Takafumi Nakanishi

In this paper, we present a new concept, a waveform model of Kansei transition for time-series media content. It is important to apply the time-series change of media content to Kansei information processing. For example, the impression of music media content changes over time. In our model, we represent Kansei transition by time-series change of media content as waveforms. We realize new Kansei similarity by comparison with Kansei transitions represented by waveforms applying a signal processing technique. Through new Kansei similarity, it is possible to realize media content retrieval and recommendation systems corresponding to the time-series Kansei transition of media content. Our model consists of two modules: a high-order media-Kansei transformation module and a waveform similarity computation module. The high-order media-Kansei transformation module extracts each Kansei magnitude by each time from the features of media content. The waveform similarity computation module computes similarities between each waveform represented as Kansei transition.

In this paper, we discuss the designing of feature metadata for sensory acts, such as human perception of media data. We defined the desired properties of feature metadata in sensibility as "intelligibility", "comprehensiveness", and "orthogonality". We also define new feature metadata for Kimono Obi designs. The Obi is a kind of belt, that is wrapped around the waist over the Kimono. In addition, we propose a method to investigate the orthogonality of feature metadata among the desired properties of feature metadata. Experiments are conducted using the proposed method to evaluate the feature metadata against the design.

In this paper, we present a thinking support system, AI-Josyu. This system also operates as a class support system which helps to teachers for lightening their work. AI-Josyu is implemented based on media-driven real-time content management framework. The system links real world media and legacy media contents together. In resent years, it is easier to collect a large amount of various kinds of data which are created with sensors in the real world. The system realizes interconnection and utilization of legacy media contents. The legacy media contents are generated and scattered on the Internet. The framework has four modules, which are called “acquisition,” “extraction,” “selection,” and “retrieval.” The real world media and the legacy media contents are interconnected by these modules. This interconnection includes semantic components. This system records teacher's voice of its lecture in real time and presents retrieved legacy media contents corresponding to subject of the lecture. By this presentation, preparing of the legacy contents is not required. This system automatically retrieves and shows the legacy media contents. This system helps students to understand contents of the lecture. In addition, the system attends to expansion of ideas. We constructed the system and conducted the demonstration in class. It shows that the system is helpful to teacher and students for expansion of thinking.

In this paper, we present a class content summary method based on media-driven real-time content management framework. It is important to summarize contents in the class for not only students but also teachers. This method acquires teacher's voice in the class in real time, and generates a summary according to the importance of contents in the class. For generating a summary, this method not only extracts important keywords from acquired teacher's voice but also retrieves legacy contents on the web. This method consists of teacher's voice acquisition, important keyword extraction, and sentence retrieval. This method is based on a media-driven real-time content management framework for interconnection between real world media and legacy media contents. We apply our framework and realize summarize contents system in the class. By this method, it is possible to show a class content summary in real-time according to importance of keywords. This method organizes educational contents for re-use.

In this paper, we present a time series structure analysis method of a meeting using text data and a method for the visualization of state transitions. Our method evaluates and visualizes the convergence/divergence of the meeting in a time series using text data from the meeting. It is important to facilitate and review meetings for improving efficiency. Therefore, it is important not only to review the final agreement and conclusion in the dialogue during the meeting but also to understand the dialogue process. We introduce two indicators: freshness and representativeness. Our system expresses the status of the meeting in four quadrants (“stagnation”, “exploration”, “deepening”, and “consensus building”) corresponding to the combination of degrees of freshness and representativeness. By conducting an analysis using these indicators, we can objectively find which parts of the dialogue stagnated or advanced the discussion. In addition, it is possible to clarify the meeting process as a structure for review and facilitation, thereby improving efficiency of the meeting. Thus, we implemented a system that realizes this method. Furthermore, we applied this system to real data gathered from meetings consisting of actual multi-company members and verified its effectiveness.

