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Recently research on text mining has attracted lots of attention from both industrial and academic fields. Text mining concerns of discovering unknown patterns or knowledge from a large text repository. The problem is not easy to tackle due to the semi-structured or even unstructured nature of those texts under consideration. Many approaches have been devised for mining various kinds of knowledge from texts. One important aspect of text mining is on automatic text categorization, which assigns a text document to some predefined category if the document falls into the theme of the category. Traditionally the categories are arranged in hierarchical manner to achieve effective searching and indexing as well as easy comprehension for human beings. The determination of category themes and their hierarchical structures were most done by human experts. In this work, we developed an approach to automatically generate category themes and reveal the hierarchical structure among them. We also used the generated structure to categorize text documents. The document collection was trained by a self-organizing map to form two feature maps. These maps were then analyzed to obtain the category themes and their structure. Although the test corpus contains documents written in Chinese, the proposed approach can be applied to documents written in any language and such documents can be transformed into a list of separated terms.

This paper describes our work on developing a language-independent technique for discovery of implicit knowledge from multilingual information sources. Text mining has been gaining popularity in the knowledge discovery field, particularity with the increasing availability of digital documents in various languages from all around the world. However, currently most text mining tools mainly focus only on processing monolingual documents (particularly English documents): little attention has been paid to apply the techniques to handle the documents in Asian languages, and further extend the mining algorithms to support the aspects of multilingual information sources. In this work, we attempt to develop a language-neutral method to tackle the linguistics difficulties in the text mining process. Using a variation of automatic clustering techniques, which apply a neural net approach, namely the Self-Organizing Maps (SOM), we have conducted several experiments to uncover associated documents based on a Chinese corpus, Chinese-English bilingual parallel corpora, and a hybrid Chinese-English corpus. The experiments show some interesting results and a couple of potential paths for future work in the field of multilingual information discovery. Besides, this work is expected to act as a starting point for exploring the impacts on linguistics issues with the machine-learning approach to mining sensible linguistics elements from multilingual text collections.

Open data has emerged to be a hot topic recently and attracted lots of attention from both researchers and practitioners. Many governments around the world are publishing government open data for public well-being, information disclosure, service innovation, and so on. Enormous amount of data and applications have been released and built in recent years. In accordance to such trend, authorities of Taiwan government are also striving to establish platforms for users to access and employ open data. However, the status and usage of these platforms are still unclear. This study conducts an exploratory investigation on the open data platforms in Taiwan to reveal their feasibility. Besides, we also try to assess the data quality of these platforms by examining medical facilities datasets published in these platforms. We hope that this research can provide a step stone for building better open datasets as well as platforms in Taiwan.

Proceedings from the 2013 LTEC conference in Kaohsiung, Taiwan. The papers examine diverse aspects of Learning Technology for Education in Cloud environments, including social, technical and infrastructure implications. Also addressed is the question of how cloud computing can be used to design applications to support real time on demand learning using technologies. The workshop proceedings provide opportunities for delegates to discuss the latest research in TEL (Technology Enhanced Learning) and its impacts for learners and institutions, using cloud technologies.

A structural-feature-to-structural-feature configuration is naturally constructed using a set of sampled features from a cursive pattern. These features are sampled by maximally fitting bent ellipses in local strokes. This configuration is transformed into an undirected graph to resolve the asymmetric difficulty. The compatibility associated with the graph is further formulated into a devised Hopfield network, where both interfeature and interlink similarities are incorporated into the compatibility. We operate this network to recognize a radical as a whole in a handprinted pattern to accomplish the selective attention task.

In this paper, we describe our work on extracting entities from the online social messages regarding emergent events for ontology learning, which can contribute to a solution for quick response of emerging disastrous events. Our work started with the development of a real-time event detection system using a data-cluster slicing approach which combines social data analysis and early warning algorithms, allowing for quickly detecting emerging large-scale events from collected tweets. Subsequently, our system computes the energy of each collected event dataset, and then encapsulates ranked temporal, spatial and topical keywords into a structured node for event-entity extraction, in order to learn and update event ontologies for fast response of emergent events. The preliminary experimental results demonstrate that our developed system is workable, allowing for prediction of possible evolution for early warning of critical incidents with a dynamic ontology engineering.

