Search results

The purpose of this paper is to present conceptual and technical knowledge about creating a large topic map by integrating the strengths of two topic maps creation tools (i.e. Ontopia and Wandora).

This study is focused on the testing of the usefulness of the two topic map creation tools. Each tool is used to create a topic map with dozens of topics in order to find out the strengths and weaknesses of each tool and the interoperability of the two tools.

When creating a large topic map, a developer may have many requirements of a desired topic maps creation tool, but may not be able to find a single tool that meets all the requirements. If multiple such tools implement the topic maps standard, there is some interoperability between the tools, and the developer may integrate these tools to meet the requirements.

Although this paper presents the strengths, weakness, and interoperability of two topic maps creation tools (i.e. Ontopia and Wandora), the findings can be applied to integrating other topic maps creation tools if they implement the topic maps standard. The technical knowledge presented in the paper can also serve as a tutorial of creating a topic map.

There is no published paper presenting the technical knowledge of how to integrate two topic map creation tools to create a large topic map.

Opinion mining (OM), also known as “sentiment classification”, which aims to discover common patterns of user opinions from their textual statements automatically or semi-automatically, is not only useful for customers, but also for manufacturers. However, because of the complexity of natural language, there are still some problems, such as domain dependence of sentiment words, extraction of implicit features and others. The purpose of this paper is to propose an OM method based on topic maps to solve these problems.

Domain-specific knowledge is key to solve problems in feature-based OM. On the one hand, topic maps, as an ontology framework, are composed of topics, associations, occurrences and scopes, and can represent a class of knowledge representation schemes. On the other hand, compared with ontology, topic maps have many advantages. Thus, it is better to integrate domain-specific knowledge into OM based on topic maps. This method can make full use of the semantic relationships among feature words and sentiment words.

In feature-level OM, most of the existing research associate product features and opinions by their explicit co-occurrence, or use syntax parsing to judge the modification relationship between opinion words and product features within a review unit. They are mostly based on the structure of language units without considering domain knowledge. Only few methods based on ontology incorporate domain knowledge into feature-based OM, but they only use the “is-a” relation between concepts. Therefore, this paper proposes feature-based OM using topic maps. The experimental results revealed that this method can improve the accuracy of the OM. The findings of this study not only advance the state of OM research but also shed light on future research directions.

To demonstrate the “feature-based OM using topic maps” applications, this work implements a prototype that helps users to find their new washing machines.

This paper presents a new method of feature-based OM using topic maps, which can integrate domain-specific knowledge into feature-based OM effectively. This method can improve the accuracy of the OM greatly. The proposed method can be applied across various application domains, such as e-commerce and e-government.

Many of today's organizations already have a strong integration of groupware systems within their IT‐infrastructure. The shared databases of these groupware systems form organizational memories, which comprise the complete knowledge of an organization collected over the time of its existence. One key problem is how to find relevant knowledge or information in continuously growing and distributed organizational memories. In many cases, the basic functionalities and mechanisms of groupware systems are not sufficient to support users in finding required knowledge or information. Topic maps provide strong paradigms and concepts for the semantic structuring of link networks and therefore, they are a considerable solution for organizing and navigating large and, continuously growing organizational memories. The K‐Discovery project suggests applying topic maps to groupware systems to address the mentioned challenges. Thus, the K‐Discovery project introduces a conceptual framework, an architecture, and an implementation approach to create knowledge structures by generating topic maps from organizational memories and offers navigation tools to exploit the created structures.

To anticipate science and technology (S&T) changes and shifts in the competitive environment for the preparation of strategic development in an organization, this paper aims to address a structured analysis method for future technology trajectories and interactions by mapping and associating the future technology themes in foresight reports with a state‐of‐the art technology classification system. The objective of this paper is to develop an integrative method for systematically clustering, analyzing and visualizing the path for technology development and transformation.

Delphi topics related to sustainable energy were collected from strategic foresight reports of Japan, South Korea and China, and used as sources for future technology themes analysis. A standard mapping taxonomy based on international patent classification system was used to map out the technology concept described in these future technology themes. Technology interactions can be identified through a causal effect analysis during the mapping, and the results among selected countries are cross‐compared and visualized in an aggregated view.

