A methodology of personalized recommendation system on mobile device for digital television viewers

With the increasing of the number of digital television (TV) channels in Thailand, this becomes a problem of information overload for TV viewers. There are mass numbers of TV programs to watch but the information about these programs is poor. Therefore, this work presents a personalized recommendation system on mobile device to recommend a TV program that matches viewer’s interests and/or needs.The main mechanism of the system is content-based similarity analysis (CBSA).Initially, the viewer defines favorite programs, and then the system utilize this list as query to find their annotations on the WWW.These annotations will be used to find other programs that are similar by using CBSA.Finally, all similar programs are grouped to the same class and stored as a dataset in a personal mobile device. For the usage, if a TV program matches the interest and specified time of viewer, the system on mobile device will notify the viewer individually

A methodology of personalized recommendation system on mobile device for digital television viewers

With the increasing of the number of digital television (TV) channels in Thailand, this becomes a problem of information overload for TV viewers. There are mass numbers of TV programs to watch but the information about these programs is poor. Therefore, this work presents a personalized recommendation system on mobile device to recommend a TV program that matches viewer’s interests and/or needs.The main mechanism of the system is content-based similarity analysis (CBSA).Initially, the viewer defines favorite programs, and then the system utilize this list as query to find their annotations on the WWW. These annotations will be used to find other programs that are similar by using CBSA. Finally, all similar programs are grouped to the same class and stored as a dataset in a personal mobile device. For the usage, if a TV program matches the interest and specified time of viewer, the system on mobile device will notify the viewer individually

An ontology-based sentiment classification methodology for online consumer reviews

This paper presents a method of ontology-based sentiment classification to classify and analyse online product reviews of consumers. We implement and experiment with a support vector machines text classification approach based on a lexical variable ontology. After testing, it could be demonstrated that the proposed method can provide more effectiveness for sentiment classification based on text content

The cancerology ontology: designed to support the search of evidence-based oncology from biomedical literatures

This work proposes a new ontology, called the Cancerology, where it faces a problem of unclear analysis in a biomedical text processing because existing ontologies such National Cancer Institute\u27s Thesaurus and Ontology do not offer some information relating to domain specific variations in terms that can be provided by the domain expert. This ontology is experimented through a method of text classification with retrieving the relevant cervix cancer abstracts relating to clinical trials from PubMed. The experimental results show more effectiveness for increasing the accuracy. This demonstrates that the Cancerology may be also effective for other areas of text processing and analysis, especially in the particular domain of oncology literature such as intelligent search service, text mining, and knowledge extraction

Ontology-based knowledge discovery from unstructured and semi-structured text

This dissertation proposes a novel methodology for knowledge discovery in large data sets, with a focus on unstructured and semi-structured textual data. To our knowledge, extracting knowledge from unstructured and semi-structured textual data is a major unsolved problem in the area of knowledge discovery in databases (KDD). The problem becomes particularly acute due to ambiguity and lexical variations in natural language. This thesis seeks to address these problems. Firstly, it proposes a unified methodology, called the Ontologybased Knowledge Discovery in unstructured and semi-structured Text (On-KDT) methodology, to discover knowledge from unstructured/semi-structured texts. This approach leverages semantic information encoded in ontologies to improve the effectiveness of the knowledge extraction processes. Secondly, the On-KDT methodology is validated in three distinct settings. In the first setting, we extract scenarios from natural language software requirements. Extracting scenarios from natural language requirements helps improve the efficiency of the requirements process. In this study, the requirements for a courseregistration system are used as the case study. The On-KDT methodology is applied to extract scenarios describing three distinct components in the system. In the second setting, we extract clinical knowledge from PubMed abstracts. PubMed is a very large collection of biomedical abstracts. To be able to make decisions that bring to bear the latest in biomedical research, clinicians need to read each of these. Searching and perusing such a huge repository is near impossible. In this study, PubMed abstracts relating to cervix cancer are used as the case study. In this dissertation, the On- KDT methodology is used to extract knowledge concerning clinical trials from PubMed abstracts. The knowledge thus extracted is represented in the Clinical Knowledge Markup Language (CKML). This approach has the potential to make effective use of relevant (and continually updated) medical knowledge contained PubMed abstracts possible, leading to potentially better clinical decisions. In the third setting, we extract business rules from process model repositories, where process models are encoded as text artefact. Business rules encode important business constraints (including legislative and regulatory compliance constraints) as well as organizational policies. Organizations are often not adequately careful in documenting, encoding and maintaining repositories of their business rules. Instead, business rules are embedded in the design of a variety of operational artefacts, such as business process models. The ability to extract explicit business rules from such artefacts is important in order to be able to understand, analyse, leverage, deploy and maintain business rules. This dissertation provides an application of the On-KDT methodology in extracting business rules implicit in business process designs. The empirical results reported in this dissertation provide grounds for confidence that the On-KDT methodology may be effective, not only in the settings described above, but potentially in knowledge extraction from other unstructured or semi-structured data repositories as well

