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Recently, approaches of applying personality on practical applications have attracted attention from multidisciplinary areas beyond psychology. An example is the personality-based recommender systems that have been successfully applied to music, movie, and game recommendation tasks. However, finding the personality traits of a person is not a trivial task. In this work, we will propose an automatic scheme for identifying the Big Five personality traits using text mining techniques. We analyzed the social messages posted by Facebook users to find the correlations among keywords and personality traits. The personality traits of a person can then be identified by examining his social messages according to such correlations. We performed the experiments using the myPersonality dataset and obtained promising results.

Graphical abstractThis work proposes a scheme to improve the self-organizing map algorithm. This is the overall flowchart of the proposed algorithm. The key ingredients include:1.A novel topic identification scheme.2.Lateral expansion using novel topic incompatibility measure.3.Hierarchical expansion scheme using novel cluster size and topic size criteria.Display Omitted HighlightsIncorporation of topic identification into SOM learning could be beneficial to text categorization task.Both lateral and hierarchical expansion during SOM learning were achieved according to criteria based on identified topics.The produced text hierarchies outperformed contemporary approaches in quality and performance on text categorization. Self-organizing maps (SOM) have been applied on numerous data clustering and visualization tasks and received much attention on their success. One major shortage of classical SOM learning algorithm is the necessity of predefined map topology. Furthermore, hierarchical relationships among data are also difficult to be found. Several approaches have been devised to conquer these deficiencies. In this work, we propose a novel SOM learning algorithm which incorporates several text mining techniques in expanding the map both laterally and hierarchically. On training a set of text documents, the proposed algorithm will first cluster them using classical SOM algorithm. We then identify the topics of each cluster. These topics are then used to evaluate the criteria on expanding the map. The major characteristic of the proposed approach is to combine the learning process with text mining process and makes it suitable for automatic organization of text documents. We applied the algorithm on the Reuters-21578 dataset in text clustering and categorization tasks. Our method outperforms two comparing models in hierarchy quality according to users' evaluation. It also receives better F1-scores than two other models in text categorization task.

Social bookmarking Websites are popular nowadays for they provide platforms that are easy and clear to browse and organize Web pages. Users can add tags on Web pages to allow easy comprehension and retrieval of Web pages. However, tag spams could also be added to promote the opportunity of being referenced of a Web page, which is troublesome to users for accessing uninterested Web pages. In this work, we proposed a scheme to automatically detect such tag spams using a proposed text mining approach based on self-organizing map (SOM) model. We used SOM to find the associations among Web pages as well as tags. Such associations were then used to discover the relationships between Web pages and tags. Tag spams can then be detected according to such relationships. Experiments were conducted on a set of Web pages collected from a social bookmarking site and obtained promising result.

Nowadays most of the Web pages contain little amount of structure and supporting information that can reveal their semantics or meanings. To enable automated processing of the Web pages, semantic information such as metadata and tags regarding to each page should be added to it. Several authoring tools have been developed to help users tackling this task. However, manual or semi-automatic authoring is implausible when we intend to annotate large amount of Web pages. In this work, we proposed a method to automatically generate some descriptive metadata and tags for a Web page. The idea is to apply the self-organizing map algorithm to cluster the Web pages and discover the relationships between these clusters. In the mean time, the themes of each cluster are also identified. We then use such relationships and themes to tag the Web pages and generate metadata for the Web pages. The result of experiments shows that our method may generate semantically relevant metadata and tags for the Web pages.

With the increasing number 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. We also use such relationships to perform multilingual information retrieval tasks. The experimental results show that our multilingual text mining approach may capture conceptual relationships among documents as well as keywords written in different languages.

Traditional content-based image retrieval (CBIR) systems often fail to meet a user's need due to the 'semantic gap' between the extracted features of the systems and the user's query. The cause of the semantic gap is the failure of extracting real semantics from an image and the query. To extract semantics of images, however, is a difficult task. Most existing techniques apply some predefined semantic categories and assign the images to appropriate categories through some learning processes. Nevertheless, these techniques always need human intervention and rely on content-based features. 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. Since an image generally co-exists with accompanying texts in various formats, we may rely on such environmental texts to discover the semantics of the image. 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.