Nowadays, crowdsourcing has emerged as a popular and important problem-solving approach. The major difference between crowdsourcing and traditional outsourcing lies on the people which tasks were outsourced. Those people involved in crowdsourcing are generally varied in knowledge, demographic properties, and number. Many applications and services have been developed to solve various types of tasks. However, these applications and services focus on providing platforms for outsourcing to the crowd. Little has been addressed so far on the management and usage of those information produced during the crowdsourcing process. Actually, as an emerging social network application and service, the data and social interactions created during crowdsourcing should carry important and valuable knowledge. This knowledge will develop various techniques for mining messages and information of crowdsourcing process. In this work, we address several approaches to discover useful knowledge from data created for and in crowdsourcing process. We hope the outcome of this research could help discovering usable knowledge from such emerging social network services and bring benefit in constructing crowdsourcing services.

These papers on Intelligent Data Analysis and Management (IDAM) examine issues related to the research and applications of Artificial Intelligence techniques in data analysis and management across a variety of disciplines. The papers derive from the 2013 IDAM conference in Kaohsiung ,Taiwan. It is an interdisciplinary research field involving academic researchers in information technologies, computer science, public policy, bioinformatics, medical informatics, and social and behavior studies, etc. The techniques studied include (but are not limited to): data visualization, data pre-processing, data engineering, database mining techniques, tools and applications, evolutionary algorithms, machine learning, neural nets, fuzzy logic, statistical pattern recognition, knowledge filtering, and post-processing, etc.

Ordinary text documents such as text books, medical records, reports, research articles, Web pages, and mails dominate the information storage and dissemination in our daily life. However, it is difficult to process and extract useful information from them due to their unstructured nature. Different methodologies have been devised to discover various types of knowledge underlying massive amount of text documents. In this talk, I will first address the general problem of text mining which aims to discover interesting knowledge from unstructured text documents. The application of the self-organizing map (SOM) model, which is well reputed as a good tool for data clustering, in resolving this issue is then discussed. I will cover the basic schemes of SOM as well as some others with upgraded features. Different aspects of text mining using these schemes will also be shown with experimental results.

Intelligence collection and analysis always play a major role in a company's growth. Traditional intelligence management process was most concealed and required massive human effort. It also has the disadvantages of rarity and danger. Therefore open source intelligence (OSINT) emerged as a major intelligence collection and analysis approach. Differing from traditional approach, the sources of OSINT are publicly accessible and have the properties of openness and massiveness which may result in disadvantages of inconsistency and lack of validation. For now, most of the OSINT processing is conducted manually which requires massive human effort and time cost. Automatic processing of OSINT is then unavoidable for modern applications. Although there exists software services to aid such automatic processing, the functionality and degree of automation are still immature and limited. In this work we developed an automatic processing approach for OSINT based on proposed text mining techniques. This approach may automatically identify interesting events from various aspects from which business could benefit. The major contribution of this work is that we have developed high-order mining techniques for OSINT, which will benefit domains like national security, personal knowledge management, business intelligence, e-learning, etc.

The “microblogs” archives keep a valuable collection of records for past disastrous events and related stories. To learn the experiences from past microblogging messages for coping with emerging real-world events, in this work we utilize the social-media messages to characterize events for relatedness analysis. First, we established an online clustering algorithm to detect burstiness on Twitter microblogs for monitoring large-scale events. Furthermore, we compared the features extracted from the on-going event with the ones of other past detected events. Through the developed algorithms for computing event relatedness the ranking of related events can be sorted, allowing for predicting possible evolution and impacts of the event. For performing event clustering and relatedness computation, in this work we have developed an online system platform that can immediately evaluate the significantly emergent events to achieve the goals of real-time discovery of disastrous information and early warning for crisis management.

Nowadays lots of information are stored as Web pages for ease of sharing and searching. It is rather impractical for users to browse such gigantic amount of Web pages to obtain target information. Therefore, users often count on search engines to retrieve information they needed. Most of the search engines rely on the contents of Web pages, which are often represented by sets of keywords, to decide their relatedness to a user's query. However, such scheme is often imprecise since there is always a semantic gap between the actual semantics of a Web page and its representation. Manual annotations by human beings will certainly help since such annotations should always reflect the true meaning of the Web pages. However, ordinary annotators may not annotate a Web page with appropriate tags and thus deteriorate the quality of tags as well as the retrieval result. In this work, we propose a novel scheme for recommending tags while a user intends to annotate a Web page. Our scheme first discover the relationships across Web pages and tags based their clustering result. We then recommend tags to users according to such relationships. We tested our work on RSDC 2008 dataset and obtained promising result.

