐oriented distributed systems

SUMMARYIn several scientific and business domains, very large data repositories are generated. To find interesting and useful information in those repositories, efficient data mining techniques and knowledge discovery processes must be used. The exploitation of data mining techniques in science helps scientists in hypothesis formation and gives them a support on their scientific practices, whereas in industrial processes, data mining can exploit existing data sources as a real value for companies that can take advantage from the knowledge that can be extracted from their large data sources. Data mining tasks are often composed by multiple stages that may be linked to each other to form various execution flows. Moreover, data mining tasks are often distributed because they involve data and tools located over geographically distributed environments. Therefore, it is fundamental to exploit effective paradigms, such as services and workflows, to model data mining tasks that are both multi‐staged and distributed. This paper discusses data mining services and workflows for analyzing scientific data in high‐performance distributed environments such as Grids and Clouds. We discuss how it is possible to define basic and complex services for supporting distributed data mining tasks in Grids. We also present a workflow formalism and a service‐oriented programming framework, named DIS3GNO, for designing and running distributed knowledge discovery processes in the Knowledge Grid system. DIS3GNO supports all the phases of a knowledge discovery process, including composition, execution, and results visualization. After introducing DIS3GNO, some relevant use cases implemented by it and a performance evaluation of the system are discussed.Copyright © 2012 John Wiley & Sons, Ltd.

A Distributed Learning and Teaching Environment across Institutions based on Advanced Grid Portal Technology

This study presents web-based networked information and collaborative authoring architecture as Information and Knowledge Grids. This architecture is made possible by advanced open source grid portal software in combination with distributed parallel computing system. The distributed learning and teaching grid environment are use for improving the quality of higher education as an initial stage. This Grid architecture provides reasonable and effective online assisted learning environment at the institution for any courses. The paper describes in some detail the architecture of an advanced grid portal based on parallel computing system, learning and teaching interactions and the processes of courses management over it.

Research on collaborative conceptual design based on distributed knowledge resource

Conceptual design is an early stage of the whole product development process, which involves the generation of solutions to satisfy the design requirements. It is a crucial issue on how to find proper principle solutions from enormous, isomeric, and even distributed knowledge resources repositories and then ally with these correlative distributed knowledge resources providers to collaborative conceptual design. Design is a task-solving process using corresponding resource, so it does not always have an agency in product design. This paper proposes a model of collaborative conceptual design based on distributed knowledge resources, which is centered on design tasks and based on dynamic knowledge alliance without agency. After a model of knowledge resource unit and a model of Web services based on knowledge resource are proposed, a virtual quantitative and qualitative knowledge resource representation based on knowledge grid is then put forward. The mixed knowledge resource repository is also proposed to support collaborative conceptual design. The strategies of knowledge register, publish, storage, reorganization, orientation, and problem solving based on mixed knowledge resource repository are then discussed in detail. The data exchange mode of principle solutions is then described. The selection strategy of dynamic knowledge alliance members is established. The framework of distributed knowledge resource is also put forward to support collaborative conceptual design. The process of collaborative conceptual design based on distributed knowledge resource is then proposed. The conceptual design of new devices for decreasing resistance of amphibious vehicles is given as an example, which demonstrates that the methodology is obviously helpful to produce valuable principle solutions with distributed knowledge for product innovation.

Research on Knowledge Sharing for Customer Collaborative Product Innovation based on Knowledge Grid

Research on Knowledge Sharing for Customer Collaborative Product Innovation based on Knowledge Grid

Business leaders and town gentry in early industrial Britain: specialist occupations and shared urbanism

ABSTRACT:Three major conclusions are derived from close study of Britain's pioneering directories in the 1770s and 1780s. First, they show that over 30,000 leading townsmen and women were enmeshed into the burgeoning knowledge grid through the public listings of their addresses, status and occupations. Secondly, a close examination of that information reveals a notable extent of occupational specialization – among both men and women, and among individuals and the nascent firms – thus confirming one of Adam Smith's key observations about the nature of Britain's increasingly commercialized, if still largely pre-mechanized, economy. Thirdly, aggregative analysis highlights systematic differences in the socio-economic characteristics of different towns: from manufacturing, commercial and professional centres to the great capital cities to the specialist leisure towns and resorts – all interlocking in an inter-dependent urban network. Hence this evidence suggests that a generic re-interpretation of all large towns as ‘residential leisure towns’ on the strength of their flourishing cultural life (as recently proposed by Stobart and Schwarz) is misleading, as it obscures significant systemic differences between different types of towns. At the same time, however, the interlinked urban network was generating a confidently shared urbanism, bridging between aristocratic and middle-class society. That link was exemplified by the listing of numerous titled and gentlemanly ‘town gentry’ alongside the business leaders – as the directories in effect flourished their collective calling cards.

