Supporting Document-Category Management: An Ontology-based Document Clustering Approach

Automated document-category management, particularly the document clustering, represents an appealing alternative of supporting a user\u27s search, access, and utilization of the ever-increasing corpora of textual. Traditional document clustering techniques generally emphasize on the analysis of document contents and measure document similarity on the basis of the overlap between or among the feature vectors representing individual document. However, it can be problematic and cannot address word mismatch or ambiguity effectively to cluster document at the lexical level. To address problems inherent to the traditional lexicon-based approach, we propose an Ontology-based Document Clustering (ODC) technique, which employs a domain-specific ontology to support the proceeding of document clustering at the conceptual level. We empirically evaluate the effectiveness of the proposed ODC technique, using the lexicon-based and LSI-based document clustering techniques (i.e., HAC and LSI-based HAC) for evaluation purpose. Our comparative analysis results show ODC to be partially effective than HAC and LSI-based HAC, showing higher cluster precision across all levels of cluster recall and statistically significant in F1 measure. In addition, our preliminary analysis on the effect of granularity of concept hierarchy suggests the usage of fine-grained concept hierarchy can make ODC reach to a better performance. Our findings have interesting implications to research and practice, which are discussed together with our future research directions

Preserving User Preferences in Document-Category Management: An Ontology-based Evolution Approach

Preserving the user’s preference in document-category management is essential because it affects his/her search efficiency, cognitive processing load, and satisfaction. Prior research has investigated automated document category evolution by using lexicon-based documentcategory evolution techniques which take into account the document categories previously created by the user. However, comparing documents at the lexical level cannot solve word mismatch or ambiguity problems effectively. To address such problems inherent to the lexicon-based approach, we propose an ONtology-based Category Evolution (ONCE) technique, which uses an appropriate ontology to support document-category evolution at the conceptual level rather than at the lexical level. Specifically, we develop an Ontology Enrichment (OE) technique for automatic leaning of concept descriptors in the adopted ontology. We empirically evaluate the effectiveness of the proposed ONCE technique, using a lexicon-based document-category evolution technique (i.e., CE2) and the hierarchical agglomerative clustering (HAC) technique for benchmark purposes. According to our empirical results, ONCE appears more effective than CE2 and HAC, and achieves higher clustering recall and precision

An Ontology- Based Multi-Document Summarization in Apocalypse Management

With the problem of extended information resources and the remarkable evaluate of data removal, the require of having automated summarization techniques revealed up. As summarization is needed the most at present searching information on the internet, where the user moves for a specific space of passion as per his question, area centered on summaries would provide the best. Ontology based summarization system for is provided. The ontology is a subjective model, which gives the important framework for semantic representation of textual data. In our suggested system implement the hierarchical levels of ontology to even more enhance the high summary and to execute hierarchical text classification in the field of earth quake management. We signify a scientific study of different techniques in which ontology has been applied for summarization practice. Comprehensive experiments on a selection of press launch appropriate to 2011 Sikkim earth quake illustrate that ontology centered multiple documents summarization techniques outperforms other baselines with regards to the conclusion top quality. Also we are designing a Hierarchical clustering algorithm instead of K-means clustering algorithm for better precision. It is found that the greater part of the current techniques often focus on sentence scoring and less attention is given to the appropriate information content in various records

A Query Integrator and Manager for the Query Web

We introduce two concepts: the Query Web as a layer of interconnected queries over the document web and the semantic web, and a Query Web Integrator and Manager (QI) that enables the Query Web to evolve. QI permits users to write, save and reuse queries over any web accessible source, including other queries saved in other installations of QI. The saved queries may be in any language (e.g. SPARQL, XQuery); the only condition for interconnection is that the queries return their results in some form of XML. This condition allows queries to chain off each other, and to be written in whatever language is appropriate for the task. We illustrate the potential use of QI for several biomedical use cases, including ontology view generation using a combination of graph-based and logical approaches, value set generation for clinical data management, image annotation using terminology obtained from an ontology web service, ontology-driven brain imaging data integration, small-scale clinical data integration, and wider-scale clinical data integration. Such use cases illustrate the current range of applications of QI and lead us to speculate about the potential evolution from smaller groups of interconnected queries into a larger query network that layers over the document and semantic web. The resulting Query Web could greatly aid researchers and others who now have to manually navigate through multiple information sources in order to answer specific questions