Social networks have been regarded as a timely and cost-effective source of spatio-temporal information for many fields of application. However, while some research groups have successfully developed topic detection methods from the text streams for a while, and even some popular microblogging services such as Twitter did provide information of top trending topics for selection, it is still unable to fully support users pickup all of the real-time event topics with a comprehensive spatio-temporal viewpoint to satisfy their information needs. This paper aims to enhance the understanding on how social networks can be used as a reliable source of spatio-temporal information, by analyzing the temporal and spatial dynamics of Twitter activity. In this work, we developed several algorithms for mining microblogging text stream to obtain real-time and geospatial event information. The goal of our approach is to effectively detecting and grouping emerging topics by making use of real-time messages and geolocation data provided by social network services.

Social tags are annotations for Web pages collaboratively added by users. It will be much easier to understand the meaning of Web pages and classify them according to their tags. The precision in retrieving Web pages may also increase using such tags. Nowadays social tags are mostly annotated manually by users via social bookmarking Web sites. Such manual annotation process may produce diverse, redundant, and inconsistent tags. Besides, many tags which are inconsistent with their annotated Web pages exist and deteriorate the feasibility of social tags. In this work we will develop an automatic scheme to discover the associations between Web pages and social tags and apply such associations on applications of social tag spam detection. We applied a text mining approach based on self-organizing maps to find the relationships between Web pages and social tags. The disadvantages of manual annotation will be remedied through such relationships. The discovered associations were then used to identify social tag spams. Preliminary experiments show that the quality and usability of social tags were improved through this method.

Self-organizing map (SOM) learning algorithm has been widely applied in solving various tasks in pattern recognition, machine learning, and data mining, etc. Recently, it has been used to cluster documents and produced reasonable results. Traditional SOM algorithm learns from data using a fixed map. Approaches have been proposed to allow adaptable map structure. In this work, we propose a novel SOM learning algorithm that can expand the map laterally and hierarchically. The adaption of the map structure is based on topics identified from the underlying document clusters. This approach is different from traditional approaches which are typically driven by the data density of clusters. Preliminary experiment result suggested that the proposed algorithm outperforms other similar approaches.

The self-organizing map (SOM) model is a well-known neural network model with wide spread of applications. The main characteristics of SOM are two-fold, namely dimension reduction and topology preservation. Using SOM, a high-dimensional data space will be mapped to some low-dimensional space. Meanwhile, the topological relations among data will be preserved. With such characteristics, the SOM was usually applied on data clustering and visualization tasks. However, the SOM has main disadvantage of the need to know the number and structure of neurons prior to training, which are difficult to be determined. Several schemes have been proposed to tackle such deficiency. Examples are growing/expandable SOM, hierarchical SOM, and growing hierarchical SOM. These schemes could dynamically expand the map, even generate hierarchical maps, during training. Encouraging results were reported. Basically, these schemes adapt the size and structure of the map according to the distribution of training data. That is, they are data-driven or dataoriented SOM schemes. In this work, a topic-oriented SOM scheme which is suitable for document clustering and organization will be developed. The proposed SOM will automatically adapt the number as well as the structure of the map according to identified topics. Unlike other data-oriented SOMs, our approach expands the map and generates the hierarchies both according to the topics and their characteristics of the neurons. The preliminary experiments give promising result and demonstrate the plausibility of the method.

With the increasing needs of location information, applications of geospatial information have gained a lot of attention in both research and commercial organizations Extraction of geospatial knowledge from the information content has been thus becoming a important process Among theses applications, a typical example is to discover relationships between various geospatial texts/data and specific locations In this paper, we describe a location based text mining approach using Artificial Neural Networks (ANN) to classify texts into various categories based on their geospatial features, with the aims to discovering relationships between documents and zones First, the collected documents were mapped to corresponding zones by the Adaptive Affinity Propagation (Adaptive AP) clustering techniques, then we performed framed maximize zones by means of Fuzzy ARTMAP (FAM) and Support Vector Machines (SVM) methods, allowing the results of relationships between documents and zones to be well presented Eventually, we compared our experimental results with that of baseline models using Self-organizing maps (SOM) and Learning Vector Quantization (LVQ) methods The preliminary results show that our platform framework has potential for geospatial knowledge discovery.

