Yasushi Kiyoki

Towards knowledge management based on harnessing collective intelligence on the web

The Web has acquired immense value as an active, evolving repository of knowledge. It is now entering a new era, which has been called “Web 2.0”. One of the essential elements of Web 2.0 is harnessing the collective intelligence of Web users. Large groups of people are remarkably intelligent, and are often smarter than the smartest people in them. Knowledge as collective intelligence is socially constructed from the common understandings of people. It works as a filter for selecting highly regarded information with collective annotation based on bottom-up consensus and the unifying force of Web-supported social networks. The rising interest in harnessing the collective intelligence of Web users entails changes in managing the knowledge of individual users. In this paper, we introduce a concept of knowledge management based on harnessing the collective intelligence of Web users, and explore the technical issues involved in implementing it.

Moving Phenomenon: Aggregation and Analysis of Geotime-Tagged Contents on the Web

The analysis of movement of people, vehicles, and other objects is important for carrying out research in social and scientific domains. The study of movement behavior of spatiotemporal entities helps enhance the quality of service in decision-making in real applications. However, the spread of certain entities such as diseases or rumor is difficult to observe compared to the movement of people, vehicles, or animals. We can only infer their locations in a certain region of space-time on the basis of observable events. In this paper, we propose a new model, called as moving phenomenon, to represent time-varying phenomena over geotime-tagged contents on the Web. The most important feature of this model is the integration of thematic dimension into an event-based spatiotemporal data model. By using the proposed model, a user can aggregate relevant contents relating to an interesting phenomenon and perceive its movement behavior; further, the model also enables a user to navigate the spatial, temporal, and thematic information of the contents along all the three-dimensions. Finally, we present an example of typhoons to illustrate moving phenomena and draw a comparison between the movement of the moving phenomenon created using information from news articles on the Web and that of the actual typhoon.

SAVVY Wiki: a context-oriented collaborative knowledge management system

This paper presents a new Wiki called SAVVY Wiki that realizes context-oriented, collective and collaborative knowledge management environments that are able to reflect users intentions and recognitions. Users can collaboratively organize fragmentary knowledge with the help of the SAVVY Wiki. Fragmentary knowledge, in this case, implies existing Wiki content, multimedia content on the web, and so on. Users select and allocate fragmentary knowledge in different contexts onto the SAVVY Wiki. Owing to this operation, it is ensured that related pages belong to the same contexts. That is, users can find correlations among the pages in a Wiki. The SAVVY Wiki provides new collective knowledge created from fragmentary knowledge, depending on contexts, in accordance with the users collaborative operations. Various collaborative working environments have been developed for the sharing of collective knowledge. Most current Wikis have a collaborative editing mode to every page, as a platform to enable a collaborative working environment. In order to understand an arbitrary concept thoroughly, it is necessary to find correlations among the various threads of content, depending on the users purpose, task or interest. In a Wiki system, it is important to realize a collaborative editing environment with correlation among pages depending on the contexts. In this paper, we present a method to realize the SAVVY Wiki, and describe its developing prototype system.

Interconnection of heterogeneous knowledge bases and its application on Knowledge Grid

Recently, 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 users 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

Exploiting service context for web service search engine

Service-oriented architecture (SOA) is rapidly becoming one of significant computing paradigms. However as the increasing of services, haphazardly of service definition makes it tedious and less efficient for service discovery. In this paper, we propose a novel context model SPOT to express services usage information. Based on SPOT definition, we build services collaboration graph and propose to analyze collaboration structure to rank services by their usage goodness. The distinctive feature of our method lies on the introducing of services context model which is a new model to deal with service context information, and integrating it for supporting service search. Our experimental results indicate that: our context-based ranking is useful for good services recommendation; services context makes up for service description heterogeneity and can help to distinguish content-similar services.

