Siu Cheung Hui

Automatic fuzzy ontology generation for semantic Web

Ontology is an effective conceptualism commonly used for the semantic Web. Fuzzy logic can be incorporated to ontology to represent uncertainty information. Typically, fuzzy ontology is generated from a predefined concept hierarchy. However, to construct a concept hierarchy for a certain domain can be a difficult and tedious task. To tackle this problem, this paper proposes the FOGA (fuzzy ontology generation framework) for automatic generation of fuzzy ontology on uncertainty information. The FOGA framework comprises the following components: fuzzy formal concept analysis, concept hierarchy generation, and fuzzy ontology generation. We also discuss approximating reasoning for incremental enrichment of the ontology with new upcoming data. Finally, a fuzzy-based technique for integrating other attributes of database to the ontology is proposed

Computational methods for Traditional Chinese Medicine: A survey

Traditional Chinese Medicine (TCM) has been actively researched through various approaches, including computational techniques. A review on basic elements of TCM is provided to illuminate various challenges and progresses in its study using computational methods. Information on various TCM formulations, in particular resources on databases of TCM formulations and their integration to Western medicine, are analyzed in several facets, such as TCM classifications, types of databases, and mining tools. Aspects of computational TCM diagnosis, namely inspection, auscultation, pulse analysis as well as TCM expert systems are reviewed in term of their benefits and drawbacks. Various approaches on exploring relationships among TCM components and finding genes/proteins relating to TCM symptom complex are also studied. This survey provides a summary on the advance of computational approaches for TCM and will be useful for future knowledge discovery in this area.

Neural networks for web content filtering

With the proliferation of harmful Internet content such as pornography, violence, and hate messages, effective content-filtering systems are essential. Many Web-filtering systems are commercially available, and potential users can download trial versions from the Internet. However, the techniques these systems use are insufficiently accurate and do not adapt well to the ever-changing Web. To solve this problem, we propose using artificial neural networks to classify Web pages during content filtering. We focus on blocking pornography because it is among the most prolific and harmful Web content. However, our general framework is adaptable for filtering other objectionable Web material.

Facial expression recognition from line-based caricatures

The automatic recognition of facial expression presents a significant challenge to the pattern analysis and man-machine interaction research community. Recognition from a single static image is particularly a difficult task. In this paper, we present a methodology for facial expression recognition from a single static image using line-based caricatures. The recognition process is completely automatic. It also addresses the computational expensive problem and is thus suitable for real-time applications. The proposed approach uses structural and geometrical features of a user sketched expression model to match the line edge map (LEM) descriptor of an input face image. A disparity measure that is robust to expression variations is defined. The effectiveness of the proposed technique has been evaluated and promising results are obtained. This work has proven the proposed idea that facial expressions can be characterized and recognized by caricatures.

Mining a web citation database for author co-citation analysis

Author co-citation analysis (ACA) has been widely used in bibliometrics as an analytical method in analyzing the intellectual structure of science studies. It can be used to identify authors from the same or similar research fields. However, such analysis method relies heavily on statistical tools to perform the analysis and requires human interpretation. Web Citation Database is a data warehouse used for storing citation indices of Web publications.In this paper,we propose a mining process to automate the ACA based on the Web Citation Database. The mining process uses agglomerative hierarchical clustering (AHC) as the mining technique for author clustering and multidimensional scaling (MDS) for displaying author cluster maps. The clustering results and author cluster map have been incorporated into a citation-based retrieval system known as PubSearch to support author retrieval of Web publications.