In this paper, we describe a location based text mining approach to classify texts into various categories based on their geospatial features, with the aims to discovering relationships between documents and zones. We first mapped documents into corresponding zones by Adaptive Affinity Propagation (Adaptive AP) clustering technique, and then framed maximize zones by means of Simplified Fuzzy ARTMAP (SFAM) and Support Vector Machines (SVM) methods. Also, we compared our experimental results with the baseline approaches of Self-organizing maps (SOM) and Learning Vector Quantization (LVQ) methods. The preliminary results show that our platform framework has the potential for geospatial data mining.

Due to the improvement of digital image technology and increasing amount of digital image data, the issue of automatic image annotation technology and applications becomes more and more important. In order to retrieval image efficiently, it is important to extract and represent the semantics of images. Traditional image semantics extraction and representation schemes were commonly divided into two categories, namely visual features and text annotations. However, visual feature scheme are difficult to extract and are often semantically inconsistent. On the other hand, the image semantics can be well represented by text annotations. It is also easier to retrieve images according to their annotations. The problem is annotating images are always time-consuming and requiring lots of human effort. In this thesis, we try to develop an automatic image annotation method. We adopted the Growing Hierarchical Self-Organizing Map (GHSOM) to help us discover the concealed relations between image data and annotation data, and annotate image according to such relations. We first applied GHSOM to cluster and generate hierarchies for images and annotations individually. We then proposed a hierarchical mapping method to discover the relations between image clusters and annotation clusters. New images can be annotated according to such relations. We conducted experiments using the proposed method on an image dataset and obtained promising result, which showed that our method could be plausible.

This paper describes our work on developing a language-independent technique for discovery of implicit knowledge about patents from multilingual patent information ources. Traditional techniques of multi- and cross-language patent retrieval are mostly based on the process of translation. One major problem of those is that it is difficult to find related patents produced from other countries in a stand-alone patent information system. In this paper, we present a novel system platform to support locating similar and relevant multilingual patent documents. The platform is developed using a multilingual vector space based on the latent semantic indexing (LSI) model, and utilizing collected professional Chinese-English parallel corpora for training the system model. These multilingual patent documents can then be mapped into the semantic vector space for evaluating their similarity by means of text clustering techniques. The preliminary results show that our platform framework has potential for retrieval and relatedness evaluation of multilingual patent documents.

Web directories cluster Web pages into categories and usually organize them into hierarchies. Many users used them to browse for interesting Web pages in a coarse-to-fine manner. Nowadays most of the Web directories access monolingual Web pages and provide only monolingual interface which may limit the coverage and accessibility of Web pages for users familiar only with their native languages. Bilingual or multilingual Web directories thus may relieve such limitations. In this work, we develop an automated process to create multilingual (or bilingual, specifically) Web directories from a set of parallel corpora. We adopted the self-organizing map model to cluster the Web pages and construct Web directories for each language. A hierarchy alignment process was then applied on these monolingual hierarchies to obtain the relationships between different languages. A multilingual Web directory was then created using such relationships. We conducted experiments on a set of parallel corpora and the result demonstrated that our method could be feasible.

Multilingual information retrieval has attracted lots of attention in recent years due to the explosive increase of multilingual Web pages. It will not be easy to retrieve documents written in languages other than the query if the relationships among entities of different languages were not found. In this work, we will develop a method based on self-organizing maps to organize documents into hierarchy. When hierarchies of individual languages are developed, an alignment process is then applied on these hierarchies to discover the associations between entities of different languages. The method had been performed on a set of parallel corpora containing Chinese and English documents and obtained promising result.

The Web pages nowadays were written in various languages including English, Chinese, Spanish, etc. There are increasing needs in searching Web pages of different languages using single query. This task is called multilingual information retrieval (MLIR). However, MLIR is difficult to achieve since we need some kind of method to find the associations between linguistic elements of different languages. In this work, we provide a method based on GHSOM to discover the associations between different languages and apply this method on MLIR task. The experiments show that our method provide a promising approach to tackle MLIR task.

