The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Raymond Y. K. Lau - One of the best experts on this subject based on the ideXlab platform.

  • toward a fuzzy Domain Ontology extraction method for adaptive e learning
    IEEE Transactions on Knowledge and Data Engineering, 2009
    Co-Authors: Raymond Y. K. Lau, Dawei Song, T C H Cheung, Jinxing Hao
    Abstract:

    With the widespread applications of electronic learning (e-Learning) technologies to education at all levels, increasing number of online educational resources and messages are generated from the corresponding e-Learning environments. Nevertheless, it is quite difficult, if not totally impossible, for instructors to read through and analyze the online messages to predict the progress of their students on the fly. The main contribution of this paper is the illustration of a novel concept map generation mechanism which is underpinned by a fuzzy Domain Ontology extraction algorithm. The proposed mechanism can automatically construct concept maps based on the messages posted to online discussion forums. By browsing the concept maps, instructors can quickly identify the progress of their students and adjust the pedagogical sequence on the fly. Our initial experimental results reveal that the accuracy and the quality of the automatically generated concept maps are promising. Our research work opens the door to the development and application of intelligent software tools to enhance e-Learning.

  • automatic Domain Ontology extraction for context sensitive opinion mining
    International Conference on Information Systems, 2009
    Co-Authors: Raymond Y. K. Lau, Chapmann C L Lai
    Abstract:

    Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform contextsensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy Domain Ontology based contextsensitive opinion mining system. Our novel Ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product Domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline.

  • towards fuzzy Domain Ontology based concept map generation for e learning
    International Conference on Web-Based Learning, 2007
    Co-Authors: Raymond Y. K. Lau, Albert Y K Chung, Dawei Song, Qiang Huang
    Abstract:

    With the wide spread applications of E-Learning technologies to education at all levels, increasing number of online educational resources and messages are generated from these E-Learning environments. Accordingly, instructors are often overwhelmed by the huge number of messages created by students through online discussion boards. It is quite difficult, if not totally impossible, for instructors to read through and analyze these messages to understand the progress of their students on the fly. As a result, adaptive classroom teaching is handicapped. The main contribution of this paper is the illustration of a novel concept map generation mechanism which is underpinned by a fuzzy Domain Ontology discovery algorithm. The proposed mechanism can automatically construct a concept map based on the messages posted to an online discussion board. Our initial experimental results reveal that the accuracy and the quality of the automatically generated concept maps are promising. Our research work opens the door to the development and application of intelligent software tools to enhance E-Learning.

  • Fuzzy Domain Ontology Discovery for Business Knowledge Management
    IEEE Computational Intelligence Bulletin, 2007
    Co-Authors: Raymond Y. K. Lau
    Abstract:

    Ontology plays an essential role in the formalization of business information (e.g., products, services, relationships of businesses) for effective human-computer interactions. However, engineering of Domain ontologies turns out to be very labor intensive and time consuming. Recently, some machine learning methods have been proposed for automatic discovery of Domain ontologies. Nevertheless, the accuracy and computational ef ciency of the existing methods need to be improved to support large scale Ontology construction for real-world business applications. This paper illustrates a novel fuzzy Domain Ontology discovery algorithm for supporting real-world business Ontology engineering. By combining lexico-syntactic and statistical learning methods, the accuracy and the computational ef ciency of the Ontology discovery process is improved. Empirical studies have con rme d that the proposed method can discover high quality fuzzy Domain Ontology which leads to signi cant improvement in information retrieval performance.

Ahhwee Tan - One of the best experts on this subject based on the ideXlab platform.

