Semantic web-based document: editing and browsing in AktiveDoc

This paper presents a tool for supporting sharing and reuse of knowledge in document creation (writing) and use (reading). Semantic Web technologies are used to support the production of ontology based annotations while the document is written. Free text annotations (comments) can be added to integrate the knowledge in the document. In addition the tool uses external services (e.g. a Semantic Web harvester) to propose relevant content to writing user, enabling easy knowledge reuse. Similar facilities are provided for readers when their task does not coincide with the author’s one. The tool is specifically designed for Knowledge Management in organisations. In this paper we present and discuss how Semantic Web technologies are designed and integrated in the system

An Intelligent Knowledge Management System from a Semantic Perspective

Abstract. Knowledge Management Systems (KMS) are important tools by which organizations can better use information and, more importantly, manage knowledge. Unlike other strategies, knowledge management (KM) is difficult to define because it encompasses a range of concepts, management tasks, technologies, and organizational practices, all of which come under the umbrella of the information management. Semantic approaches allow easier and more efficient training, maintenance, and support knowledge. Current ICT markets are dominated by relational databases and document-centric information technologies, procedural algorithmic programming paradigms, and stack architecture. A key driver of global economic expansion in the coming decade is the build-out of broadband telecommunications and the deployment of intelligent services bundling. This paper introduces the main characteristics of an Intelligent Knowledge Management System as a multiagent system used in a Learning Control Problem (IKMSLCP), from a semantic perspective. We describe an intelligent KM framework, allowing the observer (a human agent) to learn from experience. This framework makes the system dynamic (flexible and adaptable) so it evolves, guaranteeing high levels of stability when performing his domain problem P. To capture by the agent who learn the control knowledge for solving a task-allocation problem, the control expert system uses at any time, an internal fuzzy knowledge model of the (business) process based on the last knowledge model.knowledge management, fuzzy control, semantic technologies, computational intelligence

An Intelligent Knowledge Management System from a Semantic Perspective

Knowledge Management Systems (KMS) are important tools by which organizations can better use information and, more importantly, manage knowledge. Unlike other strategies, knowledge management (KM) is difficult to define because it encompasses a range of concepts, management tasks, technologies, and organizational practices, all of which come under the umbrella of the information management. Semantic approaches allow easier and more efficient training, maintenance, and support knowledge. Current ICT markets are dominated by relational databases and document-centric information technologies, procedural algorithmic programming paradigms, and stack architecture. A key driver of global economic expansion in the coming decade is the build-out of broadband telecommunications and the deployment of intelligent services bundling. This paper introduces the main characteristics of an Intelligent Knowledge Management System as a multiagent system used in a Learning Control Problem (IKMSLCP), from a semantic perspective. We describe an intelligent KM framework, allowing the observer (a human agent) to learn from experience. This framework makes the system dynamic (flexible and adaptable) so it evolves, guaranteeing high levels of stability when performing his domain problem P. To capture by the agent who learn the control knowledge for solving a task-allocation problem, the control expert system uses at any time, an internal fuzzy knowledge model of the (business) process based on the last knowledge model.knowledge management, fuzzy control, semantic technologies, computational intelligence

Coherence Identification of Business Documents: Towards an Automated Message Processing System

This paper describes our recent efforts in developing a text segmentation technique in our business document management system. The document analysis is based upon a knowledge-based analysis of the documents’ contents, by automating the coherence identification process, without a full semantic understanding. In the technique, document boundaries can be identified by observing the shifts of segments from one cluster to another. Our experimental results show that the combination of the heterogeneous knowledge is capable to address the topic shifts. Given the increasing recognition of document structure in the fields of information retrieval as well as knowledge management, this approach provides a quantitative model and automatic classification of documents in a business document management system. This will beneficial to the distribution of documents or automatic launching of business processes in a workflow management system

A Knowledge Management and Decision Support Model for Enterprises

We propose a novel knowledge management system (KMS) for enterprises. Our system exploits two different approaches for knowledge representation and reasoning: a document-based approach based on data-driven creation of a semantic space and an ontology-based model. Furthermore, we provide an expert system capable of supporting the enterprise decisional processes and a semantic engine which performs intelligent search on the enterprise knowledge bases. The decision support process exploits the Bayesian networks model to improve business planning process when performed under uncertainty

Ontologiebasierte Indexierung und Kontextualisierung multimedialer Dokumente für das persönliche Wissensmanagement