With the increasing amount of multilingual texts in the Internet, multilingual text retrieval techniques have become an important research issue. However, the discovery of relationships between different languages remains an open problem. In this paper we propose a method, which applies the growing hierarchical self- organizing map (GHSOM) model, to discover knowledge from multilingual text documents. Multilingual parallel corpora were trained by the GHSOM to generate hierarchical feature maps. A discovery process is then applied on these maps to discover the relationships between documents of different languages. The relationships between keywords of different languages are also revealed. We conducted experiments on a set of Chinese-English bilingual parallel corpora to discover the relationships between documents of these languages.

Research work related to plagiarism detection methods in dealing with monolingual texts (e.g. English texts) have been well established in recent years. However, little attention has been paid to facilitate plagiarism detection in cross-lingual text collections (e.g. English and Chinese texts). In this paper we present a system platform to evaluating text similarity and relatedness in multilingual text collections for plagiarism detection. First, we utilized a number of selected texts in Chinese-English parallel corpora collected from Internet to train text classifiers based on the Support Vector Machines (SVM) model. As such, the multilingual texts of unknown category can be classified by the trained classifiers. Subsequently, the resulting categorized texts were measured by means of a language-neutral clustering technique based on Self-Organizing Maps (SOM) method for evaluating semantic similarity among texts. The preliminary results show that our platform framework has the potential for cross-lingual text relatedness evaluation and plagiarism detection.

In this paper, we develop a platform framework for categorization of cancer related abstracts using support vector machines (SVMs) based text categorization techniques with a one-against-all (OAA) learning algorithm for classification decisions. The corpora for the work were selected from the Website of PubMed database. By using information derived from PubMed literature source, including topics of breast cancer, cervical cancer, gastric cancer, lung cancer, rectum cancer and esophagus cancer, we randomly selected 6,000 medical abstracts for implementing our system and performing experiments. The experimental results show that the platform model has potentials for categorization of multiple cancer related literature texts.

We propose a method that could automatically annotating images with some keywords that could feasibly describe the semantics of the images. A set of training images as well as their annotations are trained to find the relationships between images as well as between keywords. New image could then be annotated and retrieved according to such relationships. Our preliminary experiments suggest promising result in both image annotation task and image retrieval task.

The ability to automatically identify relationships between cancer diseases and external factors from medical records for supporting cancer diagnosis would be a valuable contribution in public health fields. Unfortunately, so far little attention has been paid on such a problem domain to developing effective solutions. In this work, we propose a prototype for automating the extraction of relationships between cancer diseases and potential factors from clinical records. We describe a scheme of the system prototype that integrates cancer ontology, and developed text mining techniques, covering self-organizing maps (SOM) algorithm as well as support vector machines (SVM) methods to carry out the system development. The results show that the integration of medical ontology and the text mining platforms is capable of extracting the potential patterns and re-categorize clinical records.

The WWW provides an ultimate source of information for all kinds of knowledge in various kinds of languages. There are emerging needs for searching documents in different languages, causing multilingual information retrieval an active research topic recently. The performance of such task depends on the degree of understanding for the relationships between different languages. Multilingual text mining aims at discovering interesting relationships between different languages. In this work, we applied the growing hierarchical self-organizing map model to cluster multilingual text documents and find the relationships between two languages. We use a set of parallel corpora to train the map and apply a discovering process to identify the semantic groups and hierarchical structures of keywords for these languages. The discovered knowledge can then be applied to tasks such as multilingual information retrieval and automatic multilingual thesaurus construction.

Latent Semantic Indexing is a well known technique in Information Retrieval, especially in dealing with polysemy and synonymy. LSI use SVD process to decompose the original term-document matrix into a lower dimension triplet. The triplet (the resulted matrices) is the approximation to original matrix and can capture the latent semantic relation between terms. In this paper, we propose a novel method for multilingual text categorization using Latent Semantic Indexing. The centroid of each class has been calculated in the decomposed SVD space. The similarity threshold of categorization is predefined for each centroid. Test documents with similarity measurement larger than the threshold will be labeled "Positive" (Relevant) or else would be labeled "Negative" (Non-Relevant). Experimental result indicated that the performance on the precision, recall and F1 are quite good using LSI technique to categorize the multi-language text. The F1 measurement has an average value of 70% and the precision can reach 80% using our algorithm.