Research on Cooperative Maintenance Decision System for Power Plant

The cooperative maintenance is carried out of China's electric power system, an important research topic, how to achieve wide-area collaboration maintenance decision is one of the forefront research tasks in the maintenance field. In order to solve the knowledge resource sharing and utilization in the plants maintenance field, on the basis of analyzing the current status and needs of the maintenance network, to achieve the object of swarm cooperative maintenance decision-making, we have established the collaborative maintenance decision system, is supported by multi-knowledge, multi-method, multi-resource, multi-information based on knowledge grid, and also have investigated the knowledge modeling approach. Resource nodes open mutually, share resource information and by the unified management and scheduling of the knowledge grid platform, coordination between each other, provide the necessary services for different power plants, manufacturers and research institutes. That is beneficial to enhancing the diagnosis standards and treatment ability of the maintenance decision system, reducing the diagnosis and maintenance costs, and improving the diagnosis efficiency.

Pretext Knowledge Grids on Unstructured Data for Facilitating Online Education

The paper describes a plan, synchronized steps to cluster the unstructured data obtained from different sources i.e. internet, web crawler, mobiles in a form that it results in phrased information satisfying someone needs. Reach of such unstructured data in each one door faces constraints like limited financial resources, insufficient number of qualified instructors, and lack of diagnostic test for imparting individual training. The paper describes the wrapped way out for the data available on nets, mobiles or some another source through agents, algorithms and some designed function kept in repositories. The proposed system contains authoring agents, algorithm and repository to lead out a traditional teaching approach.

An Agent Coordination Perspective on Software Ontologies with Knowledge Grid

The effectiveness of a team work not only depends on individual's knowledge; depends on cooperation and precise communication among them. The tradeoffs of remote communication as compared with face-to-face communication are a major issue for geographically dispersed team work. However, the challenges such as what task to be completed, what problems have been raised and clarified, clarity in project plan , availability of resource person and to make just-in-time decision are the major concerns of a software life cycle. Consequently, these issues cause project delay as well as anxiety among team members. The ease of communication can be achieved by aggregating the collective knowledge about the project, the domain knowledge and skills of managing project into a common resource platform with the help of intelligent agents and allow them to share the repository called Knowledge Grid. In this paper, we present the challenges in distributed team environment and ontology involved in software life cycle along with distributed agent algorithm.

A Sort of Knowledge Metadata Management Model in Semantic Grid Environment

As a hot spot in today's research, the grid which applies the distributed management style in the knowledge management will integrate the existing resources to the maximum. In order to change the current disorder status of knowledge resource management and get effective management and sharing of education resources, this paper takes the knowledge classification standard in LOM as the division basis of the knowledge grid metadata community, and puts forwards a sort of grid community model based on knowledge resources metadata as knowledge management framework according to the characteristics of grid management architecture. In the paper, some key issues are discussed such as the construction of grid community interior structure, the news transmission mechanism, the algorithm between communities and so on. According to the classification principles of educational information in LOM metadata as well as the grid management system structure, the concept of Grid Community based on education resource is proposed to improve the efficiency of the organization and management of educational resources in the grid.

Retraction: Changing from computing grid to knowledge grid in life‐science grid

Grid computing has a great potential to become a standard cyber infrastructure for life sciences that often require high-performance computing and large data handling, which exceeds the computing capacity of a single institution. Grid computer applies the resources of many computers in a network to a single problem at the same time. It is useful to scientific problems that require a great number of computer processing cycles or access to a large amount of data.As biologists,we are constantly discovering millions of genes and genome features, which are assembled in a library and distributed on computers around the world.This means that new, innovative methods must be developed that exploit the re-sources available for extensive calculations - for example grid computing.This survey reviews the latest grid technologies from the viewpoints of computing grid, data grid and knowledge grid. Computing grid technologies have been matured enough to solve high-throughput real-world life scientific problems. Data grid technologies are strong candidates for realizing a resourceome for bioinformatics. Knowledge grids should be designed not only from sharing explicit knowledge on computers but also from community formulation for sharing tacit knowledge among a community. By extending the concept of grid from computing grid to knowledge grid, it is possible to make use of a grid as not only sharable computing resources, but also as time and place in which people work together, create knowledge, and share knowledge and experiences in a community.

