Enhancing Groupware For Knowledge Management.

Groupware is popularly used for organisational knowledge management. However, present features of groupware, arguably, only allow very limited knowledge management to be carried out

A tacit health care knowledge explication info-structure using contrived knowledge acquisition and representation approaches.

Projek ini telah menghasilkan suatu Info-Struktur Pengeksplikasian Pengetahuan Kesihatan Tersirat yang mampu mempero1ehi, menyimpan dan menyebarkan pengetahuan kesihatan tersirat untuk digunakan oleh para pakar dan doktor kesihatan supaya perkhidmatan kesihatan yang berkualiti dapat diberi secara berterusan. The project has produced a Tacit Health care Knowledge Explication Info Structure that is designed to acquire, store and disseminate tacit health care knowledge to be used by health care specialists, experts and practitioners to ensure the provision and continuation of expert-quality health care services

Question Classification Using Extreme Learning Machine on Semantic Features

In statistical machine learning approaches for question classification, efforts based on lexical feature space require high computation power and complex data structures. This is due to the large number of unique words (or high dimensionality). Choosing semantic features instead could significantly reduce the dimensionality of the feature space. This article describes the use of Extreme Learning Machine (ELM) for question classification based on semantic features to improve both the training and testing speeds compared to the benchmark Support Vector Machine (SVM) classifier. Improvements have also been made to the head word extraction and word sense disambiguation processes. These have resulted in a higher accuracy (an increase of 0.2%) for the classification of coarse classes compared to the benchmark. For the fine classes, however, there is a 1.0% decrease in accuracy but is compensated by a significant increase in speed (92.1% on average)

Using Semantic Web, Ontologies and Blogs for Knowledge Identification, Organisation and Reuse.

In this paper, we propose an approach to make knowledge-based blogging more interesting, reliable and sophisticated by using semantic web with ontology in the blogging environment. The aim of our research is to realise the knowledge management processes of identification, organisation and reuse (adaptation and application) via the blogging environment for them to be available to users in a seamless application. To achieve knowledge identification, we employ natural language processing techniques coupled with ontologies to identify possible categories of new blog posts. For knowledge organisation, we finalise the categorisation of blog posts and store them into a repository. Finally, in knowledge reuse, we create semantic links between blog posts and other related documents. These processes are integrated in a blogging framework which we call SEMblog

An Open-Domain Question Answering System Using Annotated Web Feeds.

Open Domain Question Answering Systems (ODQA) aim to answer all possible questions, regardless of topic and time. For this to be possible, most current ODQA systems depend on the World Wide Web through search engines, e.g. Google, which provide an abundance of information. Problem arises when search engines require a few days to crawl, archive and index the latest documents depending on the popularity of the website and the search engine's indexing efficiency. Thus, recent information may not be available as soon as it is published. Our work focuses on capturing and populating current news articles from various trusted resources into a single unified repository. We have implemented a prototype which uses Web 2.0 RSS feed technology to capture the information. This includes an interface which allows other question answering systems to access our repository using various formats' e.g. XML and SQL query. We have also implemented a question answering engine which employs keyword detection, query expansion and rule matching

An Enhanced Electronic Health Community With Knowledge-Based E-Mail And Agent-Based Knowledge Search And Sharing.

Electronic health communities have the potential to go beyond providing basic communication-type services such as online chat and discussion group, health directories and specialised portals for healthcare practitioners

Analysing visual field and diagnosing glaucoma progression using a hybrid of per location differences and artificial neural network ensembles

Visual function test results for glaucoma diagnosis is perceived to be subjective and problematic.In this paper, we aim to address the issues and problems associated with these current approaches.We present (a) a system architecture for analyzing visual field and diagnosing glaucoma progression; (b) a per location differences approach for analyzing visual field to obtain measurements of glaucoma progression; and (c) a neural network ensemble approach where several artifial neural network are jointly used to diagnose glaucoma progression.It is hoped that it would be possible to diagnose glaucoma progression with just one reading of a patient’s visual field

A platform for enterprise-wide healthcare knowledge management

The importance of effective information and knowledge management in enterprises has spurred the development of numerous information and knowledge management software.Whilst emphasis is placed on effective document management, the essence of knowledge management is diluted as the focus is presently on managing uninterpreted data and information in document-type formats.To address this issue of the lack of true knowledge management in enterprises, especially in healthcare enterprises, we propose a Platform for Enterprise-Wide Healthcare Knowledge Management (KM-Platform).This platform is made up of two suites of applications and services, i.e. the Intelligent Agent-Based Knowledge Management Application Suite and the Strategic Visualisation, Planning and Coalition Formation Service Suite

Hidden sentiment behind letter repetition in online reviews

Minimal research has been done on how letter repetition affects readers’ perception of expressed sentiment within a text. To the best of the researchers’ knowledge, no studies have tested samples of text with letter repetition using sentiment tools. The main aim of this paper is to investigate whether letter repetition in product reviews are perceived to have any sentiment value, based on ratings by individual participants and analyses using sentiment tools. This study collected and analysed 1,041 consumer reviews in the form of online comments using the UCREL Wmatrix system, and simulated emotional words within the comments to contain repeated letters. A group of 500 participants rated 15 positive comments and 15 negative comments and their respective simulated counterparts, while 32 sentiment tools are used to analyse a pair of positive comment and its simulated counterpart and a pair of negative comment and its simulated counterpart. Results indicate that readers perceive letter repetition to amplify a comment’s sentiment value, in which the effect was found more strongly in negative comments than positive comments. On the other hand, analyses using sentiment tools show that a majority of these tools are unable to detect letter repetition within a word and instead, treats the word as a spelling mistake. As consumers or online users, in general, have been found to use letter repetition to intensify and express their sentiments in their comments, this study’s findings suggest that letter repetition processing in any text-based mechanism needs to be enhanced. The outcome of this paper is useful for improving the measurement of sentiment analysis for the use of marketing applications

Topic Modeling in Sentiment Analysis: A Systematic Review

With the expansion and acceptance of Word Wide Web, sentiment analysis has become progressively popular research area in information retrieval and web data analysis. Due to the huge amount of user-generated contents over blogs, forums, social media, etc., sentiment analysis has attracted researchers both in academia and industry, since it deals with the extraction of opinions and sentiments. In this paper, we have presented a review of topic modeling, especially LDA-based techniques, in sentiment analysis. We have presented a detailed analysis of diverse approaches and techniques, and compared the accuracy of different systems among them. The results of different approaches have been summarized, analyzed and presented in a sophisticated fashion. This is the really effort to explore different topic modeling techniques in the capacity of sentiment analysis and imparting a comprehensive comparison among them