One major approach for information finding in the WWW is to navigate through some web directories and browse them for the goal pages. However, such directories are generally constructed manually and have disadvantages of narrow coverage and inconsistency. In this work, we propose a machine learning approach that automatically constructs a navigational structure for the WWW to help information finding. A self-organizing map is constructed to train the web pages and obtain two feature maps, which reveal the relationships among web pages and thematic keywords respectively. We then use these maps to develop a structure that may assist the users finding the information they need. We used a small set of web pages in the experiments and obtained promising result.

The semantic Web has emerged to replace the World Wide Web (WWW or the Web) as the unique platform for information sharing. Applications such as e-commerce will be and could be plausible only if we can annotate the Web pages with their semantics. For newly developed semantic Web resources, such annotation can be done manually or by help of some authoring tools. However, it is not practical to semantically annotating existing Web pages due to the gigantic amount of them. To overcome this difficulty, we propose a machine learning approach to automatically generate semantic metadata for Web pages. The proposed automated process adopts the self-organizing map algorithm to cluster training Web pages and conducts a text mining process to discover some semantic descriptions about the Web pages. Preliminary experiments show that our method may generate semantically relevant metadata for the Web pages

Topic maps provide a general, powerful, and user-oriented way to navigate the information resources under consideration in any specific domain. A topic map provides a uniform framework that not only identifies important subjects from an entity of information resources and specifies the resources that are semantically related to a subject, but also explores the relations among these subjects. When a user needs to find some specific information on a pool of information resources, he or she only needs to examine the topic maps of this pool, select the topic that seems interesting, and the topic maps will display the information resources that are related to this topic, as well as its related topics. The user will also recognize the relationships among these topics and the roles they play in such relationships. With the help of the topic maps, you no longer have to browse through a set of hyperlinked documents and hope that you may eventually reach the information you need in a finite amount of time, while knowing nothing about where to start. You also don’t have to gather some words and hope that they may perfectly symbolize the idea you’re interested in, and be well-conceived by a search engine to obtain reasonable result. Topic maps provide a way to navigate and organize information, as well as create and maintain knowledge in an infoglut. To construct a topic map for a set of information resources, human intervention is unavoidable at the present time. Human effort is needed in tasks such as selecting topics, identifying their occurrences, and revealing their associations. Such a need is acceptable only when the topic maps are used merely for navigation purposes and when the volume of the information resource is considerably small. However, a topic map should not only be a topic navigation map. The volume of the information resource under consideration is generally large enough to prevent the manual construction of topic maps. To expand the applicability of topic maps, some kind of automatic process should be involved during the construction of the maps. The degree of automation in such a construction process may vary for different users with different needs. One person may need only a friendly interface to automate the topic map authoring process, while another may try to automatically identify every component of a topic map for a set of information resources from the ground up. In this article, we recognize the importance of topic maps not only as a navigation tool but also as a desirable scheme for knowledge acquisition and representation. According to such recognition, we try to develop a scheme based on a proposed text-mining approach to automatically construct topic maps for a set of information resources. Our approach is the opposite of the navigation task performed by a topic map to obtain information. We extract knowledge from a corpus of documents to construct a topic map. Although currently the proposed approach cannot fully construct the topic maps automatically, our approach still seems promising in developing a fully automatic scheme for topic map construction.

The quantification of evaluating semantic relatedness among texts has been a challenging issue that pervades much of machine learning and natural language processing. This paper presents a hybrid approach of a text-mining technique for measuring semantic relatedness among texts. In this work we develop several text classifiers using support vector machines (SVM) method to supporting acquisition of relatedness among texts. First, we utilized our developed text mining algorithms, including text mining techniques based on classification of texts in several text collections. After that, we employ various SVM classifiers to deal with evaluation of relatedness of the target documents. The results indicate that this approach can also be fitted to other research work, such as information filtering, and recategorizing resulting documents of search engine queries.