Framework of Knowledge Management in Education Based on Knowledge Grid

Framework of Knowledge Management in Education Based on Knowledge Grid

A framework for modeling and supporting data transformation services over data and knowledge grids with real‐time bound constraints

AbstractData and Knowledge Grids represent emerging and attracting application scenarios for Grid Computing, and pose novel and previously unrecognized challenges to the research community. Basically, Data and Knowledge Grids are found on high‐performance Grid infrastructures, and add to the latter meaningful data‐ and knowledge‐oriented abstractions and metaphors that perfectly marry with innovative requirements of modern complex Intelligent Information Systems. To this end, Service‐oriented Architectures and Paradigms are the most popular for Grids, and on the whole represent an active and widely recognized area of Grid Computing research. In this paper, we introduce the so‐called Grid‐based RTSOA frameworks, which essentially combine Grid Computing with real‐time service management and execution paradigms, and place emphasis for novel research perspectives in data‐intensive e‐science Grid applications on real‐time bound constraints. Grid‐based RTSOA frameworks are then specialized to the particular context of Data Transformation services over Grids, which play a relevant role for both Data and Knowledge Grids. Finally, we complete the main contribution of this paper with a rigorous theoretical model for efficiently supporting Grid‐based RTSOA frameworks, with particular emphasis on the context of Data Transformation services over Grids, along with its comprehensive experimental assessment and analysis. Copyright © 2010 John Wiley & Sons, Ltd.

Interconnection of heterogeneous knowledge bases and its application on Knowledge Grid

AbstractRecently, the number of users who use search engines for not only retrieving Web pages but also understanding or learning an arbitrary concept has been increasing. However, it is difficult to understand and learn an arbitrary concept by using the existing search engines. This paper presents a method for the interconnection of heterogeneous knowledge bases on Knowledge Grid for knowledge sharing and provision. In this paper, for realizing the proposed method, we introduce two types of services—intra‐operation services and inter‐operation services—on Knowledge Grid. The system dynamically creates a semantic associative network that connects the user's interests and concepts, and provides correlation views that represent various relationships for connecting related resources. In addition, we employ a link‐free browsing system by this method called correlation browsing, which represents the semantic association utilizing collective knowledge by connecting various knowledge bases. The current web browsing has two actions—browsing and jumping by static hyperlinks. It is difficult to understand the relationship between each page that is connected by static hyperlink. The system navigates the related contents while repeatedly switching between a content browsing mode and a correlation browsing mode. Therefore, a user can access various contents while understanding the relationships. Copyright © 2010 John Wiley & Sons, Ltd.

How distributed data mining tasks can thrive as knowledge services

Introduction Computer science applications are becoming more and more network centric, ubiquitous, knowledge intensive, and computing demanding. This trend will result soon in an ecosystem of pervasive applications and services that professionals and end-users can exploit everywhere. Recently, collections of IT services and applications, such as Web services and Cloud computing services, became available opening the way for accessing computing services as public utilities, like water, gas and electricity. Key technologies for implementing that perspective are Cloud computing and Web services, semantic Web and ontologies, pervasive computing, P2P systems, Grid computing, ambient intelligence architectures, data mining and knowledge discovery tools, Web 2.0 facilities, mashup tools, and decentralized programming models. In fact, it is mandatory to develop solutions that integrate some or many of those technologies to provide future knowledge-intensive software utilities. The Grid paradigm can represent a key component of the future Internet, a cyber infrastructure for efficiently supporting that scenario. Grid and Cloud computing are evolved models of distributed computing and parallel processing technologies. The Grid is a distributed computing infrastructure that enables coordinated resource sharing within dynamic organizations consisting of individuals, institutions, and resources. In the area of Grid computing a proposed approach in accordance with the trend outlined above is the Service-Oriented Knowledge Utilities (SOKU) model that envisions the integrated use of a set of technologies that are considered as a solution to information, knowledge and communication needs of many knowledge-based industrial and business applications. The SOKU approach stems from the necessity of providing knowledge and processing capabilities to everybody, thus supporting the advent of a competitive knowledge-based economy. Although the SOKU model is not yet implemented, Grids are increasingly equipped with data management tools, semantic technologies, complex work-flows, data mining features and other Web intelligence approaches. Similar efforts are currently devoted to develop knowledge and intelligent Clouds. These technologies can facilitate the process of having Grids and Clouds as strategic components for supporting pervasive knowledge intensive applications and utilities. Grids were originally designed for dealing with problems involving large amounts of data and/or compute-intensive applications. Today, however, Grids enlarged their horizon as they are going to run business applications supporting consumers and end-users. To face those new challenges, Grid environments must support adaptive knowledge management and data analysis applications by offering resources, services, and decentralized data access mechanisms. In particular, according to the service-oriented architecture (SOA) model, data mining tasks and knowledge discovery processes can be delivered as services in Grid-based infrastructures. Through a service-based approach we can define integrated services for supporting distributed business intelligence tasks in Grids. Those services can address all the aspects that must be considered in data mining and in knowledge discovery processes such as data selection and transport, data analysis, knowledge models representation and visualization. We worked in this direction for providing Grid-based architectures and services for distributed knowledge discovery such as the Knowledge Grid the Weka4WS toolkit, and mobile Grid services for data mining. Here we describe a strategy and a model based on the use of services for the design of distributed knowledge discovery services and discuss how Grid frameworks, such those mentioned above, can be developed as a collection of services and how they can be used to develop distributed data analysis tasks and knowledge discovery processes using the SOA model.