SIKA: A Decentralized Architecture for Knowledge Grid Resource Management

Grid system has facilitated large scale, flexible, distributed, secure and coordinated resource sharing. It aims to make full use of the network resources and build a harmonious collaborative community. However, resource management and quick resource location are still urgent problems for grid systems, especially under large, dynamic environment. This paper presents SIKA a grid-based overlay network for knowledge grid system. It groups the nodes into domain sensitive communities, but by defining new inter community organization mechanism, it successfully avoids using super peers. The main characteristics of this architecture are highlighted by its convenience for community analysis, promising scalability, itssearch efficiency, as well as its robustness. The experimental performance results presented here demonstrate the efficiency of the design.

Context-sensitive query expansion over the bipartite graph model for web service search

As Service Oriented Architecture (SOA) matures, service consumption demand leads to an urgent requirement to service discovery. Unlike web documents, services are intended to be executed to achieve objectives and/or desired goals of users, which means to realize application requirements. This leads to the notion that service discovery should take into account the application requirement of service with service content (descriptions) which have been well explored. Content is defined by service developers, e.g.WSDL file and context is defined by service users, which is service usages to application requirement. We find context(application) information is more useful for query generation, especially for non-expert users. So in this paper, we propose to do context-sensitive query processing to resolve application-oriented queries for web service search engine. Context is modeled by a bipartite graph model to represent the mapping relationship between application space and service space. Application-oriented queries are resolved by query expansion based on the topic sensitive bipartite graph. The experiments verify the efficiency of our idea.

A Context Dependent Dynamic Interconnection Method of Heterogeneous Knowledge Bases by Interrelation Management Function

This paper presents an interconnection method for heterogeneous knowledge bases depending on users interests as a context. Various knowledge bases have been created in each field by using collaborative working environments such as Wiki. One of the important issues is how to interconnect these knowledge bases and represent the relationships between various concepts in heterogeneous fields. An event affects various aspects of an area, field, or community. In order to understand an arbitrary event or concept, it is necessary to find various relationships over heterogeneous fields. Generally, the relationships over heterogeneous fields strongly depend on contexts and situations. It is important to realize the dynamic interconnection of knowledge bases depending on contexts and situations. Therefore, we design an interrelation management function (IMF) that defines the operator for interconnection data. In this paper, we propose a framework for a context-dependent dynamic interconnection method by using the interrelation management function.

Context-sensitive Web service discovery over the bipartite graph model

As service oriented architecture (SOA) matures, service consumption demand leads to an urgent requirement for service discovery. Unlike Web documents, services are intended to be executed to achieve objectives and/or desired goals of users. This leads to the notion that service discovery should take the “usage context” of service into account as well as service content (descriptions) which have been well explored. In this paper, we introduce the concept of service context which is used to represent service usage. In query processing, both service content and service context are examined to identify services. We propose to represent service context by a weighted bipartite graph model. Based on the bipartite graph model, we reduce the gap between query space and service space by query expansion to improve recall. We also design an iteration algorithm for result ranking by considering service context-usefulness as well as content-relevance to improve precision. Finally, we develop a service search engine implementing this mechanism, and conduct some experiments to verify our idea.

A Phenomena-of-Interest Approach for the Interconnection of Sensor Data and Spatiotemporal Web Contents

With the advance of ubiquitous computing and mobile environments, we have begun to continuously monitor changes in real-world condition and environment through wireless sensor networks. Opportunities also exist for people to create information related to the world around them by using mobile phones equipped with sensing devices, and share that information online with others. In this paper, we propose a novel approach for the interconnection of earth observation data and spatiotemporal web contents on the basis of spatiotemporal and thematic relationships. In particular, we use the concept of moving phenomena of interests to link between measurement sensing data and people-centric contents on the basis of spatiotemporal proximity and thematic relevance. This paper also shows a simple application that automatically generates semantic tags with respect to natural geographic phenomena, such as typhoons, climate changes, and air pollution, on the basis of our interconnection approach. We are able to easily understand qualitative meanings with respect to a certain phenomenon expressed by quantitative numeric conditions.