This paper describes a new application of a text-mining algorithm to the text sources of bilingual corpora. In the past, the majority of the approaches applied to measuring semantic relatedness was based on edge counting methods through a semantic network, such as WordNet. It is not well suited for applications in specific domains in which the standard lexical knowledge bases are not available. In this work, we propose an alternative solution for acquisition of semantic relatedness from text corpora by means of a machine learning technique, namely the self-organizing maps. This paper presents a hybrid approach to discovering a concept-based feature map containing word clusters and document clusters from multilingual text collections. Using SOM-based automatic clustering techniques, we have conducted several experiments to uncover associated documents based on Chinese-English bilingual parallel corpora, and a hybrid Chinese-English corpus. In essence, this work provides a method for automatic text clustering, which resolves some of the language difficulties in concept discovery and categorization from multilingual text corpora.

The printed areas of a handprinted character with thick strokes were replaced by a frame formed by bended ellipses to represent the character efficiently and emulate high order receptive fields in a visual system. To afford topology preservation during adaptive matching of this frame with a template frame, we employ a devised self-organization model. This model uses these bended ellipses as training patterns in searching, measuring and updating their corresponding ellipses in the template frame. The neighborhood of a corresponding ellipse is also weighted by the appearance of the training bended-ellipse. With this method, each handprinted character can effectively evolve into its template character with predetermined training parameters. Each template has a different number of training cycles. Within this controlled number of cycles, the model can flex a handprinted character into a correct template.

Traditional content-based image retrieval (CBIR) systems often fail to fulfill a user’s need due to the ‘semantic gap’ existed between the extracted features of the systems and the user’s query. In this paper we propose a novel approach to bridge the semantic gap which is the major deficiency of CBIR systems. We conquer the deficiency by extracting semantics of an image from the environmental texts around it. We apply a text mining process, which adopts the self-organizing map (SOM) learning algorithm as a kernel, on the environmental texts of an image to extract the semantic information from this image. Some implicit semantic information of the images can be discovered after the text mining process. We also define a semantic relevance measure to achieve the semantic-based image retrieval task. We performed experiments on a set of images which are collected from web pages and obtained promising results.

Recently, many approaches have been devised for mining various kinds of knowledge from texts. One important application of text mining is to identify themes and the semantic relations among these themes for text categorization. Traditionally, these themes were arranged in a hierarchical manner to achieve effective searching and indexing as well as easy comprehension for human beings. The determination of category themes and their hierarchical structures was mostly done by human experts. In this work, we developed an approach to automatically generate category themes and reveal the hierarchical structure among them. We also used the generated structure to categorize text documents. The document collection was trained by a self-organizing map to form two feature maps. We then analyzed these maps and obtained the category themes and their structure. Although the test corpus contains documents written in Chinese, the proposed approach can be applied to documents written in any language, and such documents can be transformed into a list of separated terms.

The paper describes a new application of a text-mining algorithm to the text sources of bilingual parallel corpora. The ultimate task, being undertaken in the context of a Chinese-English machine translation project, will be to develop a language-neutral method to discover similar documents from multilingual text collections. Using a variation of automatic clustering techniques which apply a neural net approach, namely the self-organizing maps (SOM), we have conducted several experiments to uncover associated documents based on Chinese-English bilingual parallel corpora, and a hybrid Chinese-English corpus. The experiments show some interesting results and a couple of potential ways for future work towards the field of multilingual information discovery. In addition, for exploring the impacts on linguistic issues with the machine learning approach to mining sensible linguistics elements from multilingual texts, we have examined the resulting term associations and text associations from the view of cross-lingual text similarity. To evaluate semantic relatedness of the mined bilingual texts, we applied a measure technique of semantic similarity in the resulting bilingual document clusters and word clusters. The paper presents algorithms that enable multilingual text mining based on the self-organizing map (SOM) for automatically grouping similar multilingual texts (i.e. Chinese and English texts), along with a means of measuring their semantic similarity to resolve the difficulties of syntactic and semantic ambiguity in multilingual information access.

