Kai-Hsiang Yang

Author Name Disambiguation for Citations Using Topic and Web Correlation

Today, bibliographic digital libraries play an important role in helping members of academic community search for novel research. In particular, author disambiguation for citations is a major problem during the data integration and cleaning process, since author names are usually very ambiguous. For solving this problem, we proposed two kinds of correlations between citations, namely, Topic Correlationand Web Correlation, to exploit relationships between citations, in order to identify whether two citations with the same author name refer to the same individual.The topic correlation measures the similarity between research topics of two citations; while the Web correlation measures the number of co-occurrence in web pages. We employ a pair-wise grouping algorithm to group citations into clusters. The results of experiments show that the disambiguation accuracy has great improvement when using topic correlation and Web correlation, and Web correlation provides stronger evidences about the authors of citations.

Web Appearance Disambiguation of Personal Names Based on Network Motif

Searching for information about a particular person is a common activity on search engines. However, current search engines do not provide any special function for search a person. Previous research has solved the problem by using additional background knowledge, such as a friend list, to cluster the searched Web pages. However, it is still difficult to retrieve and choose suitable background knowledge. In this paper, we propose a Web appearance disambiguation (WAD) system to solve the problem by only using the hyperlink structures between Web pages. The key idea of the WAD system is to find out smaller node motifs as evidences of close relationship between pages for clustering searched Web pages. Our experimental results show that, under no background knowledge, the performance of the WAD system achieves 70% for the F-measure

A Reviewer Recommendation System Based on Collaborative Intelligence

In this paper, expert-finding problem is transformed to a classification issue. We build a knowledge database to represent the expertise characteristic of domain from web information constructed by collaborative intelligence, and an incremental learning method is proposed to update the database. Furthermore, results are ranked by measuring the correlation in the concept network from online encyclopedia. In our experiments, we use the real world dataset which comprise 2,701 experts who are categorized into 8 expertise domains. Our experimental results show that the expertise knowledge extracted from collaborative intelligence can improve efficiency and effect of classification and increase the precision of ranking expert at least 20%.

Time-Critical Data Dissemination in Cooperative Peer-to-Peer Systems

How to rapidly disseminate a large-sized file to many recipients is a fundamental problem in many applications, such as updating software patches and distributing large scientific data sets. In this paper, we present the Bee protocol, which is a cooperative peer-to-peer data dissemination protocol aiming at minimizing the maximum dissemination time for all peers to obtain time-critical data, such as critical patch updates. Bee is a decentralized protocol that organizes peers into a randomized mesh-based overlay and each peer only works with local knowledge. We devise a slowest peer first strategy to boost the speed of dissemination, and a topology adaptation algorithm that provides the most efficient utilization of the network capacity. Bee is designed to support network heterogeneity and deal with the flash crowd arrival pattern without sacrificing the dissemination speed. We present experimental results on the performance of Bee in terms of dissemination time and show that its performance can approach lower bound of the maximum dissemination time.

Development and application of a repertory grid-oriented knowledge construction augmented reality learning system for context-aware ubiquitous learning

The rapid advance of wireless communication and sensor technologies has encouraged researchers to engage in the research issues of context-aware ubiquitous learning u-learning. However, it is still not certain whether the new learning scenario is beneficial to students in a context-aware ubiquitous learning environment. In this study, a repertory grid-oriented mobile knowledge construction augmented reality learning system was developed for context-aware ubiquitous learning. To evaluate the effectiveness of the proposed approach, an experiment was conducted to probe the feasibility of the proposed learning strategy in comparison with learning strategies of different learning systems. The results reveal that the proposed approach can facilitate the acquisition of conceptions; moreover, incorporating AR technology had a potential positive effect on the learning achievements of the students in comparison with the conventional approach. Such findings offer good references for those who intend to design and conduct augmented reality technique based mobile learning activities.

Using google distance for query expansion in expert finding

Expert finding, which identifies people with relevant knowledge or skills, is one of the most important issues under many circumstances. In this paper, we propose a method that utilizes Normalized Google Distance (NGD) with some global factors to enhance the relevance between initial query and extended query, and to improve the accuracy of the search results of the expert finding system. Results of a numerical study show that the NGD-based method has a higher accuracy than the methods proposed in the literature, and that the NGD-based method is more effective as the number of top results, N, increases. Moreover, the precision rate of our NGD-based method is, on average, higher than that of the other methods in the literature by 5%.

Combining Query Terms Extension and Weight Correlative for Expert Finding

This paper proposes two methods for solving the expert finding problem. In order to enhance the correctness, a C-value method is applied to these methods for query expansion. After query expansion, proposed system calculates correlation between all query terms and experts, and finally outputs a list of experts. The experiment results show that the proposed methods can provide higher precision than the baseline method from 3% to 10%.

Parsing Publication Lists on the Web

Researchers usually present their publication records (we call citation records in this paper) on publication lists on the Web, which could be an important data source for many applications to collect more publication records than from some digital libraries, such as DBLP. However, it is still not easy to design an algorithm to extract citation records from publication lists because of the diversity of page layouts and citation formats. In this paper, we propose an automatic approach to extract citation records from publication list pages by utilizing two properties. First, citation records are usually represented as nodes at the same level in the DOM tree. Second, citation records in the same page are presented by similar HTML tags. Extensive experiments are conducted to measure the effects of all parameters and system performance. Experiment results show that our approach performs stable and well (with 86.2% of F-measure on average).

Mining Translations of Chinese Names from Web Corpora Using a Query Expansion Technique and Support Vector Machine

Chinese name translation is a special case of the problem of named entity translation. It is a very challenging problem because there exist many kinds of Romanization systems and some people like to add additional words into their english names. Translating a scholars name to its corresponding English name could help find information about his academic achievements. In this paper, we provide a classification for Chinese names, and propose a novel approach to mining Chinese name translations from Web corpora. Our approach is based on three kinds of features, namely the phonetic similarity, the smallest distance, and the number of appearances in the neighborhood, to extract name translation candidates by using a query expansion technique and support vector machine (SVM). Experimental results show that our approach can correctly translate the majority of Chinese names.

A Peer-Assessment Mobile Kung Fu Education Approach to Improving Students' Affective Performances

Digital Library; Aluminium Industry Abstracts; Australian Education Index; Bacon’s Media Directory; Burrelle’s Media Directory; Cabell’s Directories; Ceramic Abstracts; Compendex (Elsevier Engineering Index); Computer & Information Systems Abstracts; Corrosion Abstracts; CSA Civil Engineering Abstracts; CSA Illumina; CSA Mechanical & Transportation Engineering Abstracts; DBLP; DEST Register of Refereed Journals; EBSCOhost’s Academic Search; EBSCOhost’s Academic Source; EBSCOhost’s Business Source; EBSCOhost’s Computer & Applied Sciences Complete; EBSCOhost’s Computer Science Index; EBSCOhost’s Computer Source; EBSCOhost’s Current Abstracts; EBSCOhost’s Science & Technology Collection; Electronics & Communications Abstracts; Engineered Materials Abstracts; ERIC – Education Resources Information Center; GetCited; Google Scholar; INSPEC; JournalTOCs; KnowledgeBoard; Library & Information Science Abstracts (LISA); Materials Business File Steels Alerts; MediaFinder; Norwegian Social Science Data Services (NSD); PsycINFO®; PubList.com; SCOPUS; Solid State & Superconductivity Abstracts; The Index of Information Systems Journals; The Standard Periodical Directory; Thomson Reuters; Ulrich’s Periodicals Directory Research Articles