An organization performing environmental scanning generally monitors or tracks various events concerning its external environment. One of the major resources for environmental scanning is online news documents, which are readily accessible on news websites or infomediaries. However, the proliferation of the World Wide Web, which increases information sources and improves information circulation, has vastly expanded the amount of information to be scanned. Thus, it is essential to develop an effective event episode discovery mechanism to organize news documents pertaining to an event of interest. In this study, we propose two new metrics, Term Frequency × Inverse Document FrequencyTempo (TF×IDFTempo) and TF×Enhanced‐IDFTempo, and develop a temporal‐based event episode discovery (TEED) technique that uses the proposed metrics for feature selection and document representation. Using a traditional TF×IDF‐based hierarchical agglomerative clustering technique as a performance benchmark, our empirical evaluation reveals that the proposed TEED technique outperforms its benchmark, as measured by cluster recall and cluster precision. In addition, the use of TF×Enhanced‐IDFTempo significantly improves the effectiveness of event episode discovery when compared with the use of TF×IDFTempo.

Abstract Influxes of new documents over time necessitate reorganization of document categories that a user has created previously. As documents are available in increasing quantities and accelerating frequencies, the manual approach to reorganizing document categories becomes prohibitively tedious and ineffective, thus making a system‐oriented approach appealing. Previous research (Larsen & Aone, 1999 ; Pantel & Lin, 2002 ) largely has followed the category‐discovery approach, which groups documents by using a document‐clustering technique to partition a document corpus. This approach does not consider existing categories a user created previously, which in effect reflect his or her document‐grouping preference. A handful of studies (Wei, Hu, & Dong, 2002 ; Wei, Hu, & Lee, 2009 ) have taken a category‐evolution approach to develop lexicon‐based techniques for preserving user preference in document‐category reorganizations, but have serious limitations. Responding to the significance of document‐category reorganizations and addressing the fundamental problems of salient, lexicon‐based techniques, we develop an ontology‐based category evolution (ONCE), a technique that first enriches a concept hierarchy by incorporating important concept descriptors (jointly referred to as an ontology) and then employs the resulting enriched ontology to support category evolutions at a concept level rather than analyzing and comparing feature vectors at the lexicon level. We empirically evaluate our proposed technique and compare it with two benchmark techniques: CE2 (a lexicon‐based category‐evolution technique) and hierarchical agglomerative clustering (HAC; a conventional hierarchical document‐clustering technique). Overall, our results show that the ONCE technique is more effective than are CE2 and HAC, across all the scenarios studied. Furthermore, the completeness of a concept hierarchy has important impacts on the performance of the proposed technique. Our results have some important implications for further research.

Abstract The category‐tree document‐classification structure is widely used by enterprises and information providers to organize, archive, and access documents for effective knowledge management. However, category trees from various sources use different hierarchical structures, which usually make mappings between categories in different category trees difficult. In this work, we propose a category‐tree integration technique. We develop a method to learn the relationships between any two categories and develop operations such as mapping, splitting, and insertion for this integration. According to the parent‐child relationship of the integrating categories, the developed decision rules use integration operations to integrate categories from the source category tree with those from the master category tree. A unified category tree can accumulate knowledge from multiple resources without forfeiting the knowledge in individual category trees. Experiments have been conducted to measure the performance of the integration operations and the accuracy of the integrated category trees. The proposed category‐tree integration technique achieves greater than 80% integration accuracy, and the insert operation is the most frequently utilized, followed by map and split. The insert operation achieves 77% of F1 while the map and split operations achieves 86% and 29% of F1, respectively.

Effective, personalized recommendations are central to cross-selling, a common business strategy that suggests additional items (products or services) to customers for their consideration. Content-based recommendation and collaborative filtering represent two salient approaches for automated recommendations. The content-based approach uses essential features (attributes) of items to make recommendations, without making reference to the preferences of other customers. Although content-based recommendation techniques have been shown effective in various scenarios, their utilities and value depend on the availability of a large number of training examples. In this study, we propose a collaborative content-based (COCO) recommendation technique that uses a collaboration-based expansion approach to address the small-size training set problem, a common challenge faced the content-based recommendation approach. We empirically examine the effectiveness of the proposed technique for book recommendations and include a pure content-based technique as a performance benchmark. According to our evaluation results, the proposed COCO technique substantially outperforms the benchmark technique.