Discusses current work using an adaptive learning algorithm to dynamically create the content of an e-commerce search engine so that the implicit knowledge extracted by the Web text mining module can be provided in the B2B (business-to-business) portal. In this paper, we develop an algorithmic approach for automatically discovering implicit customer knowledge from the Internet by means of a Web mining method. Using a variation of the automatic thesaurus generation techniques, namely the self-organizing map (SOM) neural net, we have conducted several experiments in a specific domain in which we created a functional thesaurus of numerous supplier- and product-specific terms. Further, we applied such a thesaurus in a topic hierarchy-based text database, as an organized text source of a search engine for a novel B2B e-commerce portal.

We achieved content-based image retrieval by using the shape information contained in a image. A kind of high-order image features which emulate the feature detection process of human eyes were used to represent both input image and the template images stored in a database. Template matching was then applied between the input image and each template image to obtain the retrieval result. The matching process performs a kind of pseudo elastic matching between the feature sets of the input image and each template image. Such elastic matching process, together with the high-order features, provides an excellent approach to measure the dissimilarity, namely the spatial topology distance, between images. The method had been tested on the Columbia Object Image Library database. Preliminary experiments suggested promising result by our approach.

In this work we developed a new automatic hypertext construction method based on a proposed text mining approach. Our method applies the self-organizing map algorithm to cluster some flat text documents in a training corpus and generate two maps. We then use these maps to identify the sources and destinations of some important hyperlinks within these training documents. The constructed hyperlinks are then inserted into the training documents to translate them into hypertext form. Such translated documents form the new corpus. Incoming documents can also be translated into hypertext form and added to the corpus through the same approach. Our method had been tested on a set of flat text documents collecting from several newswire sites. Although we only used Chinese text documents, our approach can be applied to any document that can be transformed to a set of indexed terms.

One important task for text data mining is automatic text categorization, which assigns a text document to some predefined category according to their correlations. Traditionally, these categories as well as the correlations among them are determined bp human experts. In this paper, we devised a novel approach to automatically generate categories. The self-organizing map model is used to generate two maps, namely the word cluster map and the document cluster map, in which a neuron represents a cluster of words and documents respectively. Our approach is to analyze the document cluster map to find centroids of some super-clusters. We also devised a method to select the category term from the word cluster map. The hierarchical structure of categories may be generated by recursively applying the same method. Text categorization is the natural consequence of such automatic category generation process.

The printed areas of a handprinted character with thick strokes were replaced by a frame formed by bended ellipses to represent the character efficiently and emulate the high order receptive fields in later visual system. Each bended ellipse maximally fits the local stroke pattern and captures the position, orientation and topology information contained in the local stroke pattern. Complex stroke structures are represented by concept neurons which each contains several bended ellipses. The craft of concept neurons provides an uniform representation for receptive fields in any order. The model uses these concept neurons in searching their corresponding neurons in the template frame. To obtain the correspondence, a global affine transform followed by a local distorting process are used to align the two frames. To afford topology preservation the topology order of the character is generated explicitly. The classification is achieved by examining the similarity between the topology order of the handprinted pattern and the template patterns.

The 11th Conference on Artificial Intelligence and Applications (TAAI 2006), which was held during Dec. 15-16, 2006 at Kaohsiung, Taiwan, is the annual conference of Taiwanese Association for Artificial Intelligence. The conference is intended to provide a forum for researchers and scholars in the related fields of artificial intelligence. Past conferences have proven them successful attempts to become the most important meeting of artificial intelligence researchers in Taiwan. This is also true for TAAI 2006, which focuses on various aspects on theory and practice of artificial intelligence. In this special issue, 11 papers presented in the conference are selected and extended for their outstanding performance on the conference. These papers cover wide spreading aspects, which include versatile motion planning, particle swarm optimization, data mining, image retrieval, music retrieval, natural language processing, navigation, fuzzy logic, gaming, and bioinformatics. This issue thus concisely summarizes recent advances in artificial intelligence and its applications. The readers should find them valuable and inspiring. We hope that this issue should provide a valuable resource for their researches. As guest editors of this special issue, we like to express our greatest gratitude to those that help this issue come true. Thanks to all contributors and referees for their elaborate works and careful reviews that assure the high quality of this issue. Special thanks should go to Mr. Makoto Shimada of Fuji Technology Press for his efforts and kind assistance in publishing this issue. Finally, we also like to thank the Editors in Chief of JACIII, Prof. Toshio Fukuda and Prof. Kaoru Hirota, for their generous hospitality in supporting this special issue.