Modeling views in the layered view model for XML using UML

In data engineering, view formalisms are used to provide flexibility to users and user applications by allowing them to extract and elaborate data from the stored data sources. Conversely, since the introduction of Extensible Markup Language (XML), it is fast emerging as the dominant standard for storing, describing, and interchanging data among various web and heterogeneous data sources. In combination with XML Schema, XML provides rich facilities for defining and constraining user-defined data semantics and properties, a feature that is unique to XML. In this context, it is interesting to investigate traditional database features, such as view models and view design techniques for XML. However, traditional view formalisms are strongly coupled to the data language and its syntax, thus it proves to be a difficult task to support views in the case of semi-structured data models. Therefore, in this paper we propose a Layered View Model (LVM) for XML with conceptual and schemata extensions. Here our work is three-fold; first we propose an approach to separate the implementation and conceptual aspects of the views that provides a clear separation of concerns, thus, allowing analysis and design of views to be separated from their implementation. Secondly, we define representations to express and construct these views at the conceptual level. Thirdly, we define a view transformation methodology for XML views in the LVM, which carries out automated transformation to a view schema and a view query expression in an appropriate query language. Also, to validate and apply the LVM concepts, methods and transformations developed, we propose a view-driven application development framework with the flexibility to develop web and database applications for XML, at varying levels of abstraction

Constructing a Personal Knowledge Graph from Disparate Data Sources

This thesis revolves around the idea of a Personal Knowledge Graph as a uniform coherent structure of personal data collected from multiple disparate sources: A knowledge base consisting of entities such as persons, events, locations and companies interlinked with semantically meaningful relationships in a graph structure where the user is at its center. The personal knowledge graph is intended to be a valuable resource for a digital personal assistant, expanding its capabilities to answer questions and perform tasks that require personal knowledge about the user. We explored techniques within Knowledge Representation, Knowledge Extraction/ Information Extraction and Information Management for the purpose of constructing such a graph. We show the practical advantages of using Knowledge Graphs for personal information management, utilizing the structure for extracting and inferring answers and for handling resources like documents, emails and calendar entries. We have proposed a framework for aggregating user data and shown how existing ontologies can be used to model personal knowledge. We have shown that a personal knowledge graph based on the user's personal resources is a viable concept, however we were not able to enrich our personal knowledge graph with knowledge extracted from unstructured private sources. This was mainly due to sparsity of relevant information, the informal nature and the lack of context in personal correspondence

Web competitive intelligence methodology

Master’s Degree DissertationThe present dissertation covers academic concerns in disruptive change that causes value displacements in today’s competitive economic environment. To enhance survival capabilities organizations are increasing efforts in more untraditional business value assets such intellectual capital and competitive intelligence. Dynamic capabilities, a recent strategy theory states that companies have to develop adaptive capabilities to survive disruptive change and increase competitive advantage in incremental change phases. Taking advantage of the large amount of information in the World Wide Web it is propose a methodology to develop applications to gather, filter and analyze web data and turn it into usable intelligence (WeCIM). In order to enhance information search and management quality it is proposed the use of ontologies that allow computers to “understand” particular knowledge domains. Two case studies were conducted with satisfactory results. Two software prototypes were developed according to the proposed methodology. It is suggested that even a bigger step can be made. Not only the success of the methodology was proved but also common software architecture elements are present which suggests that a solid base can be design for different field applications based on web competitive intelligence tools

A holistic approach for semantic-based game generation

The Web contains vast sources of content that could be reused to reduce the development time and effort to create games. However, most Web content is unstructured and lacks meaning for machines to be able to process and infer new knowledge. The Web of Data is a term used to describe a trend for publishing and interlinking previously disconnected datasets on the Web in order to make them more valuable and useful as a whole. In this paper, we describe an innovative approach that exploits Semantic Web technologies to automatically generate games by reusing Web content. Existing work on automatic game content generation through algorithmic means focuses primarily on a set of parameters within constrained game design spaces such as terrains or game levels, but does not harness the potential of already existing content on the Web for game generation. We instead propose a holistic and more generally-applicable game generation solution that would identify suitable Web information sources and enrich game content with semantic meta-structures.The research work disclosed in this publication is partially funded by the REACH HIGH Scholars Programme — Post- Doctoral Grants. The grant is part-financed by the European Union, Operational Programme II — Cohesion Policy 2014- 2020 Investing in human capital to create more opportunities and promote the wellbeing of society — European Social Fund.peer-reviewe