  • crctol a semantic based Domain Ontology learning system
    Journal of the Association for Information Science and Technology, 2010
    Co-Authors: Xing Jiang, Ahhwee Tan
    Abstract:

    Domain ontologies play an important role in supporting knowledge-based applications in the Semantic Web. To facilitate the building of ontologies, text mining techniques have been used to perform Ontology learning from texts. However, traditional systems employ shallow natural language processing techniques and focus only on concept and taxonomic relation extraction. In this paper we present a system, known as Concept-Relation-Concept Tuple-based Ontology Learning (CRCTOL), for mining ontologies automatically from Domain-specific documents. Specifically, CRCTOL adopts a full text parsing technique and employs a combination of statistical and lexico-syntactic methods, including a statistical algorithm that extracts key concepts from a document collection, a word sense disambiguation algorithm that disambiguates words in the key concepts, a rule-based algorithm that extracts relations between the key concepts, and a modified generalized association rule mining algorithm that prunes unimportant relations for Ontology learning. As a result, the ontologies learned by CRCTOL are more concise and contain a richer semantics in terms of the range and number of semantic relations compared with alternative systems. We present two case studies where CRCTOL is used to build a terrorism Domain Ontology and a sport event Domain Ontology. At the component level, quantitative evaluation by comparing with Text-To-Onto and its successor Text2Onto has shown that CRCTOL is able to extract concepts and semantic relations with a significantly higher level of accuracy. At the Ontology level, the quality of the learned ontologies is evaluated by either employing a set of quantitative and qualitative methods including analyzing the graph structural property, comparison to WordNet, and expert rating, or directly comparing with a human-edited benchmark Ontology, demonstrating the high quality of the ontologies learned. © 2010 Wiley Periodicals, Inc.

  • learning and inferencing in user Ontology for personalized semantic web search
    Information Sciences, 2009
    Co-Authors: Xing Jiang, Ahhwee Tan
    Abstract:

    User modeling is aimed at capturing the users' interests in a working Domain, which forms the basis of providing personalized information services. In this paper, we present an Ontology based user model, called user Ontology, for providing personalized information service in the Semantic Web. Different from the existing approaches that only use concepts and taxonomic relations for user modeling, the proposed user Ontology model utilizes concepts, taxonomic relations, and non-taxonomic relations in a given Domain Ontology to capture the users' interests. As a customized view of the Domain Ontology, a user Ontology provides a richer and more precise representation of the user's interests in the target Domain. Specifically, we present a set of statistical methods to learn a user Ontology from a given Domain Ontology and a spreading activation procedure for inferencing in the user Ontology. The proposed user Ontology model with the spreading activation based inferencing procedure has been incorporated into a semantic search engine, called OntoSearch, to provide personalized document retrieval services. The experimental results, based on the ACM digital library and the Google Directory, support the efficacy of the user Ontology approach to providing personalized information services.

  • ontosearch a full text search engine for the semantic web
    National Conference on Artificial Intelligence, 2006
    Co-Authors: Xing Jiang, Ahhwee Tan
    Abstract:

    OntoSearch, a full-text search engine that exploits ontological knowledge for document retrieval, is presented in this paper. Different from other Ontology based search engines, OntoSearch does not require a user to specify the associated concepts of his/her queries. Domain Ontology in OntoSearch is in the form of a semantic network. Given a keyword based query, OntoSearch infers the related concepts through a spreading activation process in the Domain Ontology. To provide personalized information access, we further develop algorithms to learn and exploit user Ontology model based on a customized view of the Domain Ontology. The proposed system has been applied to the Domain of searching scientific publications in the ACM Digital Library. The experimental results support the efficacy of the OntoSearch system by using Domain Ontology and user Ontology for enhanced search performance.

Konstantinos Tarabanis - One of the best experts on this subject based on the ideXlab platform.

Yonggi Kim - One of the best experts on this subject based on the ideXlab platform.

  • opinion mining based on fuzzy Domain Ontology and support vector machine
    Applied Soft Computing, 2016
    Co-Authors: Farman Ali, Kyung Sup Kwak, Yonggi Kim
    Abstract:

