Alisa Kongthon

Mapping the knowledge evolution and professional network in the field of technology roadmapping: a bibliometric analysis

To maintain a competitive advantage, organisations constantly adjust their operations and strategies to incorporate changes in their business environment. Technology roadmapping (TRM) has emerged as a method that can link technology development with the changing business requirements. Since the method of TRM is fairly new, publications that can explain and assist the implementation of a roadmapping process are still limited. As the concept of TRM continues to gain awareness among scholars and practitioners, the need for further development of TRM related knowledge becomes a vital part of their activities. The purpose of this paper is not only to reveal the body of knowledge and existing networks of TRM professionals but also to suggest potential collaborations among researchers and practitioners who have similar interests in the field. This study applies the bibliometric technique to analyse TRM related journal and conference articles published during 1987 and 2010.

The Role of Social Media During a Natural Disaster: A Case Study of the 2011 Thai Flood

Recently, social media has become a key platform that allowed people to interact and share information. The use of social media is expanding significantly and can serve a variety of purposes. Over the last few years, users of social media have played an increasing role in the dissemination of emergency and disaster information. In this paper, we conduct a case study exploring how Thai people used social media such as Twitter in response to one of the countrys worst disasters in recent history: the 2011 Thai Flood. We combine multiple analysis methods in this study, including content analysis of Twitter messages, trend analysis of different message categories, and influential Twitter users analysis. This study helps us understand the role of social media in time of natural disaster.

Implementing an online help desk system based on conversational agent

Recently the demand for cost-effective solutions to the customer service problem has increased significantly. By delivering solutions automatically to the customers, an enterprise can essentially reduce their operating and training cost. In this paper, we present the design and implementation of an online help desk system based on conversational agent. Our system exploits the enabling technologies of artificial intelligence and natural language processing to offer an organization the ability to provide customer service much more economically and interactively than with traditional methods. The proposed system demonstrates a new form of e-service for an organization to increase their customer satisfaction and retention leading to competitive advantage over other organizations.

Constructing term thesaurus using text association rule mining

This paper presents a new algorithm called ldquoconcept-groupingrdquo that adapts an association rule mining technique to construct term thesaurus for data preprocessing purpose. Similar terms, which are written differently, can be grouped together into the same concept based on their associations before they are used for subsequent analysis. This data preprocessing is important since it has an impact on the quality of other data mining techniques such as data clustering. The algorithm is applied to bibliographic databases such as INSPEC and EI Compendex toward the objective of enhancing traditional bibliometrics and content analysis. From the experiments with a set of publication abstracts, applying the proposed algorithm to combine similar terms into a pertinent concept before clustering process yields better cluster quality.

Expert identification for multidisciplinary R&D project collaboration

A large-scale R&D project collaboration requires various areas of expertise, i.e, multidisciplinary, with multiple partners. Such R&D problems include global warming, emerging infectious diseases, and energy issues. One typical approach for identifying a group of expert candidates is to first come up with an initial expert and then use his/her referral to find additional experts. Hence the traditional process relies significantly on humans and their personal interrelationship. However with an increasing in the availability and accessibility of R&D information in electronic forms, one can apply techniques in the fields of information retrieval, natural language processing, and machine learning to automatically retrieve experts and their areas of expertise from such information sources. In this paper, we present an approach based on the Latent Dirichlet Allocation (LDA) method to discover experts and their associated areas of expertise from R&D bibliographic data. The LDA method could generate multiple hidden topics underlying the given data set. These topics are representatives for those multiple areas of expertise in which individual experts could be assigned into. As an illustration, we apply our approach to analyze abstracts from Compendex database in the domain of Emerging Infectious Diseases (EIDs). Our approach can help enhance the traditional expert identification process in term of topical coverage and unbiased selection of expert candidates.

Mapping the knowledge evolution and network of technology roadmapping (TRM)

As the concept of technology roadmapping (TRM) continues to gain more acceptances from practitioners, many researches are undertaking to improve the visualization of a roadmap as well as to operationalize the process so that a roadmap can be effectively kept alive. This presentation presents the evolution and the current profile of the body of knowledge in the technology roadmapping (TRM) field. Text-mining technique was applied to analyze journal and conference publications related to TRM subjects which are listed in the electronic databases of Web of Science and IEEE Explore.

Enhancing the Literature Review Using Author-Topic Profiling

In this paper, we utilize bibliographic data for identifying author-topic relations which can be used to enhance the traditional literature review. When writing a research paper, researchers often cite on the order of tens of references which do not provide the complete coverage of the research context especially when the targeted research is multidisciplinary. Author-topic profiling can help researchers discover a broader picture of their topic of interest including topical relationships and research community. We apply the Latent Dirichlet Allocation (LDA) to generate multinomial distributions over words and topics to discover author-topic relations from text collections. As an illustration, we apply the methodology to bibliographic abstracts related to Emerging Infectious Diseases (EIDs) research topic.

Automatically Constructing Areas of Expertise Based on R&D Publication Data

For developing countries such as Thailand where the number of knowledge workers is quite limited, a complete national researcher profile database is essential to locate experts in various fields in order to foster collaborative opportunities. Since the current national researcher database was constructed based upon voluntary responses from researchers to create their profile, we found that the database is incomplete, and particularly the area of expertise field has been empty for a great number of researchers. To solve the problem, we propose several approaches to automatically construct areas of expertise using keywords from R&D publications. The best performance is by using term frequency- inverse document frequency (TF-IDF) with title weighting and keyword merging approach. The evaluating result yields 7% higher performance than the baseline (i.e., term frequency) method.

Finding Key Terms Representing Events from Thai Twitter

In the fast and big data era, we all desire to understand trend or big picture of a story instantly. This work wants to find an automatic approach to extract the good-enough key terms of each event appear in Thai Twitter society. The core idea is to help reducing time for human to do the key term extraction, yet the quality of such selected key terms are acceptable by human and is better than our previous implementation. Our studied approaches focus to work on Thai language and covered preprocessing, feature selections and weighting schemes on three Thai real tweet events with different characteristics. Our experiment comprise four main approaches and a number of hypothesis. Our findings confirm the usefulness of hashtag terms with five or more character length, the benefit of bigram with stop words and the importance of event characteristics. In fact, we conclude to use different approaches for different types of event. The performance and rational evaluations are done by statistical analysis, evaluators voting, and F-Score measurement and are confirmed to be better than previous work twice as much.

A framework for managing R&D for Thai research community using text information exploitation

Science and technology (S&T) information presents a rich resource, vital for managing research and development (R&D) programs. Modern S&T electronic abstract databases such as Science Citation Index and INSPEC provide comprehensive information on research activities in many different domains. These databases mostly include English language publications. However for a country such as Thailand which does not use English as the primary language, some S&T information is represented in Thai language as well. Hence to reflect the whole R&D activities in the country, there is a need to extract intelligence from both English and Thai language research publications. In this paper, we describe our ongoing project to consolidate research content focusing on Thai language. We present a framework called Thailandpsilas Research Information Portal and Search Engine (ThaiReSearch) which integrates research information from various databases such as researchers, research projects, patent records, and publications. The framework also provides an intelligent information analysis module which incorporates the following functions: statistical analysis, natural language processing (NLP), and text mining. By using this system, Thai R&D managers and policy planners could improve their strategic decision-making processes towards a sustainable economy.