In this paper, we present a new topic variation detection method that combines a topic extraction method with a change point detection method. It extracts topics from time-series text data as the feature of each time and detects change points from the changing patterns of the extracted topics. We applied the method to analyze effective albeit underutilized text data that contained the Japanese Prime Minister's (PM's) detailed daily activities of over 32 years. Our method and data provide novel insights into the empirical analysis of the political business cycle, a classical issue in economics and political science. For example, because our approach enabled us to directly observe and analyze the PM's actions, it overcame empirical challenges that previous researchers encountered owing to the unobservability of the PM's behavior. Our empirical observations are mostly consistent with recent theoretical developments regarding this topic. Despite limitations, by employing a completely new method and data, our approach enhances our understanding and provides new insights into this classic issue.

In this paper, we present a media-driven real-time content management framework for interconnection between real world media and legacy media contents. Recently, it is easier to install various sensors in the real world, and a large amount of various kinds of media data are generated from these sensors. It is important to interconnect and utilize legacy media contents scattered on the Internet from media data generated in real time from the real world. The framework consists of four modules, 'acquisition,' 'extraction,' 'selection,' and 'retrieval.' By these four modules, it is possible to semantically interconnect real-world media data and legacy media contents. We apply our framework and realize thinking support system in classes. This system acquires teacher's voice in the class in real time, retrieves and presents legacy media contents corresponding to contents of the class on a screen. By this system, it is possible to show legacy media contents in real time on a screen according to the contents of the class without preparing in advance. This system is utilized by students to understand contents of the class and expand the ideas.

In this paper, we present a dialogue structure analysis method to visualize the transition of topics in a meeting as the one of dialogue process representation. Our method extracts topics in a meeting on time series. In addition, we define an index to assess the importance of the whole meeting in each phase. By this index, we can represent important phases in the meeting. In organizations such as companies, it is important to improve the efficiency of a meeting, because the meeting time occupies a large proportion in business hours. We should analyze contents and flows of remarks in dialogue on meetings in order to improve efficiency of a meeting. Generally, improving the efficiency of a meeting is improving the form of a meeting, such as pre-sharing of documents, keeping time, clarification of roles of members, and appointing a facilitator. Our method provides the one of the visualization for the flow of remarks in dialogue in a meeting. In this paper, we also represent some preliminary experiment by using text data for actual meetings.

We propose a new model, which is called the action-demographic interconnection model which realizes an interconnection function between customer actions and their customer demographic attribution. The features of our method are a semantic-dependent web access log analysis for predicting customer attribution. Our method makes predictions from a web access log. Therefore, we can determine what kind of customers are accessing each website. We present experiments for effectiveness with real advertising web access logs. Based on the results of the experiments, we discuss the possibility and the practicability of our proposed model.

In this paper, we present an impression estimation method for television commercials with a visualization method. Our method estimates the impressions viewers might have of a new proposal for a TV commercial written in text as weighted favorable factors and visualizes the estimated favorable factors. During the production of TV commercials, it is important to create commercials that clearly communicate the intended messages to viewers. Currently, some agents conduct questionnaire surveys regarding favorable factors for TV commercials. The surveys are being utilized for the production of commercials to meet viewer expectations. If it is possible to estimate the impressions viewers might have of a television commercial before it is broadcast, the producers will be able to quickly make television commercials according to their intentions and based on the estimated impressions. Using our method, and based on the questionnaire survey data of favorable factors for past TV commercials, this system can estimate and visualize the favorable factors as impressions of a new proposal for a TV commercial.

In this paper, we present a new topic extraction method for meetings according to the flow of conversation. Our method extracts appropriate topic words according to their importance in the conversation in a time series using text data taken from meetings. Since meetings take up a great deal of time, one of the most important issues for organizations and companies is to improve meeting efficiency. Therefore, we should analyze the contents of the meetings, but in order to do that, it is important to be able to automatically extract the most important topics made during each meeting. The changes in the importance of a topic can be seen in a time series, so it is necessary to utilize topic extraction according to its importance in time series variation during a meeting. We can then find the topic words in a meeting according to their importance in the time series variation by using our method.

