A distributed knowledge management infrastructure based on a Topic Map grid

Modern enterprise knowledge management systems typically require distributed approaches and the integration of numerous heterogeneous sources of information. A powerful foundation for these tasks can be Topic Maps, which not only provide a semantic net-like knowledge representation means and the possibility to use ontologies for modelling knowledge structures, but also offer concepts to link these knowledge structures with unstructured data stored in files, external documents etc. In this paper, we present the architecture and prototypical implementation of a Topic Map application infrastructure, the ‘Topic Grid’, which enables transparent, node-spanning access to different Topic Maps distributed in a network.

hyo-xtm: a set of hyper-graph operations on xml topic map toward knowledge management

Knowledge management is a critical issue for the next-generation web application, because the next-generation web is becoming a semantic web, a knowledge-intensive network. XML Topic Map (XTM), a new standard, is appearing in this field as one of the structures for the semantic web. It organizes information in a way that can be optimized for navigation. In this paper, a new set of hyper-graph operations on XTM (HyO-XTM) is proposed to manage the distributed knowledge resources.HyO-XTM is based on the XTM hyper-graph model. It is well applied upon XTM to simplify the workload of knowledge management.The application of the XTM hyper-graph operations is demonstrated by the knowledge management system of a consulting firm. HyO-XTM shows the potential to lead the knowledge management to the next-generation web.

Semi-automatic annotation and retrieval of visual content using the topic map technology

There are still major challenges in the area of automatic indexing and retrieval of multimedia content data for very large multimedia content corpora. Current indexing and retrieval applications still use keywords to index multimedia content and those keywords usually do not provide any knowledge about the semantic content of the data. With the increasing amount of multimedia content, it is inefficient to continue with this approach. In this paper, we describe the project DREAM, which addresses such challenges by proposing a new framework for semi-automatic annotation and retrieval of multimedia based on the semantic content. The framework uses the Topic Map Technology, as a tool to model the knowledge automatically extracted from the multimedia content using an Automatic Labelling Engine. We describe how we acquire knowledge from the content and represent this knowledge using the support of NLP to automatically generate Topic Maps. The framework is described in the context of film post-production.

An ontology-driven topic mapping approach to multi-level management of e-learning resources

An appropriate use of various pedagogical strategies is fundamental for the effective transfer of knowledge in a flourishing e-learning environment. The resultant information superfluity, however, needs to be tackled for developing sustainable e-learning. This necessitates an effective representation and intelligent access to learning resources. Topic maps address these problems of representation and retrieval of information in a distributed environment. The former aspect is particularly relevant where the subject domain is complex and the later aspect is important where the amount of resources is abundant but not easily accessible. Conversely, effective presentation of learning resources based on various pedagogical strategies along with global capturing and authentication of learning resources are an intrinsic part of effective management of learning resources. Towards fulfilling this objective, this paper proposes a multi-level ontology-driven topic mapping approach to facilitate an effective visualization, classification and global authoring of learning resources in e-learning.

Semantic-driven context-aware visual information indexing and retrieval: applied in the film post-production domain

A large volume of visual content is inaccessible until effective and efficient indexing and retrieval of such data is achieved. In this paper, we introduce the DREAM system, which is a knowledge-assisted semantic-driven context-aware visual information retrieval system applied in the film post production domain. We mainly focus on the automatic labelling and topic map related aspects of the framework. The use of the context- related collateral knowledge, represented by a novel probabilistic based visual keyword co-occurrence matrix, had been proven effective via the experiments conducted during system evaluation. The automatically generated semantic labels were fed into the Topic Map Engine which can automatically construct ontological networks using Topic Maps technology, which dramatically enhances the indexing and retrieval performance of the system towards an even higher semantic level.

KAIFIA: Knowledge assisted intelligent framework for information access

Accessing information, which is spread across multiple sources, in a structured and connected way, is a general problem for enterprises. A unified structure for knowledge representation is urgently needed to enable integration of heterogeneous information resources. Topic Maps seem to be a solution for this problem. The Topic Map technology enables connecting information, through concepts and relationships, and their occurrences across multiple systems. In this paper, we address this problem by describing a framework built on topic maps, to support the current need of knowledge management. New approaches for information integration, intelligent search and topic map exploration are introduced within this framework.

Análise de indicadores metodológicos de sustentabilidade socioambiental

Pós-graduação em Geografia - IGCE

The FORA Framework - A Fuzzy Grassroots Ontology for Online Reputation Management

Online reputation management deals with monitoring and influencing the online record of a person, an organization or a product. The Social Web offers increasingly simple ways to publish and disseminate personal or opinionated information, which can rapidly have a disastrous influence on the online reputation of some of the entities. This dissertation can be split into three parts: In the first part, possible fuzzy clustering applications for the Social Semantic Web are investigated. The second part explores promising Social Semantic Web elements for organizational applications,while in the third part the former two parts are brought together and a fuzzy online reputation analysis framework is introduced and evaluated. Theentire PhD thesis is based on literature reviews as well as on argumentative-deductive analyses.The possible applications of Social Semantic Web elements within organizations have been researched using a scenario and an additional case study together with two ancillary case studies—based on qualitative interviews. For the conception and implementation of the online reputation analysis application, a conceptual framework was developed. Employing test installations and prototyping, the essential parts of the framework have been implemented.By following a design sciences research approach, this PhD has created two artifacts: a frameworkand a prototype as proof of concept. Bothartifactshinge on twocoreelements: a (cluster analysis-based) translation of tags used in the Social Web to a computer-understandable fuzzy grassroots ontology for the Semantic Web, and a (Topic Maps-based) knowledge representation system, which facilitates a natural interaction with the fuzzy grassroots ontology. This is beneficial to the identification of unknown but essential Web data that could not be realized through conventional online reputation analysis. Theinherent structure of natural language supports humans not only in communication but also in the perception of the world. Fuzziness is a promising tool for transforming those human perceptions intocomputer artifacts. Through fuzzy grassroots ontologies, the Social Semantic Web becomes more naturally and thus can streamline online reputation management.

