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.

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.

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.

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.>