In this paper, we present a construction method of Stochastic generalized inverse operator for media contents. This method realizes automatic media contents creation depending on impression words as an inverse operation of our automatic metadata extraction method which we have proposed. This is an inverse operator which extracts words from media contents. However, this creation mechanism contains ill-posed problems. In order to solve them, we introduce stochastic data about the media and it's studies. Moreover, we construct a mutual conversion between impression words and media contents. Furthermore, we apply our method to music data creation system. We performed verification experiments, and showed the effectiveness of our method.

In this paper, we propose evaluation of customer journey for contents of Owned Media. In recent years, many companies publish the Owned Media in order to brand its products and services. The Owned Media is useful for provision of novel information correctly and rapidly. On these backgrounds, there is the demand of evaluation for effectiveness of the Owned Media. It is necessary to recognize which detailed content of Owned Media has importance. Our proposed method is able to produce attractive contents through appreciating which contents make approaches to customers. We demonstrate the evaluation using a certain Web-site as Owned Media and show the effectiveness of our methods.

In this paper, we introduce an automatic media content creation system according to an impression. The system creates media content such as music, an image, etc. according to an impression given by a user. The impression is represented in words. Generally, there are two systems related to an impression. The first is a recognition system. The recognition system evokes an impression from media content. The other is a creation system. The creation system creates media content according to an impression. We construct a cycle of the impression via the recognition creation systems. In this cycle, it is mutual mapping between impressions and media contents. In this paper, we construct a recognition operator and a creation operator for realizing the impression cycle. Each operator is a relation of inverse calculation. We apply this system to music media. We introduce an automatic music creation system.

In this paper, we propose a method of image feature selection for integration of image classification by Bag-of-Keypoints method and efficient search method. Our method is integration of image classification, which provides high-precision classification for various images. Therefore, in order to select a type of image feature in accordance with various purposes, it is necessary to select method easily. In addition, our method integrates various kinds of image feature by Bag-of-Keypoints method. Classification with a combination of some detectors and descriptors is more effective than with single detector and descriptor. To realize combination of some detectors and descriptors, we propose an integrating method. This paper is LATE BREAKING PAPERS for CSCI-ISAI.

Recently, more minors are using online communication services due to the popularity of mobile devices and smartphones. Even though minors can deepen their relationships by such communication, online communication sites introduce risks including cyber-bullying or the luring of sexual predators. Such communication risks involving minors are social matters that must be detected automatically and prohibited. In this paper, we clarify communication behaviors by focusing on private chat systems, which are sometimes used to sexually entrap minors. Our analysis found that most of users communicate with less then 5 users, and the communication ended within a half hour. Then we classified users into 15 clusters based on their communication behaviors. From the clustering results, we identified both active communication senders and active communication receivers. Such communication receivers must be monitored because they might have much more opportunities to be entrapped by sexual predators.

In this paper, we present a new knowledge extraction method on Big data era.We introduce new concepts, anteroposterior correlation, and propose an extraction method of anteroposterior correlation. The anteroposterior correlation means the correlation based on the time anteroposterior relation. We consider that Heterogeneity, continuity, and visualization are the most critical features of Big data analytics, which provides a scale and connection merits based on them. No current data analysis methods are based on opened assumptions. Big data analytics provides a new data analysis method based on opening assumptions. In this paper, we especially focus on an aspect of heterogeneity. We discover a correlation in consideration of the continuity of time. By our method, we effectively discover relationships between heterogeneous things, events and phenomena. The anteroposterior correlations are represented in relative comparison with each conditional probability distribution. The one of the features of our method is a measurement correlation by using conditional probability. That is, we calculate the correlation relative by representing all in conditional probability, no absolutely. Our method is determined higher correlation by comparison to each heterogeneous thing, event and phenomenon. This is the most important points on the Big data era. When you apply current association rule extraction techniques, you obtain too big rule base to organize them. By our method, we realize the one of the methods for decision mining.