Fuzzy Online Reputation Analysis Framework

The fuzzy online reputation analysis framework, or “foRa” (plural of forum, the Latin word for marketplace) framework, is a method for searching the Social Web to find meaningful information about reputation. Based on an automatic, fuzzy-built ontology, this framework queries the social marketplaces of the Web for reputation, combines the retrieved results, and generates navigable Topic Maps. Using these interactive maps, communications operatives can zero in on precisely what they are looking for and discover unforeseen relationships between topics and tags. Thus, using this framework, it is possible to scan the Social Web for a name, product, brand, or combination thereof and determine query-related topic classes with related terms and thus identify hidden sources. This chapter also briefly describes the youReputation prototype (www.youreputation.org), a free web-based application for reputation analysis. In the course of this, a small example will explain the benefits of the prototype.

Authors Support in the TM4L Environment

The TM4L environment enables the development and use of ontology-aware courseware based on the Semantic Web technology Topic Maps. In this paper we discuss its features in the light of authoring support, giving illustrative examples to highlight its use.

Easy Creation of Semantics-Enhanced Digital Artwork Collections

In this paper we propose an approach for cost-effective employing of semantic technologies to improve the efficiency of searching and browsing of digital artwork collections. It is based on a semi-automatic creation of a Topic Map-based virtual art gallery portal by using existing Topic Maps tools. Such a ‘cheap’ solution could enable small art museums or art-related educational programs that lack sufficient funding for software development and publication infrastructure to take advantage of the emerging semantic technologies. The proposed approach has been used for creating the WSSU Diggs Gallery Portal.

Smartbook: Semantics Inside

This paper presents a vision for the future of the e-books which entails further development of technologies that will facilitate the creation and use of a new generation of "smart" books: e-books that are evolving, highly interactive, customisable, adaptable, intelligent, and furnished with a rich set of collaborative authoring and reading support services. The proposed set of tools will be integrated into an intelligent framework for collaborative book authoring and experiencing called SmartBook. The paper promotes the idea that the semantic technologies, intensively developed recently in connection with the Semantic Web initiative, can be incorporated in the book and become the key factor of making it "smarter".

CV-Muzar - Um ambiente de suporte a comunidades virtuais para apoio à aprendizagem em museus

Este artigo tem como objetivo mostrar que é possível incentivar a aprendizagem em museus através da construção de comunidades virtuais, com base em repositórios de objetos de aprendizagem (OA), ferramentas comunicacionais e produção de OA por parte dos visitantes. O enfoque é incentivar a aprendizagem no sentido de motivar a participação/envolvimento do visitante nas atividades da comunidade virtual. Nesta perspectiva, partimos do pressuposto de que a informação, a comunicação, a interação e a colaboração são essenciais para o processo de aprender no contexto informal dos museus. Acreditamos que a interação e a colaboração são partes integrantes do processo de aprendizagem proporcionado por comunidades virtuais e que o principal recurso de aprendizagem oferecido nessas comunidades são os objetos de aprendizagem. Assim sendo, por meio de um entendimento do aprender baseado na comunicação e na linguagem, percebemos os museus interativos como espaços discursivos em que os visitantes mergulham e por eles são modificados. Neste sentido, argumentamos que as comunidades virtuais de aprendizagem, com a possibilidade de virtualizar a linguagem, são excelentes mecanismos para ampliar o poder comunicacional dos museus, criando novas estratégias comunicativas. Para atingir o objetivo, foi necessário reunir quatro conceitos técnicos da área de informática, são eles: comunidades virtuais de aprendizagem; objetos de aprendizagem; metadados e mapas de tópicos. A junção destes conceitos permitiu a construção do ambiente de comunidade virtual, denominada CV-Muzar. Diante do exposto, de modo a identificar os meios pelos quais se podem motivar os visitantes a realmente produzirem novos conhecimentos durante sua visita informal ao museu, examinando essa questão tanto do ponto de vista quantitativo, como também qualitativamente, foi realizada uma experimentação do ambiente com um grupo de pessoas convidadas segundo suas áreas de formação.

A framework for extracting and representing project knowledge contexts using topic models and dynamic knowledge maps

There is still a lack of effective paradigms and tools for analysing and discovering the contents and relationships of project knowledge contexts in the field of project management. In this paper, a new framework for extracting and representing project knowledge contexts using topic models and dynamic knowledge maps under big data environments is proposed and developed. The conceptual paradigm, theoretical underpinning, extended topic model, and illustration examples of the ontology model for project knowledge maps are presented, with further research work envisaged.

Career-path analysis using optimal matching and self-organizing maps

This paper is devoted to the analysis of career paths and employability. The state-of-the-art on this topic is rather poor in methodologies. Some authors propose distances well adapted to the data, but are limiting their analysis to hierarchical clustering. Other authors apply sophisticated methods, but only after paying the price of transforming the categorical data into continuous, via a factorial analysis. The latter approach has an important drawback since it makes a linear assumption on the data. We propose a new methodology, inspired from biology and adapted to career paths, combining optimal matching and self-organizing maps. A complete study on real-life data will illustrate our proposal.