Die Verwaltung persönlicher, multimedialer Dokumente kann mit Hilfe semantischer Technologien und Ontologien intelligent und effektiv unterstützt werden. Dies setzt jedoch Verfahren voraus, die den grundlegenden Annotations- und Bearbeitungsaufwand für den Anwender minimieren und dabei eine ausreichende Datenqualität und -konsistenz sicherstellen. Im Rahmen der Dissertation wurden notwendige Mechanismen zur semi-automatischen Modellierung und Wartung semantischer Dokumentenbeschreibungen spezifiziert. Diese bildeten die Grundlage für den Entwurf einer komponentenbasierten, anwendungsunabhängigen Architektur als Basis für die Entwicklung innovativer, semantikbasierter Lösungen zur persönlichen Dokumenten- und Wissensverwaltung.Personal multimedia document management benefits from Semantic Web technologies and the application of ontologies. However, an ontology-based document management system has to meet a number of challenges regarding flexibility, soundness, and controllability of the semantic data model. The first part of the dissertation proposes necessary mechanisms for the semi-automatic modeling and maintenance of semantic document descriptions. The second part introduces a component-based, application-independent architecture which forms the basis for the development of innovative, semantic-driven solutions for personal document and information management

SylvaDB: A Polyglot and Multi-backend Graph Database Management System

This paper presents SylvaDB, a graph database management system designed to be used by people with no technical knowledge. SylvaDB is based on flexible schema definitions and has been developed taking into account the need to deal with semantic information. It relies on the mathematical notion of property graph. SylvaDB is an open source project and aims at lowering the barrier of adoption for anyone using graph databases. At the same time, it is robust and scalable enough to support collaborative large projects related to knowledge management, document archiving, and research

Exploiting multimedia content : a machine learning based approach

Advisors: Prof. M Gopal, Prof. Santanu Chaudhury. Date and location of PhD thesis defense: 10 September 2013, Indian Institute of Technology DelhiThis thesis explores use of machine learning for multimedia content management involving single/multiple features, modalities and concepts. We introduce shape based feature for binary patterns and apply it for recognition and retrieval application in single and multiple feature based architecture. The multiple feature based recognition and retrieval frameworks are based on the theory of multiple kernel learning (MKL). A binary pattern recognition framework is presented by combining the binary MKL classifiers using a decision directed acyclic graph. The evaluation is shown for Indian script character recognition, and MPEG7 shape symbol recognition. A word image based document indexing framework is presented using the distance based hashing (DBH) defined on learned pivot centres. We use a new multi-kernel learning scheme using a Genetic Algorithm for developing a kernel DBH based document image retrieval system. The experimental evaluation is presented on document collections of Devanagari, Bengali and English scripts. Next, methods for document retrieval using multi-modal information fusion are presented. Text/Graphics segmentation framework is presented for documents having a complex layout. We present a novel multi-modal document retrieval framework using the segmented regions. The approach is evaluated on English magazine pages. A document script identification framework is presented using decision level aggregation of page, paragraph and word level prediction. Latent Dirichlet Allocation based topic modelling with modified edit distance is introduced for the retrieval of documents having recognition inaccuracies. A multi-modal indexing framework for such documents is presented by a learning based combination of text and image based properties. Experimental results are shown on Devanagari script documents. Finally, we have investigated concept based approaches for multimedia analysis. A multi-modal document retrieval framework is presented by combining the generative and discriminative modelling for exploiting the cross-modal correlation between modalities. The combination is also explored for semantic concept recognition using multi-modal components of the same document, and different documents over a collection. An experimental evaluation of the framework is shown for semantic event detection in sport videos, and semantic labelling of components of multi-modal document images

Structure and content semantic similarity detection of eXtensible markup language documents using keys

XML (eXtensible Mark-up Language) has become the fundamental standard for efficient data management and exchange. Due to the widespread use of XML for describing and exchanging data on the web, XML-based comparison is central issues in database management and information retrieval. In fact, although many heterogeneous XML sources have similar content, they may be described using different tag names and structures. This work proposes a series of algorithms for detection of structural and content changes among XML data. The first is an algorithm called XDoI (XML Data Integration Based on Content and Structure Similarity Using Keys) that clusters XML documents into subtrees using leaf-node parents as clustering points. This algorithm matches subtrees using the key concept and compares unmatched subtrees for similarities in both content and structure. The experimental results show that this approach finds much more accurate matches with or without the presence of keys in the subtrees. A second algorithm proposed here is called XDI-CSSK (a system for detecting xml similarity in content and structure using relational database); it eliminates unnecessary clustering points using instance statistics and a taxonomic analyzer. As the number of subtrees to be compared is reduced, the overall execution time is reduced dramatically. Semantic similarity plays a crucial role in precise computational similarity measures. A third algorithm, called XML-SIM (structure and content semantic similarity detection using keys) is based on previous work to detect XML semantic similarity based on structure and content. This algorithm is an improvement over XDI-CSSK and XDoI in that it determines content similarity based on semantic structural similarity. In an experimental evaluation, it outperformed previous approaches in terms of both execution time and false positive rates. Information changes periodically; therefore, it is important to be able to detect changes among different versions of an XML document and use that information to identify semantic similarities. Finally, this work introduces an approach to detect XML similarity and thus to join XML document versions using a change detection mechanism. In this approach, subtree keys still play an important role in order to avoid unnecessary subtree comparisons within multiple versions of the same document. Real data sets from bibliographic domains demonstrate the effectiveness of all these algorithms --Abstract, page iv-v