Automatic fuzzy ontology generation for semantic help-desk support

Customer service support is an important operation for most multinational manufacturing companies. With the advancement of internet technologies, customer services nowadays are supported through web-based systems. More recently, rapid development of the semantic web and semantic web services has prompted us to develop a semantic help-desk for supporting customer services over the semantic web environment, which is presented in this paper. In particular, a fuzzy formal concept analysis (FCA)-based approach is developed for automatic generation of fuzzy machine service ontology that can deal with uncertain information. The proposed automatic fuzzy ontology generation technique consists of the following steps: fuzzy formal concept analysis, fuzzy conceptual clustering, and ontology generation. As such, the supporting machine services provided by the proposed system will potentially improve customer satisfaction in terms of reducing machine down time and increasing productivity. In this paper, an experiment has also been conducted for performance evaluation. The experimental result shows that the proposed approach has attained good performance in terms of both accuracy and efficiency when the queries are associated with appropriate membership values, and a suitable confident threshold is set

Automatic generation of ontology for scholarly semantic web

Semantic Web provides a knowledge-based environment that enables information to be shared and retrieved effectively. In this research, we propose the Scholarly Semantic Web for the sharing, reuse and management of scholarly information. To support the Scholarly Semantic Web, we need to construct ontology from data which is a tedious and difficult task. To generate ontology automatically, Formal Concept Analysis (FCA) is an effective technique that can formally abstract data as conceptual structures. To enable FCA to deal with uncertainty in data and interpret the concept hierarchy reasonably, we propose to incorporate fuzzy logic into FCA for automatic generation of ontology. The proposed new framework is known as Fuzzy Formal Concept Analysis (FFCA). In this paper, we will discuss the Scholarly Semantic Web, and the ontology generation process from the FFCA framework. In addition, the performance of the FFCA framework for ontology generation will also be evaluated and presented.

A web-based intelligent fault diagnosis system for customer service support

Abstract In traditional help desk service centres, service engineers provide a world-wide customer support service through the use of long-distance telephone calls. Such a mode of support is found to be inefficient, ineffective and generally results in high costs, long service cycles, and poor quality of service. With the advent of the Internet technology, it is possible to deliver customer service support over the World Wide Web. This paper describes a Web-based intelligent fault diagnosis system, known as WebService, to support customer service over the Web. In the WebService system, a hybrid case-based reasoning (CBR) and artificial neural network (ANN) approach is adopted as the intelligent technique for machine fault diagnosis. Instead of using traditional CBR technique for indexing, retrieval and adaptation, the hybrid CBR–ANN approach integrates ANN with the CBR cycle to extract knowledge from service records of the customer service database and subsequently recall the appropriate service records using this knowledge during the retrieval phase.

Associative Classification With Artificial Immune System

Associative classification (AC), which is based on association rules, has shown great promise over many other classification techniques. To implement AC effectively, we need to tackle the problems on the very large search space of candidate rules during the rule discovery process and incorporate the discovered association rules into the classification process. This paper proposes a new approach that we call artificial immune system-associative classification (AIS-AC), which is based on AIS, for mining association rules effectively for classification. Instead of massively searching for all possible association rules, AIS-AC will only find a subset of association rules that are suitable for effective AC in an evolutionary manner. In this paper, we also evaluate the performance of the proposed AIS-AC approach for AC based on large datasets. The performance results have shown that the proposed approach is efficient in dealing with the problem on the complexity of the rule search space, and at the same time, good classification accuracy has been achieved. This is especially important for mining association rules from large datasets in which the search space of rules is huge.

Structural analysis of chat messages for topic detection

Purpose – The purpose of this research is to study the characteristics of chat messages from analysing a collection of 33,121 sample messages gathered from 1,700 sessions of conversations of 72 pairs of MSN Messenger users over a four month duration from June to September of 2005. The primary objective of chat message characterization is to understand the properties of chat messages for effective message analysis, such as message topic detection.Design/methodology/approach – From the study on chat message characteristics, an indicative term‐based categorization approach for chat topic detection is proposed. In the proposed approach, different techniques such as sessionalisation of chat messages and extraction of features from icon texts and URLs are incorporated for message pre‐processing. Naive Bayes, Associative Classification, and Support Vector Machine are employed as classifiers for categorizing topics from chat sessions.Findings – Indicative term‐based approach is superior to the traditional documen...