Study on Enterprise Knowledge Management Based on Knowledge Grid

Knowledge management has entered into many organizations. However, because of the independent organizations' own hardware and software defects, knowledge management has no significant effect. Knowledge grid provides a strong foundation and a larger space for development of knowledge management. This paper discusses how knowledge grid can be applied into the enterprise knowledge management. From knowledge sharing and knowledge mining the paper builds the knowledge management architecture based on knowledge grid, which is expected to help the management systems and tools of large-scale enterprises’ informatization.

Changing from computing grid to knowledge grid in life‐science grid

Grid computing has a great potential to become a standard cyber infrastructure for life sciences that often require high-performance computing and large data handling, which exceeds the computing capacity of a single institution. Grid computer applies the resources of many computers in a network to a single problem at the same time. It is useful to scientific problems that require a great number of computer processing cycles or access to a large amount of data.As biologists,we are constantly discovering millions of genes and genome features, which are assembled in a library and distributed on computers around the world.This means that new, innovative methods must be developed that exploit the re-sources available for extensive calculations - for example grid computing.This survey reviews the latest grid technologies from the viewpoints of computing grid, data grid and knowledge grid. Computing grid technologies have been matured enough to solve high-throughput real-world life scientific problems. Data grid technologies are strong candidates for realizing a "resourceome" for bioinformatics. Knowledge grids should be designed not only from sharing explicit knowledge on computers but also from community formulation for sharing tacit knowledge among a community. By extending the concept of grid from computing grid to knowledge grid, it is possible to make use of a grid as not only sharable computing resources, but also as time and place in which people work together, create knowledge, and share knowledge and experiences in a community.

SQORE: an ontology retrieval framework for the next generation Web

AbstractIn order to resolve semantic issues in the Semantic Web and in the Semantic Knowledge Grid, tools for retrieving a suitable ontology from an ontology database are essential. However, existing approaches to ontology retrieval base their search mechanisms solely on keyword matching and return a lengthy list of relevant ontologies that may not satisfy user requirements. Users, therefore, are not equipped with expressive means to structurally and semantically describe their ontology needs. To tackle this problem, this paper develops a framework for Semantic Query based Ontology Retrieval, namely, SQORE. It enables precise formulation of a semantic query in order to best capture a user's ontology requirements, which include not only the desired class and property names, but also their relations and restrictions. SQORE employs the XML Declarative Description theory as its theoretical foundation for modeling ontology databases and evaluating semantic queries. Moreover, similarity score is formally defined as a key metric for ranking the resulting ontologies based on their conceptual closeness, their query coverage and their compactness with respect to the given query. A Web‐based prototype system is developed as a proof of concept, and experiments are conducted to evaluate the effectiveness of the SQORE framework. Experimental results indicate that SQORE yields better results and better rankings compared with existing systems. Copyright © 2009 John Wiley & Sons, Ltd.

Knowledge acquisition for product development in knowledge grid

This paper is mainly concerned with the implementation of innovative product development-oriented knowledge acquisition in a knowledge grid. The architecture of a knowledge acquisition platform based on the theory of inventive problem solving (TRIZ) and ontology is proposed at first. Then the key techniques analysed are: ontology building for product development, semantic register, semantic parser, query dispatch, and knowledge acquisition based on contradiction matrix. Finally, working scenarios are employed to illustrate the knowledge acquisition process.

The Knowledge Grid Environment

The knowledge grid environment is an autonomous human-machine environment evolving with science, technology, culture, and society. It consists of autonomous individuals, self-organized semantic communities, an adaptive-networking mechanism, and an evolving semantic networking mechanism. A memex extension (ME) offers a general model for autonomous individuals in this environment. MEs are configurable, adaptive, and context-aware digital organisms that model various types of network resources and host distributed network software and devices. MEs organize themselves to perform tasks according to social and economical principles. The evolving environment supports the generation of new MEs through inheritance. The ME model is also a knowledge model that can actively detect problems and fuse with and inherit from others' knowledge to obtain a reputation in providing knowledge services. The ME models advance information and knowledge in the knowledge grid environment.