By this standard mapping taxonomy and structured analysis, future technology themes in strategic foresight reports from countries in focus are systematically mapped and integrated for viewing future technology options and interactions. Similarities and discrepancies for prospecting the future technology trajectory among these countries are also identified.

It would be a significant contribution if this structured analysis could be applied more broadly across different geographic regions or across research areas in foresight reports. This research may help to solve the practical difficulties faced during the secondary analysis of foresight studies in foresight preparatory studies by providing a consistent classification framework to make comparison and aggregation of future technology options from different countries/regions. Also, this classification framework can provide a bridge for linking with current technology performance such as patent productivity or quality and help in identifying the gaps between the probable future changes in S&T and the current capability.

The integrative method in this research provides a way to combine both the advantage of strategic technology foresight and competitive technology intelligence by utilizing the results deriving from the former as targets for analysis and the analytic practice deriving from the latter to identify the possible competitive or cooperative landscapes in the future.

Many enterprises implement various business projects on the Internet in the global knowledge economy. The task of managing distributed and heterogeneous project knowledge is very important in increasing the knowledge assets of enterprises. Accordingly, this work presents a project‐based knowledge map system to properly organize project knowledge into topic maps, from which users can obtain in‐depth concepts to facilitate further project development. A two‐phase data mining approach involving the ISO/ISEC 13250 topic maps and Extensible Markup Language (XML) is used to establish the proposed system, which can determine knowledge patterns from previous projects and transform these patterns into a navigable knowledge map. The map can help users to locate required information and also offers subject‐related information easily and rapidly over the Internet.

In academic work, it is important to identify a specific domain of research. Many researchers may look to conference issues to determine interesting or new topics. Furthermore, conference issues can help researchers identify current research trends in their field and learn about cutting-edge developments in their area of specialization. However, so much conference information is published online that it can be difficult to navigate and analyze in a meaningful or productive way. Hence, the use of knowledge management (KM) could be a way to resolve these issues. In KM, ontology is widely adopted, but most ontology construction methods do not consider social information between target users. Therefore, this study aims to propose a novel method of constructing research topic maps using an open directory project (ODP) and social information.

The approach is to incorporate conference information (i.e. title, keywords and abstract) as sources and to consider the ways in which social information automatically produces research topic maps. The methodology can be divided into four modules: data collection, element extraction, social information analysis and visualization. The data collection module collects the required conference data from the internet and performs pre-processing. Then, the element extraction module extracts topics, associations and other basic elements of topic maps while considering social information. Finally, the results will be shown in the visualization module for researchers to browse and search.

The results of this study propose three main findings. First, creating topic maps with the ODP category information can help capture a richer set of classification associations. Second, social information should be considered when constructing topic maps. This study includes the relationship among different authors and topics to support information in social networks. By considering social information, such as co-authorship/collaborator, this method helps researchers find research topics that are unfamiliar but interesting or potential cooperative opportunities in the future. Third, this study presents topic maps that show a clear and simple pathway in interested domain knowledge.

First, this study analyzes and collects conference information, including the titles, keywords and abstracts of conference papers, so the data set must include all of the abovementioned information. Second, social information only analyzes co-authorship associations (collabship associations); other social information could be extracted in the future study. Third, this study only analyzes the associations between topics. The intensity of associations is not discussed in the study.

The study will have a great impact on learned societies because it bridges the gap between theory and practice. The study is useful for researchers who want to know which conferences are related to their research. Moreover, social networks can help researchers expand and diversify their research.

The purpose of this paper is to provide an overview of the current use of topic maps in the library field, how they might be integrated into the ILS structure and some of the inherent challenges in trying to transform MARC data.

A review of available literature was conducted as well as e‐mail interviews with researchers and vendors in the field. An introduction to some of the basic concepts quickly leads into a recap of some of the possibilities that have been tried with this technology in the library field. Specific examples of the use of the XML standard XTM are given as well as some theoretical possibilities discussed. Finally some thought is given to where this technology will fit into the ILS.

The paper finds that more work needs to be done by vendors and libraries in structuring data to allow for easier transformation.

This study was a limited overview. The lack of training materials and software make topics maps have an unnecessarily high barrier to entry.

This paper points a way for further research and a need for basic tools and training geared towards the library community.

This paper attempts to address some of the potential and challenges associated with using topic maps in a library environment, especially as part of an ILS.