When performing environmental scanning, organizations typically deal with a numerous of events and topics about their core business, relevant technique standards, competitors, and market, where each event or topic to monitor or track generally is associated with many news documents. To reduce information overload and information fatigues when monitoring or tracking such events, it is essential to develop an effective event episode discovery mechanism for organizing all news documents pertaining to an event of interest. In this study, we propose a new metric, referred to as TFxIDFTempo and develop a temporal-based event episode discovery technique that uses the proposed TFxIDFTempo metric as its feature selection method and document representation scheme. Using the traditional TFxIDF-based HAC technique as performance benchmarks, our empirical evaluation results suggest that the proposed temporal-based event episode discovery technique outperforms its benchmark in cluster recall and cluster precision.

Web 2.0 technologies have facilitated the interaction between web users. The business-to-consumer electronic commerce is no longer restricted between consumers and online retail stores. It has been extended to opinions sharing between consumers and consumers. Before making purchasing decision, consumers like to see other consumer opinions in order to identify the best consumer product that fits their preferences. In the recent years, many research works have focused on sentiment classification and analysis of online consumer reviews. Most of them rely on natural language processing techniques to parse and analyze the sentences in online consumer reviews. However, the writing of consumer reviews contributed by web users is usually less formal than those appear in news or journal articles. Many sentences in consumer reviews may contain grammatical errors and unknown terms that do not exist in any dictionaries. As a result, the natural language processing rules are not applicable in many consumer review text and the performance is relatively poor. In this work, we propose to utilize machine learning techniques to classify the consumer product features and produce a summary of consumer reviews for products such as digital cameras. We have conducted an experiment to compare the performance of class association rules and naïve Bayesian classifier for sentiment analysis. The results show that over 70% of macro- and micro- F measures are achieved. It is substantially higher than those achieved by natural language processing approaches.

Due to the popularity of online retail stores, consumers are not only shopping and comparing consumer products on the Web but also providing their consumer reviews on the Internet platform. The Web has become the largest repository of consumer reviews. Consumer reviews are beneficial to consumers, merchants, and manufacturers. Consumers may read the comments of other consumers and decide whether the product is good in the specific product features that they are interested in. For merchants or product manufacturers, consumer reviews help them understand general responses of customers on their products for product or marketing campaign improvement. In addition, consumer reviews can enable merchants better understand specific preferences of individual customers and facilitates effective marketing decisions. However, the large volume of consumer reviews makes it impossible for any individual consumer, merchant, or manufacturer to extract important knowledge efficiently. In this study, we concentrate on opinion sentence identification of focused sentiment analysis and propose a collaborative-filtering-based opinion sentence identification (CF-OSI) technique. The proposed CF-OSI technique considers opinion sentence identification as the sentence retrieval problem. In addition, a collaborative-filtering-based query expansion approach is incorporated into the CF-OSI technique to address possible effectiveness degradation caused by short user queries (i.e., limited number of query terms in query queries). Experiments have been conducted to empirically evaluate the effectiveness of our proposed technique. Our evaluation results show that the performance of our proposed CF-OSI technique is promising.

Given the advance of Internet technologies, we can now easily extract hundreds or thousands of news stories of any ongoing incidents from newswires such as CNN.com, but the volume of information is too large for us to capture the blueprint. Information retrieval techniques such as topic detection and tracking are able to organize news stories as events, in a flat hierarchical structure, within a topic. However, they are incapable of presenting the complex evolution relationships between the events. We are interested to learn not only what the major events are but also how they develop within the topic. It is beneficial to identify the seminal events, the intermediary and ending events, and the evolution of these events. In this paper, we propose to utilize the event timestamp, event content similarity, temporal proximity, and document distributional proximity to model the event evolution relationships between events in an incident. An event evolution graph is constructed to present the underlying structure of events for efficient browsing and extracting of information. Case study and experiments are presented to illustrate and show the performance of our proposed technique. It is found that our proposed technique outperforms the baseline technique and other comparable techniques in previous work.

E-commerce applications generate and consume a tremendous amount of online information, which is typically available as textual documents. Conceivably, organizations and individuals generally use category sets or hierarchies to organize, archive, and access their documents. Meanwhile, organizations and individuals constantly acquire relevant documents from various Internet sources, each of which may organize its documents in a category set or hierarchy different from that used by the acquiring organization or individual. Consequently, the integration of source documents organized in a category hierarchy into an existing category hierarchy deployed by the acquiring organization or individual becomes an important issue in the e-commerce era. Existing category-integration techniques are mainly designed to integrate document catalogs, each of which is organized nonhierarchically (i.e., in a flat set). In this paper, we propose a clustering-based category-hierarchy integration (CHI) technique, which is an extension of the clustering-based category-integration (CCI) technique. Our empirical evaluation results show that the proposed CHI technique appears to improve the effectiveness of category-hierarchy integration compared with that attained by nonhierarchical category-integration techniques, particularly in homogeneous and comparable scenarios.