In this paper, we present a new feature selection method for comparison of each event in big data. The method selects appropriate features for comparing or evaluating each event when we prepare an event set. There are massive data on the Internet as big data. We have been able to retrieve appropriate data sets from them by using given keywords. However, it is still difficult to use these data to compare some concepts. For example, when you would like to compare the difference between your company and a rival company, it is difficult to compare them, because evaluation axes are needed to compare them. This method extracts evaluation axes as features in a data-driven manner. We can compare them by using the extracted axes as the features. This means that the system automatically selects the appropriate features which we should focus on when we compare them.

In this paper, we present a new recognition method of the important image regions of retrieved image data set by a given query. This method indicates where you should focus on the image data set. An image data has the various aspects for making the appearance of data legible to persons. Therefore, an image data sometimes provides us more various knowledges than text data. This is obvious from using not text data but image data for visualization of data in many cases. It is important to utilize such as image data, however, it is hard to compare image data and any other data written in text, etc. This paper proposes our new method-glocal information extraction for recognition of important image regions by a given query written in text data and also demonstrates the implementation of this method. © 2014 WIT Press.

We represent a new model of knowledge creation and knowledge utilization. Recently, the one of the important knowledge management issues is how to create knowledge and how to utilize their knowledge. Actually, we cannot maximize the accumulated know-how on various fields, although there are various and a massive of data on the World Wide Web. It is important to consider how to organize knowledge and how to use their knowledge. The one of the solutions is to measurement correlation between data sets on heterogeneous fields. Furthermore, we have to consider how to use the organized data in the interconnection. We propose a representation method of knowledge as a matrix. Generally, a matrix represents relationships between each element of rows and columns. This is the one of the knowledge creation, because the matrix creation process is as same as the relationships between each element of data as knowledge. Moreover, we represent that the generalized inverse operation of the matrix is as same as knowledge utilization. Knowledge creation and knowledge utilization can be regarded as reverse operation. Therefore, we can define that knowledge utilization operator as generalized inverse operator, when we abstract knowledge as a matrix from various data. The features of this method are simplification of a knowledge representation, an inverse operation of knowledge creation and knowledge utilization, and the knowledge management by correlation. By our proposed method, we get new values by interconnection among data sets that are in heterogeneous fields.

We represent a new framework - Knowledge creation grid for Big data era. Currently, there are various types of data in various fields. The essences of ICT are 'scale merit,' 'scope merit,' and 'connection merit.' The Big data itself represents 'scale merit' and 'scope merit,' because there are massive of data and these data are utilized in various fields. However, the aspect of the 'connection merit' in big data has not represented. To lead to value creation from such data, it is important to interconnect data set among heterogeneous fields. We propose Knowledge creation grid as the one method of interconnection for data set. Almost correlation between data sets are discovered by synchronization or co-occurring on spatiotemporal. We, persons discover correlation from the meaning of data. In this framework, a meaningful data set is abstracted and represented as knowledge. This abstraction as knowledge is possible to discover various effective correlations by diversity and consensus building. In this paper, we represent a whole design of Knowledge creation grid and its primitive functions to realize diversity and consensus building.

In this paper, we design a new model for Big data analytics-data-driven axes creation model. In Big data environment, the one of the important technologies is a correlation measurement. We cannot define a protocol of measurement on Big data era, because there are many varieties of data. However, almost current data analytics and data mining method cannot apply to Big data environment, because the big data environment is opened assumption and we have to consider new methods for opened assumption. That is, we have to design a new data-driven axes creation model for correlation measurement method. Our proposed model creates axes for correlation measurement on Big data analytics. Specifically, this model infers in the Bayesian network and measures correlation in the coordinate axes. Therefore, this model maps the Bayesian network into measure correlation mutually. This model contributes to a paradigm shift of Big data analytics.

