Chang-Shing Lee

A fuzzy ontology and its application to news summarization

In this paper, a fuzzy ontology and its application to news summarization are presented. The fuzzy ontology with fuzzy concepts is an extension of the domain ontology with crisp concepts. It is more suitable to describe the domain knowledge than domain ontology for solving the uncertainty reasoning problems. First, the domain ontology with various events of news is predefined by domain experts. The document preprocessing mechanism will generate the meaningful terms based on the news corpus and the Chinese news dictionary defined by the domain expert. Then, the meaningful terms will be classified according to the events of the news by the term classifier. The fuzzy inference mechanism will generate the membership degrees for each fuzzy concept of the fuzzy ontology. Every fuzzy concept has a set of membership degrees associated with various events of the domain ontology. In addition, a news agent based on the fuzzy ontology is also developed for news summarization. The news agent contains five modules, including a retrieval agent, a document preprocessing mechanism, a sentence path extractor, a sentence generator, and a sentence filter to perform news summarization. Furthermore, we construct an experimental website to test the proposed approach. The experimental results show that the news agent based on the fuzzy ontology can effectively operate for news summarization.

A Type-2 Fuzzy Ontology and Its Application to Personal Diabetic-Diet Recommendation

It has been widely pointed out that classical ontology is not sufficient to deal with imprecise and vague knowledge for some real-world applications like personal diabetic-diet recommendation. On the other hand, fuzzy ontology can effectively help to handle and process uncertain data and knowledge. This paper proposes a novel ontology model, which is based on interval type-2 fuzzy sets (T2FSs), called type-2 fuzzy ontology (T2FO), with applications to knowledge representation in the field of personal diabetic-diet recommendation. The T2FO is composed of 1) a type-2 fuzzy personal profile ontology ( type-2 FPPO); 2) a type-2 fuzzy food ontology ( type-2 FFO); and 3) a type-2 fuzzy-personal food ontology (type-2 FPFO). In addition, the paper also presents a T2FS-based intelligent diet-recommendation agent ( IDRA), including 1) T2FS construction; 2) a T2FS-based personal ontology filter; 3) a T2FS-based fuzzy inference mechanism; 4) a T2FS-based diet-planning mechanism; 5) a T2FS-based menu-recommendation mechanism; and 6) a T2FS-based semantic-description mechanism. In the proposed approach, first, the domain experts plan the diet goal for the involved diabetes and create the nutrition facts of common Taiwanese food. Second, the involved diabetics are requested to routinely input eaten items. Third, the ontology-creating mechanism constructs a T2FO, including a type-2 FPPO, a type-2 FFO, and a set of type-2 FPFOs. Finally, the T2FS-based IDRA retrieves the built T2FO to recommend a personal diabetic meal plan. The experimental results show that the proposed approach can work effectively and that the menu can be provided as a reference for the involved diabetes after diet validation by domain experts.

A Fuzzy Expert System for Diabetes Decision Support Application

An increasing number of decision support systems based on domain knowledge are adopted to diagnose medical conditions such as diabetes and heart disease. It is widely pointed that the classical ontologies cannot sufficiently handle imprecise and vague knowledge for some real world applications, but fuzzy ontology can effectively resolve data and knowledge problems with uncertainty. This paper presents a novel fuzzy expert system for diabetes decision support application. A five-layer fuzzy ontology, including a fuzzy knowledge layer, fuzzy group relation layer, fuzzy group domain layer, fuzzy personal relation layer, and fuzzy personal domain layer, is developed in the fuzzy expert system to describe knowledge with uncertainty. By applying the novel fuzzy ontology to the diabetes domain, the structure of the fuzzy diabetes ontology (FDO) is defined to model the diabetes knowledge. Additionally, a semantic decision support agent (SDSA), including a knowledge construction mechanism, fuzzy ontology generating mechanism, and semantic fuzzy decision making mechanism, is also developed. The knowledge construction mechanism constructs the fuzzy concepts and relations based on the structure of the FDO. The instances of the FDO are generated by the fuzzy ontology generating mechanism. Finally, based on the FDO and the fuzzy ontology, the semantic fuzzy decision making mechanism simulates the semantic description of medical staff for diabetes-related application. Importantly, the proposed fuzzy expert system can work effectively for diabetes decision support application.

Weighted fuzzy mean filters for image processing

Copyright (c) 1997 Elsevier Science B.V. All rights reserved. A new fuzzy filter for the removal of heavy additive impulse noise, called the weighted fuzzy mean (WFM) filter, is proposed and analyzed in this paper. The WFM-filtered output signal is the mean value of the corrupted signals in a sample matrix, and these signals are weighted by a membership grade of an associated fuzzy set stored in a knowledge base. The knowledge base contains a number of fuzzy sets decided by experts or derived from the histogram of a reference image. When noise probability exceeds 0.3, WFM gives very superior performance compared with conventional filters when evaluated by mean absolute error (MAE), mean square error (MSE), peak signal-to-noise-rate (PSNR) and subjective evaluation criteria. For dedicated hardware implementation, WFM is also much simpler than the conventional median filter.

