Chao-Chin Wu

Developing a fuzzy search engine based on fuzzy ontology and semantic search

Most of existing search engines retrieve web pages by means of finding exact keywords. Traditional keyword-based search engines suffer several problems. First, synonyms and terms similar to keywords are not taken into consideration to search web pages. Users may need to input several similar keywords individually to complete a search. Second, traditional search engines treat all keywords as the same importance and cannot differentiate the importance of one keyword from that of another. Third, traditional search engines lack an applicable classification mechanism to reduce the search space and improve the search results. In this paper, we develop a fuzzy search engine, called Fuzzy-Go. First, a fuzzy ontology is constructed by using fuzzy logic to capture the similarities of terms in the ontology, which offering appropriate semantic distances between terms to accomplish the semantic search of keywords. The Fuzzy-Go search engine can thus automatically retrieve web pages that contain synonyms or terms similar to keywords. Second, users can input multiple keywords with different degrees of importance based on their needs. The totally satisfactory degree of keywords can be aggregated based on their degrees of importance and degrees of satisfaction. Third, the domain classification of web pages offers users to select the appropriate domain for searching web pages, which excludes web pages in the inappropriate domains to reduce the search space and to improve the search results.

Parallelizing CLIPS-Based Expert Systems by the Permutation Feature of Pattern Matching

CLIPS is a non-algorithmic language designed especially for developing expert systems. To address the problem that CLIPS suffers from long execution time because of the characteristics of rule-based language, previously we have proposed a Grid-enabled parallel CLIPS language and a dynamic load balancing programming model that can parallelize the execution of a CLIPS program automatically if the data can be inferred independently. In this paper, we investigate how to apply the idea of automatic parallelization to other kinds of applications. For instance, a rule usually requires choosing multiple data items from the knowledge base to match with. This kind of matching is a permutation problem. All the different permutations must be divided into partitions and assigned to slaves for independent inferences. A programmer only needs to use three simple directives to provide necessary information to automatically parallelize the execution of an application. Experiment results show that the best speedup is 10.38 when executing a knowledge management system in a heterogeneous cluster system with 12 processor cores.

A self-adaptation approach to Fuzzy-Go search engine

The Fuzzy-Go search engine develops a fuzzy ontology to capture the similarities of terms in the ontology for accomplishing the semantic search of keywords, a web crawler to gather and classify web pages, and a fuzzy search mechanism to aggregate all fuzzy factors based on their degrees of importance and degrees of satisfaction. In this paper, we apply the genetic algorithm to propose a self-adaptation approach to Fuzzy-Go search engine. For each search, the fuzzy search engine records the difference between the ordering of search results and users real behavior on clicking web pages. The feedbacks are gathered and analyzed to adjust the fuzzy similarities between terms in the fuzzy ontology, the domain classification of web pages, and the importance degrees of fuzzy factors. The ordering of search results can thus be improved gradually by continuous learning and adaptation.

Enhanced Parallel Loop Self-Scheduling for Heterogeneous Multi-core Cluster Systems

Recently, more and more studies investigated the is-sue of dealing with the heterogeneity problem on heterogeneous cluster systems consisting of multi-core computing nodes. Previously we have proposed a hybrid MPI and OpenMP based loop self-scheduling approach for this kind of system. The allocation functions of several well-known schemes have been modified for better performance. Though the previous approach can improve system performance significantly, in this paper we present how to enhance the speedup further. First, we exploit the thread-level parallelism on the multi-core master node. Second, we investigate how to design a loop self-scheduling scheme which is able to smartly assign a proper chunk size according to each node’s performance. At the beginning of dispatching, we prevent the slow slaves from being as-signed too many tasks. On the other hand, the master will not assign too many small chunks to slaves at the end. Experimental results show that our approach could obtain the best speedup of 1.35.

Development of knowledge-based system for predicting the stability of proteins upon point mutations

Prediction of protein stability upon amino acid substitution is an important problem in designing stable proteins. We have developed a classification rule generator for integrating the knowledge of amino acid sequence and experimental stability change upon single mutation. These rules are human readable and hence the method enhances the synergy between expert knowledge and computational system. Utilizing the information about wild type, mutant, three neighboring residues and experimentally observed stability data, we have developed a method based on decision tree for discriminating the stabilizing and destabilizing mutants and predicting the protein stability changes upon single point mutations, which showed an accuracy of 82% and a correlation of 0.70, respectively. In addition, we have developed a fuzzy query method to predict protein stability with partial information. We have developed a web server for predicting the protein stability changes upon single mutations by using fuzzy query mechanism and it is available at http://bioinformatics.myweb.hinet.net/fqstab.htm.