Semantic Interaction in Web-based Retrieval Systems : Adopting Semantic Web Technologies and Social Networking Paradigms for Interacting with Semi-structured Web Data

Existing web retrieval models for exploration and interaction with web data do not take into account semantic information, nor do they allow for new forms of interaction by employing meaningful interaction and navigation metaphors in 2D/3D. This thesis researches means for introducing a semantic dimension into the search and exploration process of web content to enable a significantly positive user experience. Therefore, an inherently dynamic view beyond single concepts and models from semantic information processing, information extraction and human-machine interaction is adopted. Essential tasks for semantic interaction such as semantic annotation, semantic mediation and semantic human-computer interaction were identified and elaborated for two general application scenarios in web retrieval: Web-based Question Answering in a knowledge-based dialogue system and semantic exploration of information spaces in 2D/3D

Extraction of ontology and semantic web information from online business reports

CAINES, Content Analysis and INformation Extraction System, employs an information extraction (IE) methodology to extract unstructured text from the Web. It can create an ontology and a Semantic Web. This research is different from traditional IE systems in that CAINES examines the syntactic and semantic relationships within unstructured text of online business reports. Using CAINES provides more relevant results than manual searching or standard keyword searching. Over most extraction systems, CAINES extensively uses information extraction from natural language, Key Words in Context (KWIC), and semantic analysis. A total of 21 online business reports, averaging about 100 pages long, were used in this study. Based on financial expert opinions, extraction rules were created to extract information, an ontology, and a Semantic Web of data from financial reports. Using CAINES, one can extract information about global and domestic market conditions, market condition impacts, and information about the business outlook. A Semantic Web was created from Merrill Lynch reports, 107,533 rows of data, and displays information regarding mergers, acquisitions, and business segment news between 2007 and 2009. User testing of CAINES resulted in recall of 85.91%, precision of 87.16%, and an F-measure of 86.46%. Speed with CAINES was also greater than manually extracting information. Users agree that CAINES quickly and easily extracts unstructured information from financial reports on the EDGAR database

Implementation and Web Mounting of the WebOMiner_S Recommendation System

The ability to quickly extract information from a large amount of heterogeneous data available on the web from various Business to Consumer (B2C) or Ecommerce stores selling similar products (such as Laptops) for comparative querying and knowledge discovery remains a challenge because different web sites have different structures for their web data and web data are unstructured. For example: Find out the best and cheapest deal for Dell Laptop comparing BestBuy.ca and Amazon.com based on the following specification: Model: Inspiron 15 series, ram: 16gb, processor: i5, Hdd: 1 TB. The “WebOMiner” and “WebOMiner_S” systems perform automatic extraction by first parsing web html source code into a document object model (DOM) tree before using some pattern mining techniques to discover heterogeneous data types (e.g. text, image, links, lists) so that product schemas are extracted and stored in a back-end data warehouse for querying and recommendation. Although a web interface application of this system needs to be developed to make it accessible for to all users on the web.This thesis proposes a Web Recommendation System through Graphical User Interface, which is mounted readily on the web and is accessible to all users. It also performs integration of the web data consisting of all the product features such as Product model name, product description, market price subject to the retailer, etc. retained from the extraction process. Implementation is done using “Java server pages (JSP)” as the GUI designed in HTML, CSS, JavaScript and the framework used for this application is “Spring framework” which forms a bridge between the GUI and the data warehouse. SQL database is implemented to store the extracted product schemas for further integration, querying and knowledge discovery. All the technologies used are compatible with UNIX system for hosting the required application