Automated ontology construction for unstructured text documents

Ontology is playing an increasingly important role in knowledge management and the Semantic Web. This study presents a novel episode-based ontology construction mechanism to extract domain ontology from unstructured text documents. Additionally, fuzzy numbers for conceptual similarity computing are presented for concept clustering and taxonomic relation definitions. Moreover, concept attributes and operations can be extracted from episodes to construct a domain ontology, while non-taxonomic relations can be generated from episodes. The fuzzy inference mechanism is also applied to obtain new instances for ontology learning. Experimental results show that the proposed approach can effectively construct a Chinese domain ontology from unstructured text documents.

Combining subjective and objective QoS factors for personalized web service selection

Abstract Web service is an emerging Internet technology to dynamically describe, discover and communicate. According to the Web Service Architecture (WSA) published by W3C, users can find services through the repository Universal Description, Discovery and Integration (UDDI). However, the UDDI may find many web services with similar functions without considering the nonfunctional quality of service (QoS) information. Under such circumstances, users may have difficulty deciding which service is suitable. This paper proposes a fuzzy-based UDDI with QoS support. Unlike many similar researches, the proposed method tries to consider not only the objective factors described by service providers but also the subjective information with trustability evaluations from users who use those services. Genetic algorithm (GA) is adapted to learn user preferences, and fuzzy logic is applied for making decisions. With a fuzzy query interface to input subjective and objective factors, users can determine the most suitable web service for personal use.

Ontology-based intelligent decision support agent for CMMI project monitoring and control

This paper presents an ontology-based intelligent decision support agent (OIDSA) to apply to project monitoring and control (PMC) of capability maturity model integration (CMMI). The OIDSA is composed of three agents, namely a natural language processing agent, a fuzzy inference agent and a performance decision support agent. All the needed information is stored into an ontology repository, including the CMMI ontology and the project personal ontology (PPO), as well as Chinese dictionary. In addition, the natural language processing agent periodically collects the information of the project progress from project member to analyze the features of the terms for semantic concept clustering through document pre-processing and term filter process. Next, based on the CMMI ontology, the project personal ontology, and the processing results from the natural language processing agent, the fuzzy inference agent and performance decision support agent perform an inference mechanism to calculate the completed percentage of the project progress for each project member, then send the results out to the project manager for evaluating the performance of each project member. The experimental results show that the OIDSA can work effectively for PMC of CMMI

Ontology-based fuzzy event extraction agent for Chinese e-news summarization

Abstract An Ontology-based Fuzzy Event Extraction (OFEE) agent for Chinese e-news summarization is proposed in this article. The OFEE agent contains Retrieval Agent (RA), Document Processing Agent (DPA) and Fuzzy Inference Agent (FIA) to perform the event extraction for Chinese e-news summarization. First, RA automatically retrieves Internet e-news periodically, stores them into the e-news repository, and sends them to DPA for document processing. Then, the DPA will utilize the Chinese Part-of-speech (POS) tagger provided by Chinese knowledge information processing group to process the retrieved e-news and filter the Chinese term set by Chinese term filter. Next, the FIA and Event Ontology Filter (EOF) extract the e-news event ontology based on the Chinese term set and domain ontology. Finally, the Summarization Agent (SA) will summarize the e-news by the extracted-event ontology. By the simulation, the proposed method can summarize the Chinese weather e-news effectively.

Current Frontiers in Computer Go

This paper presents the recent technical advances in Monte Carlo tree search (MCTS) for the game of Go, shows the many similarities and the rare differences between the current best programs, and reports the results of the Computer Go event organized at the 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE2009), in which four main Go programs played against top level humans. We see that in 9 × 9, computers are very close to the best human level, and can be improved easily for the opening book; whereas in 19 × 19, handicap 7 is not enough for the computers to win against top level professional players, due to some clearly understood (but not solved) weaknesses of the current algorithms. Applications far from the game of Go are also cited. Importantly, the first ever win of a computer against a 9th Dan professional player in 9 × 9 Go occurred in this event.

Ontological recommendation multi-agent for Tainan City travel

Due to the gradual increase in travel, the travel agent plays an important role in both planning and recommending a personalized travel route. Tainan City, located in the southern Taiwan, is famous for its abundant historic sites and delicious snack food, and it has been one of the top tourist attractions in Taiwan for years. In this paper, we propose an ontological recommendation multi-agent for Tainan City travel. The core technologies of the agent contain the ontology model, fuzzy inference mechanism, and ant colony optimization. The proposed agent can recommend the tourist a personalized travel route to enjoy Tainan City according to the tourists requirements. It includes a context decision agent and a travel route recommendation agent. First, the context decision agent finds a suitable location distance, counts the context relation, and infers the context information based on the tourists requirements and Tainan City travel ontology. Next, the travel route recommendation agent is responsible for finding a personalized tour and plotting this travel route on the Google Map. Finally, the tourist can follow the personalized travel route to enjoy the cultural heritage and the local gourmet food during his stay at Tainan City. The experimental results show that the proposed approach can effectively recommend a travel route matched with the tourists requirements.