Transformation of Extracted Knowledge in Malay Unstructured Documents Into an Interrogative Structured Form

The availability of knowledge discovery operation helps to extract valuable information and knowledge in large volumes of data in structured databases. However, a large portion of the available information is not in structured form but rather collections of text documents in unstructured format, which also implies to Malay unstructured documents. Therefore, structuring characteristics must be imposed to unstructured documents in order to transform information available in unstructured documents into knowledge. A new approach has been established to transform extracted knowledge in Malay unstructured document by identifying, organizing, and structuring them into interrogative structured form. Its architecture is developed based on the implementation of (i) interrogative knowledge identification; (ii) interrogative contextual information; and (iii) interrogative knowledge organization and structuring with Malay knowledge representation by concepts. It utilizes the Malay language corpus; interrogative theory; as well as object-oriented, ontology, and database model. The research involves system development based on architecture of the MalaylK-Ontology, which is being measured by quantitative retrieval performance using the recall and precision metrics. The development of the Retrieval lnterrogative Ontology Analysis Application is used to verify fitness of task for the functionalities and usefulness on the utilization of interrogative contextual information with color coding supplement, additional information annotation, and Malay knowledge representation by concepts. A number of experiments are carried out to quantify the accuracy of knowledge extracted. The MalaylK-Ontology is tested by using stratified random sampling drawn from various sources of Malay unstructured documents such as news, e-mails, articles, magazines, and texts from children story books. The results of the experiments have proved that the approach of MalaylK-Ontology performed well as compared to knowledge extracted manually done by an expert. The results of questionnaires evaluation on the Retrieval lnterrogative Ontology Analysis Application have shown good achievement in understanding the main point of the unstructured document easily and clearly. This is to improve better understanding the process of making sense of information into knowledge, maintaining the meaning of the information and gaining the interpretation of the identical knowledge in unstructured document which facilitate identical knowledge perceived by different people

Soft computing agents for e-health applied to the research and control of unknown diseases

This paper presents an Ontology-based Holonic Diagnostic System (OHDS) that combines the advantages of the holonic paradigm with multi-agent system technology and ontology design, for the organization of unstructured biomedical research into structured disease information. We use ontologies as 'brain' for the holonic diagnostic system to enhance its ability to structure information in a meaningful way and share information fast. To integrate dispersed heterogeneous knowledge available on the web we use a fuzzy mechanism ruled by intelligent agents, which automatically structures the information in the adequate ontology template. Our vision of how this system implementation should be backed by a solid security shield that ensures the privacy and safety of medical information concludes the paper

Ontology Driven Web Extraction from Semi-structured and Unstructured Data for B2B Market Analysis

The Market Blended Insight project1 has the objective of improving the UK business to business marketing performance using the semantic web technologies. In this project, we are implementing an ontology driven web extraction and translation framework to supplement our backend triple store of UK companies, people and geographical information. It deals with both the semi-structured data and the unstructured text on the web, to annotate and then translate the extracted data according to the backend schema

Novel Metaknowledge-based Processing Technique for Multimedia Big Data clustering challenges

Past research has challenged us with the task of showing relational patterns between text-based data and then clustering for predictive analysis using Golay Code technique. We focus on a novel approach to extract metaknowledge in multimedia datasets. Our collaboration has been an on-going task of studying the relational patterns between datapoints based on metafeatures extracted from metaknowledge in multimedia datasets. Those selected are significant to suit the mining technique we applied, Golay Code algorithm. In this research paper we summarize findings in optimization of metaknowledge representation for 23-bit representation of structured and unstructured multimedia data in order toComment: IEEE Multimedia Big Data (BigMM 2015

Integrative Use of Information Extraction, Semantic Matchmaking and Adaptive Coupling Techniques in Support of Distributed Information Processing and Decision-Making

In order to press maximal cognitive benefit from their social, technological and informational environments, military coalitions need to understand how best to exploit available information assets as well as how best to organize their socially-distributed information processing activities. The International Technology Alliance (ITA) program is beginning to address the challenges associated with enhanced cognition in military coalition environments by integrating a variety of research and development efforts. In particular, research in one component of the ITA ('Project 4: Shared Understanding and Information Exploitation') is seeking to develop capabilities that enable military coalitions to better exploit and distribute networked information assets in the service of collective cognitive outcomes (e.g. improved decision-making). In this paper, we provide an overview of the various research activities in Project 4. We also show how these research activities complement one another in terms of supporting coalition-based collective cognition

Text-mining and ontologies: new approaches to knowledge discovery of microbial diversity

Microbiology research has access to a very large amount of public information on the habitats of microorganisms. Many areas of microbiology research uses this information, primarily in biodiversity studies. However the habitat information is expressed in unstructured natural language form, which hinders its exploitation at large-scale. It is very common for similar habitats to be described by different terms, which makes them hard to compare automatically, e.g. intestine and gut. The use of a common reference to standardize these habitat descriptions as claimed by (Ivana et al., 2010) is a necessity. We propose the ontology called OntoBiotope that we have been developing since 2010. The OntoBiotope ontology is in a formal machine-readable representation that enables indexing of information as well as conceptualization and reasoning.Comment: 5 page

Relation Discovery from Web Data for Competency Management

This paper describes a technique for automatically discovering associations between people and expertise from an analysis of very large data sources (including web pages, blogs and emails), using a family of algorithms that perform accurate named-entity recognition, assign different weights to terms according to an analysis of document structure, and access distances between terms in a document. My contribution is to add a social networking approach called BuddyFinder which relies on associations within a large enterprise-wide "buddy list" to help delimit the search space and also to provide a form of 'social triangulation' whereby the system can discover documents from your colleagues that contain pertinent information about you. This work has been influential in the information retrieval community generally, as it is the basis of a landmark system that achieved overall first place in every category in the Enterprise Search Track of TREC2006