In this paper, we represent a dynamic context-dependent weighting method for vector space model. A meaning is relatively decided by a context dynamically. A vector space model, including latent semantic indexing (LSI), etc. relatively measures correlations of each target thing that represents in each vector. However, the vectors of each target thing in almost method of the vector space models are static. It is important to weight each element of each vector by a context. Recently, it is necessary to understand a certain thing by not reading one data but summarizing massive data. Therefore, the vectors in the vector space model create from data set corresponding to represent a certain thing. That is, we should create vectors for the vector space model dynamically corresponding to a context and data distribution. The features of our method are a dynamic calculation of each element of vectors in a vector space model corresponding to a context. Our method reduces a vector dimension corresponding to context by context-depending weighting. Therefore, We can measure correlation with low calculation cost corresponding to context because of dimension deduction. © 2014 IEEE.

Specification of collaboration has neglected over a long period although collaboration is one of main conceptions in computer science and computer engineering. We distinguish between collaborations among systems and socio-technical collaboration. Database research has succeeded in developing approaches for collaboration among database systems that incorporate conceptual specification and allow to reason on systems at a far higher abstraction level. Conceptual modelling of socio-technical collaborating systems is however an open research issue. With the advent of web information systems systems became naturally socio-technical and collaborating. Therefore, we need a techniques for conceptual description of collaboration. Collaboration modelling may be based on models for communication, models for coordination, and models for cooperation. In socio-technical systems users work within their local environment and collaborate within the global world. Therefore, users inject their culture and their specific behaviour into collaboration. Users use information, communication, cooperation and coordination services. These services must be highly flexible and adaptable to the needs of users, to the circumstances and contexts of these users, and to the technical infrastructures used. © 2014 The authors and IOS Press.

In this paper, we will introduce the new technology of correlation search a retrieval technique based on measurement space model. There are said to be two types of human's knowledge. One is the kind we obtain from reading literature. This may be called absolute knowledge. With ICT technology, it is knowledge that can be described by the ontology used in a conceptual dictionary and Semantic Web. Another kind may be called relative knowledge. This is paradigmatically, relational knowledge we gain when we compare knowledge A with knowledge B. Such knowledge may be connected in thought without being displayed (i.e, as a document on the Internet or being inscribed on a piece of paper). Humans often compare heterogeneous things. Moreover, humans are thought to unconsciously create the criteria for linking heterogeneous things. In this model, the criteria are represented by a set of axes which form space and by a measurement method which mathematically measures norms, distance or inner product. It is important to search not only for the correlation between heterogeneous knowledge, but also for factors (axis, index) that contribute to the correlation. This model will show correlations between heterogeneous knowledge and their factors, by deriving a selected knowledge cluster that has high correlation magnitude and a set of axes used to calculate the magnitude.

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 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.

In this paper, we present a phoneme-impression transformation operator construction method for an arbitrary word. This method extracts impression that comes from sounds of arbitrary words, including undefined words, unknown words, and new words. It realizes interfaces between persons and computers utilizing user's feeling expression by undefined/unknown words. This paper presents a Kansei retrieval system by utilizing this method for music MIDI data as an experiment system and an application example. Finally we perform various experiments on the system in order to verify the effectiveness o...

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.

Various knowledge resources are spread to a world-wide scope. Unfortunately, most of them are community-based and never thought to be used among different communities. That makes it difficult to gain "connection merits" in a web-scale information space. This paper presents a three-layered system architecture for computing dynamic associations of events to related knowledge resources. The important feature of our system is to realize dynamic interconnection among heterogeneous knowledge resources by event-driven and event-centric computing with resolvers for uncertainties existing among those resources. This system navigates various associated data including heterogeneous data-types and fields depending on user's purpose and standpoint. It also leads to effective use for the sensor data because the sensor data can be interconnected with those knowledge resources. This paper also represents application to the space weather sensor data. © 2011 The authors and IOS Press. All rights reserved.