Drug-related problems, particularly those that result from sub- or overtherapeutic doses of high-alert medications, have become a growing concern in clinical medicine. In this paper, we use a model-tree-based regression technique (namely, M5) and support vector machine (SVM) for regression to develop learning-based systems for predicting the adequacy of a vancomycin regimen. We empirically evaluate each system's accuracy in predicting patients' peak and trough concentrations in different clinical scenarios characterized by renal functions and regimen types. Our data consist of 1099 clinical cases that were collected from a major tertiary medical center in southern Taiwan. We also examine the use of Bagging for enhancing the prediction power of the respective systems and include in our evaluation a salient one-compartment model for performance benchmark purposes. Overall, our evaluation results suggest that both M5 and SVM are significantly more accurate than the benchmark one-compartment model in predicting patients' peak and trough concentrations across all investigated clinical scenarios. M5 appears to benefit considerably from Bagging, which has a positive but seemingly smaller effect on SVM. Taken together, our findings indicate supervised learning techniques that are capable of effectively supporting clinicians' use of vancomycin or similar high-alert drugs in their patient care and management.

Recent advances in information and networking technologies have contributed significantly to global connectivity and greatly facilitated and fostered information creation, distribution, and access. The resultant ever-increasing volume of online textual documents creates an urgent need for new text mining techniques that can intelligently and automatically extract implicit and potentially useful knowledge from these documents for decision support. This research focuses on identifying and discovering event episodes together with their temporal relationships that occur frequently (referred to as evolution patterns (EPs) in this paper) in sequences of documents. The discovery of such EPs can be applied in domains such as knowledge management and used to facilitate existing document management and retrieval techniques [e.g., event tracking (ET)]. Specifically, we propose and design an EP discovery technique for mining EPs from sequences of documents. We experimentally evaluate our proposed EP technique in the context of facilitating ET. Measured by miss and false alarm rates, the EP-supported ET (EPET) technique exhibits better tracking effectiveness than a traditional ET technique. The encouraging performance of the EPET technique demonstrates the potential usefulness of EPs in supporting ET and suggests that the proposed EP technique could effectively discover event episodes and EPs in sequences of documents

Data mining refers to the process of automatic extracting previously unknown, valid, and actionable patterns or knowledge from large databases for crucial decision support. Among different data mining technique, classification analysis is widely adopted for healthcare applications for supporting medical diagnostic decisions, improving quality of patient care, etc. If a training dataset contains irrelevant features (i.e., attributes), classification analysis may produce less accurate and less understandable results. Two commonly employed feature selection approaches include use of automatic feature selection mechanisms (i.e., data-driven) or expert judgment (i.e., knowledgedriven). Due to differences in their underlying processes, the two prevailing feature selection approaches may have their unique biases that possibly lead to dissimilar classification effectiveness. In this study, we empirically evaluate the classification effectiveness resulted from the two feature selection approaches on a risk prediction of cardiovascular disease dataset. Our evaluation results suggest that the feature subsets selected domain experts improve the sensitivity of a classifier, while the feature subsets selected by an automatic feature selection mechanism improve the predictive power of a classifier on the majority class (i.e., the specificity in this study).

Information extraction is important for crime analysis. Due to the popularity of the Web, information related to crime and terrorism is available in multiple languages. As a result, cross-lingual semantic interoperability is essential when we extract information across multiple languages. In our previous work, we have developed several techniques to generate an automatic cross-lingual thesaurus to support cross-lingual information retrieval based on a parallel corpus collected from the Web. The techniques include Hopfield network and associate constraint network with backmarking. Although these techniques obtain satisfactory performance, they have weaknesses in efficiency, consistency, precision or recall. In this work, we develop a new searching technique, namely forward evaluation, on the basis of our previously developed associate constraint network model. We have conducted an experiment and show that the proposed forward evaluation technique outperforms both Hopfield network and associate constraint network with backmarking in terms of precision and recall. In addition, its efficiency is better than Hopfield network but is not as good as associate constraint network with backmarking.

Among various knowledge management initiatives, the creation of knowledge repositories has emerged as a prevalent approach in current knowledge management practices. In order to transfer tacit knowledge from individuals to a knowledge repository, organizations usually deploy community-based discussion forums. Typically, discussions among participants are organized into reply-replied structures, and reply semantic relationships among these discussion documents exist either explicitly or implicitly. Such relationships, once discovered or annotated in the knowledge repository, can facilitate subsequent knowledge navigation by providing a novel and more semantic mechanism and can support other organizational knowledge management activities (e.g., construction of expert networks). In this study, we propose a preliminary taxonomy of reply semantic relationships for discussion documents organized in reply-replied structures and develop a Semantic Enrichment between Knowledge-sharing documents (SEEK) technique that automatically annotates semantic relationships between reply pairs of documents. Specifically, we propose and evaluate six different feature models that combine keyword features, part-of-speech statistic features, and/or text statistic features.