    The available classical Ontology-based systems are inadequate and limit the information extraction from the internet.An Ontology with fuzzy logic is effective technology for precise information extraction from blurred data environment.We proposed fuzzy Domain Ontology with SVM to extract feature's opinion from reviews and to compute polarity.The result of opinion mining by using SVM with FDO for online large data set is better than SVM-based existing systems.The proposed system thoroughly explains the feature extraction and polarity computation. With the explosion of Social media, Opinion mining has been used rapidly in recent years. However, a few studies focused on the precision rate of feature review's and opinion word's extraction. These studies do not come with any optimum mechanism of supplying required precision rate for effective opinion mining. Most of these studies are based on Naive Bayes, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and classical Ontology. These systems are still imperfect for classifying the feature reviews into more degrees of polarity terms (strong negative, negative, neutral, positive and strong positive). Further, the existing classical Ontology-based systems cannot extract blurred information from reviews; thus, it provides poor results. In this regard, this paper proposes a robust classification technique for feature review's identification and semantic knowledge for opinion mining based on SVM and Fuzzy Domain Ontology (FDO). The proposed system retrieves a collection of reviews about hotel and hotel features. The SVM identifies hotel feature reviews and filter out irrelevant reviews (noises) and the FDO is then used to compute the polarity term of each feature. The amalgamation of FDO and SVM significantly increases the precision rate of review's and opinion word's extraction and accuracy of opinion mining. The FDO and intelligent prototype are developed using Protege OWL-2 (Ontology Web Language) tool and JAVA, respectively. The experimental result shows considerable performance improvement in feature review's classification and opinion mining.

Deepak S Yavagal - One of the best experts on this subject based on the ideXlab platform.

  • building decision support problem Domain Ontology from natural language requirements for software assurance
    International Journal of Software Engineering and Knowledge Engineering, 2006
    Co-Authors: Seok Won Lee, Divya Muthurajan, Robin A Gandhi, Deepak S Yavagal, Gailjoon Ahn
    Abstract:

    The process of engineering software-intensive systems that comply with their Certification and Accreditation (C&A) requirements involves many critical decision-making activities for the related stakeholders. Considering the exhaustive nature of C&A activities together with the complexity of software-intensive systems, effective decision making relies heavily on the ways to understand and structure the problem Domain concepts concerning decision points for interpretation, applicability, scope, evaluation, and impact of the enforced C&A requirements. These decision points are further complicated by natural language specifications of inherently non-functional C&A requirements scattered across multiple regulatory documents with complex interdependencies at different levels of abstractions in the organizational hierarchy, which often result in subjective interpretations and non-standard implementations of the C&A process. To address these issues, we define a systematic methodology using novel techniques from software Requirements Engineering (RE) and knowledge engineering for understanding and structuring the problem Domain concepts based on a uniform representation format that promotes common understanding among stakeholders. Specifically, we use advanced ontological engineering techniques driven by theoretical RE foundations to systematically elicit, model, understand, and analyze problem Domain concepts concerning significant and difficult decision points throughout the C&A process. We demonstrate the appropriateness of our methodology in creating decision support problem Domain Ontology using several examples derived from our experiences on automating the Department of Defense Information Technology Security C&A Process (DITSCAP).

  • building problem Domain Ontology from security requirements in regulatory documents
    International Conference on Software Engineering, 2006
    Co-Authors: Robin A Gandhi, Divya Muthurajan, Deepak S Yavagal
    Abstract:

    Establishing secure systems assurance based on Certification and Accreditation (C&A) activities, requires effective ways to understand the enforced security requirements, gather relevant evidences, perceive related risks in the operational environment, and reveal their causal relationships with other Domain concepts. However, C&A security requirements are expressed in multiple regulatory documents with complex interdependencies at different levels of abstractions that often result in subjective interpretations and non-standard implementations. Their non-functional nature imposes complex constraints on the emergent behavior of software-intensive systems, making them hard to understand, predict, and control. To address these issues, we present novel techniques from software requirements engineering and knowledge engineering for systematically extracting, modeling, and analyzing security requirements and related concepts from multiple C&A-enforced regulatory documents. We employ advanced ontological engineering processes as our primary modeling technique to represent complex and diverse characteristics of C&A security requirements and related Domain knowledge. We apply our methodology to build problem Domain Ontology from regulatory documents enforced by the Department of Defense Information Technology Security Certification and Accreditation Process (DITSCAP).