A Fuzzy Query Mechanism for Human Resource Websites

Users preferences often contain imprecision and uncertainty that are difficult for traditional human resource websites to deal with. In this paper, we apply the fuzzy logic theory to develop a fuzzy query mechanism for human resource websites. First, a storing mechanism is proposed to store fuzzy data into conventional database management systems without modifying DBMS models. Second, a fuzzy query language is proposed for users to make fuzzy queries on fuzzy databases. Users fuzzy requirement can be expressed by a fuzzy query which consists of a set of fuzzy conditions. Third, each fuzzy condition associates with a fuzzy importance to differentiate between fuzzy conditions according to their degrees of importance. Fourth, the fuzzy weighted average is utilized to aggregate all fuzzy conditions based on their degrees of importance and degrees of matching. Through the mutual compensation of all fuzzy conditions, the ordering of query results can be obtained according to users preference.

First report of knowledge discovery in predicting protein folding rate change upon single mutation

To explore the mechanism of protein folding is one of the important topics in protein research. The accurate prediction of protein folding rate change is helpful and useful in protein design. In earlier study, we have firstly analyzed the prediction of folding rate change upon single point mutation and constructed a non-redundant dataset of F467. F467 consists of 467 mutants with various features and widely distributed on secondary structure, solvent accessibility, conservation score and long-range contacts. In this work, we therefore focused on effectively developing the knowledge in F467 dataset. We have systematically analyzed the dataset and presented several representative data mining techniques, including decision tree, decision table and association rule algorithms. Furthermore, we have interpreted, evaluated, and compared the knowledge obtained from different techniques. The experimental results showed that the present approach can effectively develop the knowledge in the dataset and the outcomes can increase the understanding of predicting protein folding rate change upon single mutation. We have also created a website with related information about this work and it is freely available at http://bioinformatics.myweb.hinet.net/kdfreedom.htm .

Predicting protein stability change upon double mutation from partial sequence information using data mining approach

The prediction of stability change for protein mutants is one of the important issues in protein design. Recently, the prediction upon double mutation has attracted more and more attention. In this work, we have employed a data mining approach to discriminating stability change for protein double mutants. We incorporated a reliable rule induction algorithm along with accuracy of 82.2% to construct rule-based knowledge patterns. Further, a fuzzy query method was utilized to value important and similar rule patterns for an input with partial sequence information. The results showed that the approach has two major advantages: (i) A rule-based knowledge representation offers intuitive interpretation on raw data, which is helpful to understand the content; and (ii) A fuzzy query method incorporates the concept of uncertainty, which can make predictions from partial information. Based on the proposed approach, we have also developed a web service for predicting protein stability change upon double mutation from partial sequence information and it is available at http://bioinformatics.myweb.hinet.net/tandem.htm.

A Batch Fuzzy Query System for Predicting Protein Stability Changes

Predicting protein mutant stability changes is important for protein design. Although many methods have tried to improve prediction accuracy by various models, it will be difficult to employ them when the required input information is incomplete. Therefore, we have proposed a fuzzy query method previously to predict stability changes upon single mutations using partial input information. However, when a researcher has tens or hundreds of queries to predict, he needs to input query data repeatedly. It is better to issue queries in a batch, which incurs another problem. Because our proposed fuzzy query method is implemented in the FuzzyCLIPS language, it takes a long time to process a batch of queries. To shorten the response time, we propose parallelizing queries in emerging cluster systems or grid systems. Furthermore, to address the problem of heterogeneous computing power of cluster and grid nodes, a variety of self-scheduling schemes have been implemented for better load balance. Experimental results show that our proposed parallel fuzzy query system can provide hundreds of predications in more reasonable response time.

Developing the KMKE knowledge management system based on design patterns and parallel processing

KMKE provides a knowledge engineering approach to integrating knowledge management activities (such as knowledge modeling, knowledge verification, knowledge storage and knowledge querying) into a systematic framework. In this paper, we develop the KMKE knowledge management system based on design patterns and parallel processing. First, several design patterns are applied to develop the KMKE system for enhancing its flexibility and extensibility. Making the KMKE system flexible and extensible is useful to deal with continuous changes originated in knowledge. Second, JAVA programs and CLIPS programs are bound to offer the capability of knowledge inference for the KMKE system. Knowledge verification and knowledge querying can then be performed through the execution of CLIPS rules. Finally, we propose the Parallel CLIPS to shorten the execution time of the KMKE system. Since a large amount of knowledge may increase the execution time substantially, parallelizing the execution of CLIPS rules in cluster system could effectively reduce the search space of the CLIPS inference engine.