Our study aims to focus on the application of knowledge mapping to provide pedagogically-structured learners' competences.

We conducted an experiment examined the associations between the pedagogical quality of students’ pedagogically-informed knowledge (PIK) maps, class assignment scores and perceptions of PIK mapping’s uses.

The results showed that higher assignment scores were significantly predicted by higher quality PIK maps, ratings for PIK mapping were significantly higher than other mappings, and the learners’ experience of PIK mapping led to a significant change of attitude towards mapping as a learning activity and to a positive opinion of the value of PIK mapping in particular. Interestingly, there was no significant relation between learners’ opinion ratings of the uses of PIK mapping in learning and their assignment scores.

Questions remain on the generalizability of the findings, and on the features of a PIK map which are particularly useful to a learner. This study investigated the value of PIK mapping in the context of a practical class on the building of simple DIY (do-it-yourself) holographic projectors; it may be thought that the applied nature of the topic was more suited to the PIK mapping of learner competences and intended learning outcomes than a more theoretic classroom topic on holography. A future study is planned to address this issue.

The purpose of this article is to investigate the use of word co-occurrence analysis method in mapping of the scientific fields with emphasis on the field of Informetrics.

This is an applied study using scientometrics, co-word analysis and network analysis and its steps are summarised as follows: collecting the data related to the Informetrics field indexed in Web of Science (WOS) database, refining and standardising the keywords of the extracted articles from WOS and preparing a selected list of these keywords, drawing the word co-occurrence map in the Informetrics field and analysing of results.

Based on the resulted maps the concepts such as information science, library, bibliometric analysis, innovation and text mining are the most widely used topics in the field of Informetrics. The co-word occurrence maps drawn at different periods show the changes and stabilities in the concepts related to the field of Informetrics. A number of topics such as “bibliometric analysis” are present in all years, whereas others such as “innovation” have disappeared. New topics emerge as a recombination of existing topics and in interaction with new (technological) developments.

The results of these analytical studies can be used as a guide for determining research priorities in the scientific fields, and also for planning and management in academic institutions.

This paper aims to establish a two‐part research agenda for marketing in supply chain management (SCM) through the application of an interdisciplinary model, using marketing, operations, logistics/purchasing, and information technology as the nodes for a model.

After generating a list of the highly ranked and relevant journals in each of the four disciplines, an exhaustive search was conducted of the literature published from January 1999 through December 2002, using the keywords supply chain and supply chain management. The keywords were searched for in any field (i.e. title or abstract). The authors also conducted a Delphi study of experts to identify relevant journals in each field. The resulting articles were sorted by topic and mapped to one of the other remaining three functional disciplines. This yielded six intersections between functions, three of which are examined in this manuscript as dyads with marketing. Thus, it was possible to identify current overlap in topics researched and potential areas of overlap, representing opportunities for collaboration between the disciplines.

For simplicity and focus, this paper presents only marketing SCM research. The mapping process yielded: topics that are being researched from the marketing perspective but not in the IT, logistics, or operations perspectives; topics that are being researched from the IT, logistics, or operations perspectives but not from the marketing perspective; and similar (or identical) topics that are being researched from both the marketing and the IT perspective, the marketing and logistics perspective, and the marketing and operations perspective. Based on these mappings, an interdisciplinary research agenda for marketing SCM researchers was derived.

Using an automated extraction of articles from published databases by using keywords may present inconsistencies. The authors have attempted to minimize the inconsistencies by documenting the process and cross‐validating the work in each function with at least two of the research team independently extracting, categorizing, and mapping the articles. Another limitation that arose was in terms of language. Since the research team consisted of researchers from different functional areas, it had to address semantics issues as the study was conducted. The authors also limited the initial endeavor to mapping only as a dyad and only using dichotomous variables. Future work on this model may include an ordinal ranking system or multi‐function mapping.

This work presents a useful model for determining an interdisciplinary research agenda in marketing. Since business and supply chain integration are increasingly important, concepts in business, academic research should take an interdisciplinary approach, providing the prospects for richer and more applicable results. Interdisciplinary research can also help to combat the silos that people tend to work in, creating new knowledge.

This paper provides the example of a model for determining an interdisciplinary research agenda. Supply chain management has been co‐opted by almost every business discipline. There is much to be learned by working together to bring new ideas and knowledge to bear on the issues related to managing the supply chain.