Business rules discovery from process design repositories

Traditional process mining approaches focus on extracting process constraints or business rules from repositories of process instances. In this context, process designs or process models tend to be overlooked although they contain information that are valuable for the process of discovering business rules. This paper will propose an alternative approach to process mining in terms of using process designs as the mining resources. We propose a number of techniques for extracting business rules from repositories of business process designs or models, leveraging the well-known Apriori algorithm. Such business rules are then used as a prior knowledge for further analysing, verifying, and modifying process designs

Mining Bug Report Repositories to Identify Significant Information for Software Bug Fixing

Most studies relating to bug reports aims to automatically identify necessary information from bug reports for software bug fixing. Unfortunately, the study of bug reports focuses only on one issue, but more complete and comprehensive software bug fixing would be facilitated by assessing multiple issues concurrently. This becomes a challenge in this study, where it aims to present a method of identifying bug reports at severe level from a bug report repository, together with assembling their related bug reports to visualize the overall picture of a software problem domain. The proposed method is called “mining bug report repositories”. Two techniques of text mining are applied as the main mechanisms in this method. First, classification is applied for identifying severe bug reports, called “bug severity classification”, while “threshold-based similarity analysis” is then applied to assemble bug reports that are related to a bug report at severe level. Our datasets are from three opensource namely SeaMonkey, Firefox, and Core:Layout downloaded from the Bugzilla. Finally, the best models from the proposed method are selected and compared with two baseline methods. For identifying severe bug reports using classification technique, the results show that our method improved accuracy, F1, and AUC scores over the baseline by 11.39, 11.63, and 19% respectively. Meanwhile, for assembling related bug reports using threshold-based similarity technique, the results show that our method improved precision, and likelihood scores over the other baseline by 15.76, and 9.14% respectively. This demonstrate that our proposed method may help increasing chance to fix bugs completely

Mining business rules from business process model repositories

Purpose – Business process has become the core assets of many organizations and it becomes increasing common for most medium to large organizations to have collections of hundreds or even thousands of business process models. The purpose of this paper is to explore an alternative dimension to process mining in which the objective is to extract process constraints (or business rules) as opposed to business process models. It also focusses on an alternative data set – process models as opposed to process instances (i.e. event logs). Design/methodology/approach – The authors present a new method of knowledge discovery to find business activity sequential patterns embedded in process model repositories. The extracted sequential patterns are considered as business rules. Findings – The authors find significant knowledge hidden in business processes model repositories. The hidden knowledge is considered as business rules. The business rules extracted from process models are significant and valid sequential correlations among business activities belonging to a particular organization. Such business rules represent business constraints that have been encoded in business process models. Experimental results have indicated the effectiveness and accuracy of the approach in extracting business rules from repositories of business process models. Social implications – This research will assist organizations to extract business rules from their existing business process models. The discovered business rules are very important for any organization, where rules can be used to help organizations better achieve goals, remove obstacles to market growth, reduce costly mistakes, improve communication, comply with legal requirements, and increase customer loyalty. Originality/value – There has very been little work in mining business process models as opposed to an increasing number of very large collections of business process models. This work has filled this gap with the focus on extracting business rules