Petri Rantanen, Pekka Sillberg, Hannu Jaakkola, Takafumi Nakanishi, 2010, 'An Asynchronous Message-Based Knowledge Communication in a Ubiquitous Environment', <i>Lecture Notes in Computer Science</i>, pp. 434-444

Service-oriented architecture (SOA) is emerging as a paradigm for developing distributed application. As the development of hardware and software technology, fast increasing of peers or services has issued critical problems for the popularity of SOA. One is system scalability and robustness, and the other one is service location validation. In this paper, we introduce-SOBEX -a web service search engine which designs a distributed indexing structure SIKA, and proposes proactive web services reuse mechanism by introducing service context model SPOT. SIKA is a community-oriented virtual hierarchical distributed indexing structure based on classic Chord algorithm. Though it groups nodes into interest-based communities, it is completely distributed and without central management. Then it promises system search efficiency together with scalability and robustness. The growing number of web services available with an organization and on the web raises new problem: locating the desired web services. Generally keyword-based search has meet with high recall and low precision. In order to improve search efficiency, SOBEX proposes to qualify services using service usage context model, which tries to reduce the concept understanding gap between human and computer by proactively assigning the services with their own story background. On the other hand, besides traditional keyword-based methods, it introduces context-based queries to improve service reusability. Copyright 2009 ACM.

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. Copyright © 2009 ACM.

This paper presents a semantic associative browsing system called Semantic Association Various Viewpoint sYstem (SAVVY). Recently, the number of users who employ search engines for not only retrieving Web pages but also understanding or learning an arbitrary concept has been increasing. It is difficult to understand and learn an arbitrary concept by using most of the current search engines, because they provide a list of results according to the input keywords. The proposed approach represents the various relationships between arbitrary concepts as an outline. For example, it is assumed that a user wants information related to an input keyword. Current search engines represent a list of contents that match the keyword. The proposed approach dynamically represents various related concepts and contents on heterogeneous fields by using multiple views that are mutually synchronized. It helps to obtain various contents related to heterogeneous resources while understanding or learning an arbitrary concept. © 2009 IEEE.

This paper presents an interconnection method for heterogeneous knowledge bases depending on user's 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.

Under web service environment, services' calling or called relationships are usually represented as links. Based on link- or link-content analysis, it may produce tight clusters, which is useful for resource management. However current work only considers the existence of collaboration and it seldom takes service usage context into consideration. In this paper, we suppose to cluster similar services by introducing service usage context which is the complete stories for services' activities and helps to improve clustering performance. Being different with link- or link-content-based clustering methods that considering one-link-distance-away-neighbors (part of the activity story), this work proves that in team-up work situation, all collaborators (long-distance-neighbor) can provide information for distinguishing services.

This paper proposes a general framework of for a system with a semantic browsing and visualization interface called Knowledge Communication, Collaboration and Creation Browser (KC3 Browser) integrates multimedia contests and web services on the grid networks, and makes a semantic mash-up called knowledge workspace (k-workspace) with various visual gadgets according to user's contexts (e.g. their interests, purpose and computational environments). KC3 Browser also achieves a link-free browsing for seamless knowledge access by generating semantic links based on an arbitrary knowledge models such as ontology and vector space models. It assists users to look down and to figure out various social and natural events from the web contents. We have implemented a prototype of KC3 Browser and tested it to an international project on risk intelligence against natural disaster.

This paper presents a method for the creation of dynamic cross-domain links for interconnection of heterogeneous knowledge bases. The proposed method represents various relationships in heterogeneous fields and navigates to related contents in these heterogeneous fields. Most information resources are connected by static links that do not describe the significance of the connections. Recently, it has been important for a lot of users to employ search engines not only for retrieving Web pages but also for understanding or learning semantic concepts. However, in the current Web environment, it remains difficult for a user to understand the relationships among arbitrary concept. The proposed method dynamically creates links between heterogeneous fields depending on the user context. The method obtains various related information resources between heterogeneous fields while understanding various relationships among arbitrary concepts. Our approach has functions for the reconfiguration of Web resources and legacy knowledge bases by dynamic links creation.