An Event-Ontology-Based Approach to Constructing Episodic Knowledge from Unstructured Text Documents

Document summarization is an important function for knowledge management when a digital library of text documents grows. It allows documents to be presented in a concise manner for easy reading and understanding. Traditionally, document summarization adopts sentence-based mechanisms that identify and extract key sentences from long documents and assemble them together. Although that approach is useful in providing an abstract of documents, it cannot extract the relationship or sequence of a set of related events (also called episodes). This paper proposes an event-oriented ontology approach to constructing episodic knowledge to facilitate the understanding of documents. We also empirically evaluated the proposed approach by using instruments developed based on Bloom’s Taxonomy. The result reveals that the approach based on proposed event-oriented ontology outperformed the traditional text summarization approach in capturing conceptual and procedural knowledge, but the latter was still better in delivering factual knowledge

Ontology-based Document Spanning Systems for Information Extraction

Information Extraction (IE) is the task of automatically organizing in a structured form data extracted from free text documents. In several contexts, it is often desirable that extracted data are then organized according to an ontology, which provides a formal and conceptual representation of the domain of interest. Ontologies allow for a better data interpretation, as well as for their semantic integration with other information, as in Ontology-based Data Access (OBDA), a popular declarative framework for data management where an ontology is connected to a data layer through mappings. However, the data layer considered so far in OBDA has consisted essentially of relational databases, and how to declaratively couple an ontology with unstructured data sources is still unexplored. By leveraging the recent study on document spanners for rule-based IE by Fagin et al., in this paper we propose a new framework that allows to map text documents to ontologies, in the spirit of OBDA. We investigate the problem of answering conjunctive queries in this framework. For ontologies specified in the Description Logics DL-LiteR and DL-LiteF , we show that the problem is polynomial in the size of the underlying documents. We also provide algorithms to solve query answering by rewriting the input query on the basis of the ontology and its mapping towards the source documents. Through these techniques we pursue a virtual approach, similar to that typically adopted in OBDA, which allows us to answer a query without having to first populate the entire ontology. Interestingly, for DL-LiteR both the spanners used in the mapping and the one computed by the rewriting algorithm belong to the same class of expressiveness. This holds also for DL-LiteF , modulo some limitations on the form of the mapping. These results say that in these cases our framework can be easily implemented by decoupling ontology management and document access, which can be delegated to an external IE system able to compute the extraction rules we use in the mapping

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

Ontology Stack for A Policy Wizard

An ontology provides a common vocabulary through which to share information in a particular area of knowledge, including the key terms, their semantic interconnections and certain rules of inference. The ACCENT policy-based management system uses a policy description language called APPEL and supports policy document formation through the use of a comprehensive user interface wizard. Through the use of OWL (the Web Ontology Language), the core aspects of APPEL have been captured and defined in an ontology. Assigned the acronym genpol, this ontology describes the policy language independent of any user interface or domain-specific policy information. A further ontology has been developed to define common interface features implemented by the policy wizard [17]. This ontology, referred to as wizpol, directly extends genpol. It provides additional information to the language itself, whilst retaining freedom from any domain-specific policy details. Combined, both genpol and wizpol act as a base for defining further domain-specific ontologies which may describe policy options tailored for a particular application. This report presents a technical overview of both the generic policy language ontology (genpol) and the wizard policy ontology (wizpol), expressed in the form of graphical depictions of OWL classes and properties

Модель семантического репозитория текстовых документов для онтологического портала МОНУ

Данная работа является составной частью комплекса прикладных исследований, направленных на разработку онтологического портала менеджмента и оценки национальных ресурсов Украины в области образования и науки путем создания крупномасштабной онтологии национальных образовательных ресурсов и разработки онтологической Web-ориентированной распределенной информационной системы для взаимодействия с этой онтологией. Рассматривается проблема расширения функциональности онтологического портала МОНУ путем построения семантического репозитория текстовых документов (РТД), по сути информационного хранилища (Document Warehouse), которое должно учитывать интересы пользователей и их роли в организации.This work is part of the complex applied researches aimed to developing ontology-based portal management and evaluation of Ukrainian national resources in the field of education and science. This is achieved through the creation of large-scale ontology of national education-state resources and the development of ontological Web-oriented distributed information system to interact with this ontology. The problem was considered to extend the functionality of the ontological MESU portal by building a repository of semantic text documents (RTD), in fact an information storage (Document Warehouse), which should take into account the users’ interests and their roles in the organization