The expanded role of radiology in clinical medicine and its emerging digital practice have made patient-image management a growing concern for health-care organizations. A fundamental aspect of patient-image management is to provide a radiologist with convenient access to prior images relevant to his or her reading of a recently taken radiological examination. For confirmation or evaluation purposes, radiologists often reference relevant prior images of the same patient when interpreting the images of a current examination. To alleviate the time and physical requirements on radiologists, many health-care organizations have taken a prefetching strategy for meeting their patient-image reference needs. Radiologists' patient-image reference knowledge understandably may exhibit subtle individual variations and dynamically evolves over time, thus making the artificial intelligence-based inductive learning approach appealing. Central to patient-image prefetching is a knowledge base of which knowledge elements need continual update and individual customization. In this study, we extended a decision rule induction technique (i.e., CN2 algorithm) to address the challenging characteristics of the targeted learning. We experimentally evaluated the extended algorithm using the learning performances achieved by backpropagation neural network as benchmarks. Overall, our evaluation results suggest that the extended algorithm exhibited satisfactory learning effectiveness and, at the same time, showed desirable noise tolerance, immunity to missing data, and robustness in relation to limited training data.

Management of textual documents obtained from various online sources represents a challenge in emerging e-commerce environments, where individuals and organisations have to perform continual surveillance of important events or trends pertinent to multiple topic areas of interest. Observations of textual document management by individuals and organisations have suggested the popularity of using categories to organise, archive and access documents. The sheer volume and availability of documents obtained from the internet make manual document-category management prohibitively tedious, if practicable or effective at all. An automated approach underpinned by appropriate artificial intelligence techniques has potential for solving this problem. In this vein, a critical challenge is the preservation of the user's perspective on semantic coherence in different documents and thus supports his or her preferred practice for document groupings. Motivated by the significance of, and the need for automated document-category management, the current research proposed and experimentally examined an evolution-based approach for supporting user-centric document-category management in e-commerce environments. Specifically, we designed and implemented the Category Evolution (CE) technique, capable of supporting personalised document-category management by taking into account categories previously established by the user. Our evaluation results suggest that CE exhibited satisfactory effectiveness and reasonable robustness in different scenarios and achieved a performance level better than that recorded by the benchmark technique using complete category discovery.

When performing primary reading on a newly taken radiological examination, a radiologist often needs to reference relevant prior images of the same patient for confirmation or comparison purposes. Support of such image references is of clinical importance and may have significant effects on radiologists' examination reading efficiency, service quality, and work satisfaction. To effectively support such image reference needs, we proposed and developed a knowledge-based patient image pre-fetching system, addressing several challenging requirements of the application that include representation and learning of image reference heuristics and management of data-intensive knowledge inferencing. Moreover, the system demands an extensible and maintainable architecture design capable of effectively adapting to a dynamic environment characterized by heterogeneous and autonomous data source systems. In this paper, we developed a synthesized object-oriented entity- relationship model, a conceptual model appropriate for representing radiologists' prior image reference heuristics that are heuristic oriented and data intensive. We detailed the system architecture and design of the knowledge-based patient image pre-fetching system. Our architecture design is based on a client-mediator-server framework, capable of coping with a dynamic environment characterized by distributed, heterogeneous, and highly autonomous data source systems. To adapt to changes in radiologists' patient prior image reference heuristics, ID3-based multidecision-tree induction and CN2-based multidecision induction learning techniques were developed and evaluated. Experimentally, we examined effects of the pre-fetching system we created on radiologists' examination readings. Preliminary results show that the knowledge-based patient image pre-fetching system more accurately supports radiologists' patient prior image reference needs than the current practice adopted at the study site and that radiologists may become more efficient, consultatively effective, and better satisfied when supported by the pre-fetching system than when relying on the study site's pre-fetching practice.

The objective is to develop a structured object-oriented modeling and design methodology for coupled knowledge-base/database (KB/DB) systems by exploring the useful principles and features of object-oriented modeling and software development techniques. The methodology uses a synthesize object-oriented entity-relationship model for representing the knowledge and the embedded data semantics involved in coupled KB/DB systems. An associated design procedure is presented. This methodology improves on existing coupled KB/DB design methods because of its well-defined constructs that deal with various forms of knowledge involved in data processing, knowledge-based problem solving and object-oriented reasoning.>