In the manufacturing of semiconductor encapsulation, the production yield is one of critical issues concerned by all foundries. It is because that yield rate can directly affects the quality of the final product and the profitability. In this work we take the defect-products as samples and use machine learning techniques to classify the samples and verify the accuracy and feasibility of the experiment. We use Support Vector Machines (SVM) model to perform classification and compare the resulting accuracy with the results of Back Propagation Neural Network (BPN) model. Furthermore, we employ a statistical method namely Pearson product-moment correlation coefficient to identify the influential factors for production quality. The experimental result demonstrates that our hybrid method has great potentials for yield improvement in semiconductor manufacturing.

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.

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.

Self-organizing map (SOM) algorithm has been applied widely in tasks such as data clustering and visualization. Two major deficiencies of classical SOM are the need of predefined map structure and the lack of hierarchy generation. Several approaches have been devised to tackle these deficiencies. One of our previous works, namely the topic-oriented self-organizing map (TOSOM), tries to remedy the classical SOM by combining topic identification process into the training phase. In this work, we will further expand the learning algorithm of TOSOM by incorporating user's constraints. Both structural and topical constraints which specified by the user could be used to guide the learning process. Preliminary experiments demonstrate improvements over previous algorithm on text categorization task.

In this work we present a self-organization matching approach to accomplish the recognition of handprinted characters drawn with thick strokes. This approach is used to flex the unknown handprinted character toward matching its object characters gradually. The extracted character features used in the self-organization matching are center loci, orientation, and major axes of ellipses which fit the inked area of the patterns. Simulations provide encouraging results using the proposed method.

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. One major shortage of classical SOM learning algorithm is the necessity of predefined map topology. Furthermore, hierarchical relationships among data are also difficult to be revealed. In this work, we propose a novel SOM learning algorithm which incorporates several text mining techniques in expanding the map both laterally and hierarchically that could discover the relationships among documents in both perspectives. The proposed algorithm will first cluster a set of training documents using classical SOM algorithm. We then identify the topics of each cluster and use them to evaluate the criteria on expanding the map. We applied the algorithm on medium-size datasets and obtained promising result.

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.

With the continuously increasing needs of location information for users around the world, applications of geospatial information have gained a lot of attention in both research and commercial organizations. Extraction of semantics from the image content for geospatial information seeking and knowledge discovery has been thus becoming a critical process. Unfortunately, the available geographic images may be blurred, either too light or too dark. It is therefore often hard to extract geographic features directly from images. In this paper, we describe our developed methods in applying local scale-invariant features and bag-of-keypoints techniques to annotating images, in order to carry out image categorization and geographic knowledge discovery tasks. First, local scale-invariant features are extracted from geographic images as representative geographic features. Subsequently, the bag-of-keypoints methods are used to construct a visual vocabulary and generate feature vectors to support image categorization and annotation. The annotated images are classified by using geographic nouns. The experimental results show that the proposed approach is sensible and can effectively enhance the tasks of geographic knowledge discovery.

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.

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.

In this paper we describe our work on developing a novel technique for discovery of implicit knowledge about patents from multilingual patent information sources. In this work we developed a system platform to support locating similar and relevant multilingual patent documents. The platform was implemented 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 could 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.

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.

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.

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.

In this paper we discuss the implementation of the leading supervised and unsupervised approaches for multilingual text categorization. We selected support vector machines (SVM) and latent semantic indexing (LSI) techniques as representatives of supervised and unsupervised methods for system implementation, respectively. The preliminary results show that our platform models including both supervised and unsupervised learning methods have the potentials for multilingual text categorization

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 NaviSOM, a machine learning approach to automatically construct a navigational structure for the WWW. 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.

Game-based learning has been recognized as an effective way for learning and has emerged to be a promising approach in addition to traditional lecture-based learning. Many schemes, methodologies, as well as systems were developed and applied to training courses in both academic and industrial domains. However, the creation of games for learning purposes is not easy for common teachers since it requires lots of specific knowledge about game development such as computer animations, programming, human-computer interface design, etc. Customized games are thus used for specific types of courses. Such customized games are, unfortunately, hard to adapt to individual courses and require much time for development. In this work, we propose a general framework for creating games that can be applied in many domains without much effort of the teachers. Teachers may create games through writing scripts that define the objectives of courses and the steps required to complete the objectives. The system will automatically create a game based on the scripts.

