Browsing by Author "Shen, Minxin"

Now showing 1 - 4 of 4

Complementary QA Network Analysis for QA Retrieval in Social Question-Answering Websites
(2015-01-01)
Liu, Duen-Ren
;
Chen, Yu-Hsuan
;
Shen, Minxin
;
Lu, Pei-Jung
;
資訊管理與財務金融系註：原資管所+財金所
With the ubiquity of the Internet and the rapid development of Web 2.0 technology, social question and answering (SQA) websites have become popular knowledge-sharing platforms. As the number of posted questions and answers (QAs) continues to increase rapidly, the massive amount of question-answer knowledge is causing information overload. The problem is compounded by the growing number of redundant QAs. SQA websites such as Yahoo! Answers are open platforms where users can freely ask or answer questions. Users also may wish to learn more about the information provided in an answer so they can use related keywords in the answer to search for extended, complementary information. In this article, we propose a novel approach to identify complementary QAs (CQAs) of a target QA. We define two types of complementarity: partial complementarity and extended complementarity. First, we utilize a classification-based approach to predict complementary relationships between QAs based on three measures: question similarity, answer novelty, and answer correlation. Then we construct a CQA network based on the derived complementary relationships. In addition, we introduce a CQA network analysis technique that searches the QA network to find direct and indirect CQAs of the target QA. The results of experiments conducted on the data collected from Yahoo! Answers Taiwan show that the proposed approach can more effectively identify CQAs than can the conventional similarity-based method. Case and user study results also validate the helpfulness and the effectiveness of our approach.
Discovering Workflow-Aware Virtual Knowledge Flows for Knowledge Dissemination
(2013-01-01)
Shen, Minxin
;
Liu, Duen-Ren
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
In order to effectively disseminate task-relevant and process-scope knowledge, knowledge-intensive enterprises adopt knowledge flows to explicitly represent workers\' knowledge needs and referencing behavior of codified knowledge during the execution of business tasks. However, due to differences in expertise and experience, individual workers impose varied knowledge needs on the knowledge flows directed by the workflows they participate in. This study proposes a model of workflow-aware knowledge-flow views, i.e. virtual knowledge flows abstracted from workflow-driven knowledge flows, to provide adaptable knowledge granularity. Moreover, a text mining approach is developed to derive knowledge-flow views from codified knowledge objects of knowledge flows, such as documents. Both task knowledge semantics and task execution sequences are utilized to evaluate the degrees of workers\' knowledge demands in workflow contexts. Knowledge management systems can thus present different abstracted knowledge flows to diverse workflow participants, and facilitate knowledge sharing and collaboration.
Extracting semantic relations to enrich domain ontologies
(2012-12-01)
Shen, Minxin
;
Liu, Duen-Ren
;
Huang, Yu-Siang
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Domain ontologies facilitate the organization, sharing and reuse of domain knowledge, and enable various vertical domain applications to operate successfully. Most methods for automatically constructing ontologies focus on taxonomic relations, such as is-kind-of and is-part-of relations. However, much of the domain-specific semantics is ignored. This work proposes a semi-unsupervised approach for extracting semantic relations from domain-specific text documents. The approach effectively utilizes text mining and existing taxonomic relations in domain ontologies to discover candidate keywords that can represent semantic relations. A preliminary experiment on the natural science domain (Taiwan K9 education) indicates that the proposed method yields valuable recommendations. This work enriches domain ontologies by adding distilled semantics.
Outlier detection from vehicle trajectories to discover roaming events
(2015-02-10)
Shen, Minxin
;
Liu, Duen-Ren
;
Shann, Shi-Han
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Roaming, referring to the behavior of repeated observation of intended crime scenes before committing crimes, is a suspicious pattern often mentioned by experienced police investigators, but still remains a vague concept which needs to be well realized in video surveillance systems. This work first describes the scenario of roaming behaviors related to planned crimes, and then derives formal specifications for detecting suspicious roaming events from vehicle trajectories. Consequently, algorithms are designed for rapidly sorting out potential outliers and thoroughly examining their suspicious intention through circling activities, relative driving speed and time dispersion. Roaming trajectories and relevant trajectories are finally grouped into events and appropriately ranked. Furthermore, preliminary experiments and illustrative examples on synthetic data demonstrate the feasibility of our methods. The proposed approach enhances conventional vehicle plate analysis so that it becomes capable of discovering complex criminal behaviors and hence increasing investigation performance and decision quality. (C) 2014 Elsevier Inc. All rights reserved.