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.

The World Wide Web (WWW) has been recognized as the ultimate and unique source of information for information retrieval and knowledge discovery communities. Tremendous amount of knowledge are recorded using various types of media, producing enormous amount of web pages in the WWW. Retrieval of required information from the WWW is thus an arduous task. Different schemes for retrieving web pages have been used by the WWW community. One of the most widely used scheme is to traverse predefined web directories to reach a user's goal. These web directories are compiled or classified folders of web pages and are usually organized into hierarchical structures. The classification of web pages into proper directories and the organization of directory hierarchies are generally performed by human experts. In this work, we provide a corpus-based method that applies a kind of text mining techniques on a corpus of web pages to automatically create web directories and organize them into hierarchies. The method is based on the self-organizing map learning algorithm and requires no human intervention during the construction of web directories and hierarchies. The experiments show that our method can produce comprehensible and reasonable web directories and hierarchies.

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.

In this paper we propose a novel method to bridge the 'semantic gap' between a user's information need and the image content. The semantic gap describes the major deficiency of content-based image retrieval (CBIR) systems which use visual features extracted from images to describe the images. We conquer the deficiency by extracting semantic of an image from the environmental texts around it. Since an image generally co-exists with accompany texts in various formats, we may rely on such environmental texts to discover the semantic of the image. A text mining approach based on self-organizing maps is used to extract the semantic of an image from its environmental texts. We performed experiments on a small set of images and obtained promising results.

Topic maps standard (ISO-13250) has been gradually recognized as an emerging standard for information exploration and knowledge organization in the web era. One advantage of topic maps is that they enable a user to navigate and access the documents he wants in an organized manner, rather than browsing through hyperlinks that are generally unstructured and often misleading. Nowadays, the topic maps are generally manually constructed by domain experts or users since the functionality and feasibility of automatically generated topic maps still remain unclear. In this work we propose a semi-automatic scheme to construct topic maps. We first apply a text mining process on a corpus of information resources to identify the topics and discover the relations among these topics. Necessary components of topic maps such as topics, topic types, topic occurrences, and topic associations may be automatically revealed by our method.

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.

In this paper we propose a novel method to discover the semantics of an image within a web page and bridge the semantic gap between the image features and a user’s query. Since an image is always accompanied with some text segments which are closely related to the image, we propose that the semantics of the image may be discovered from such text segments through a data mining process on these texts. Based on such recognition, we applied a text mining process on the accompany texts of an image to discover the themes of this image, which constitute the semantics of this image. The self-organizing map algorithm is first applied to cluster a set of preprocessed web pages. Based on the clustering result, we design a theme identification process to identify a set of words which constitute the semantics of an image. We performed experiments on a small set of images and obtained promising results.

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.

E-learning has gradually been accepted as an alternative for traditional lecture-based learning. One key factor for the success of e-learning is the possibility of understanding the semantics of learning contents autonomously by machines. The semantic web could naturally fit in according to its ability on information interchange and sharing between machines. Such ability is made possible when the semantic web pages are properly annotated. To transform the existing semantics-lacking learning contents to semantics-enriched ones, we propose a machine learning approach to automatically generate semantic markups for traditional learning contents, which are usually presented in web pages. The proposed method applies the self-organising map algorithm to cluster training web pages and conducts a text mining process to discover the anchor texts to be tagged and their semantic descriptions. Preliminary experiments show that our method may successfully generate semantic markups for the web pages that could be used for e-learning in the semantic web environment.

The semantic Web is gaining popularity for its ability on information interchange and sharing between machines. Such ability is possible when the Web pages are properly annotated. For newly developed semantic Web resources, such annotation can be done manually or by help of sophisticated authoring tools. However, it is not practical to semantically annotating existing Web pages due to the gigantic amount of them. To bridge the gap between the semantic Web and the World Wide Web, we propose a machine learning approach to automatically generate semantic markups for traditional Web pages. The proposed method applies the self-organizing map algorithm to cluster training Web pages and conducts a text mining process to discover the words to be tagged and their semantic descriptions. Preliminary experiments show that our method may successfully generate semantical markups for the Web pages