Multi-modal neuroimaging analysis is crucial for a comprehensive understanding of brain function and pathology, as it allows for the integration of different imaging techniques, thus overcoming the limitations of individual modalities. However, the high costs and limited availability of certain modalities pose significant challenges. To address these issues, this paper proposes the Condition-Aligned Temporal Diffusion (CATD) framework for end-to-end cross-modal synthesis of neuroimaging, enabling the generation of functional magnetic resonance imaging (fMRI)-detected Blood Oxygen Level Dependent (BOLD) signals from more accessible Electroencephalography (EEG) signals. By constructing Conditionally Aligned Block (CAB), heterogeneous neuroimages are aligned into a latent space, achieving a unified representation that provides the foundation for cross-modal transformation in neuroimaging. The combination with the constructed Dynamic Time-Frequency Segmentation (DTFS) module also enables the use of EEG signals to improve the temporal resolution of BOLD signals, thus augmenting the capture of the dynamic details of the brain. Experimental validation demonstrates that the framework improves the accuracy of brain activity state prediction by 9.13% (reaching 69.8%), enhances the diagnostic accuracy of brain disorders by 4.10% (reaching 99.55%), effectively identifies abnormal brain regions, enhancing the temporal resolution of BOLD signals. The proposed framework establishes a new paradigm for cross-modal synthesis of neuroimaging by unifying heterogeneous neuroimaging data into a latent representation space, showing promise in medical applications such as improving Parkinson's disease prediction and identifying abnormal brain regions.

This paper is a brief personal journal of our relentless pursuit of Web Intelligence (WI). How it all started? What have achieved? Where are we heading? Our search for the ultimate meaning of the Web enables us to see and appreciate the power of the Web for building a better human society through collaboration, co-learning, and co-creation. The Web is a powerful idea, a scientific and technological innovation, and a social creation. Web Intelligence explores the connectivity, diversity, and plasticity of the Web, as well as the global brain supported by the Web. The goal of research on Web Intelligence is to build a better connected world of everything, by people, and for a new intelligent human society.

The convergence of systems neuroscience and open science arouses great interest in the current brain big data era, highlighting the thinking capability of intelligent agents in handling multi-source knowledge, information and data across various levels of granularity. To realize such thinking-inspired brain computing during a brain investigation process, one of the major challenges is to find a holistic brain map that can model multi-dimensional variables of brain investigations across brain functions, experimental tasks, brain data and analytical methods synthetically. In this paper, we propose a context-enhanced graph learning method to fuse open knowledge from different sources, including: contextual information enrichment, structural knowledge fusion, and holistic graph learning. Such a method can enhance contextual learning of abstract concepts and relational learning between two concepts that have large gap from different dimensions. As a result, an extensible space, namely Thinking Space, is generated to represent holistic variables and their relations in a map, which currently contributes to the field of brain research for systematic brain computing. In the future, the Thinking Space coupled with the rapid development and spread of artificial intelligence generated content will be developed in more scenarios so as to promote global interactions of intelligence in the connected world.

Graph Neural Networks (GNNs) have been successfully used to learn user and item representations for Collaborative Filtering (CF) based recommendations (GNN-CF). Besides the main recommendation task in a GNN-CF model, contrastive learning is taken as an auxiliary task to learn better representations. Both the main task and the auxiliary task face the noise problem and the distilling hard negative problem. However, existing GNN-CF models only focus on one of them and ignore the other. Aiming to solve the two problems in a unified framework, we propose a Multi-Mixing strategy for GNN-based CF (M2GCF). In the main task, M2GCF perturbs embeddings of users, items and negative items with sample-noise by a mixing strategy. In the auxiliary task, M2GCF utilizes a contrastive learning mechanism with a two-step mixing strategy to construct hard negatives. Extensive experiments on three benchmark datasets demonstrate the effectiveness of the proposed model. Further experimental analysis shows that M2GCF is robust against interaction noise and is accurate for long-tail item recommendations.

Sentiment analysis classification models trained using neural networks require large amounts of data, but collecting these datasets requires significant time and resources. Although artificial data has been used successfully in computer vision, there are few effective and generalizable methods for creating artificial augmented text data. In this paper, a text based data augmentation method is proposed called back-and-forth translation that can be used to artificially increase the size of any natural language dataset. By creating augmented text data and adding it to the original dataset, it is demonstrated by empirical experiments that back-and-forth translation data augmentation can reduce the error rate in binary sentiment classification models by up to 3.4%. These results are shown to be statistically significant.

Brain informatics adopts a systematic methodology to study clinical diagnosis and pathology of mental disorders from macro, meso and micro points of view. Systematic studies produce multi-level and multi-aspect health big data of mental disorders. Effectively integrating multi-level mental health big data and providing multi-level and content-oriented data services for different types of users by an open and extendable mode becomes new requirements on the acquisition and computing of mental health big data. The existing Data-Brain-based brain data center cannot meet these new requirements. This paper proposes a brain and health big data center and develops a content-oriented cloud service architecture, namely, wisdom as a service, for realizing the brain and health big data center. The illustrative example demonstrates significance and usefulness of the proposed approach.

This research is aimed to analysis the resting EEG features in depression and the application in clinic. Sixteen patients with depression and sixteen healthy controls were involved in this study. Both features from the alpha and beta frequency bands were selected to analysis in this study. First the features' sensitivity to the group-difference and the correlation to the clinical HAMD scale score were analyzed, and then the classification method was used to further test the role of the resting EEG features in depression. The results showed that the difference between depression and healthy controls in the absolute power of beta band in the left prefrontal lobe was significant. And the alpha left-right asymmetry in the prefrontal cortex had a correlation with HAMD scale score. In addition, the classification based on the features showed that there was a relative higher accuracy rate to identify the depressions than to identify the healthy controls. Specifically, the classification based on alpha asymmetry was higher than that based on beta asymmetry, and the absolute power in beta band was higher than that in alpha band. Alpha asymmetry is a traditional sensitive resting EEG features for depression, this study provide new evidence to support the view. The findings here further suggest that absolute power in beta band would be important biomarker in depression.

Recommender systems aim to provide users with preferred items to address the information overload problem in the Web era. Social relations, item connections, and user-generated item reviews and ratings play important roles in recommender systems as they contain abundant potential information. Many methods have been proposed to predict users’ ratings by learning latent topic factors from their reviews and ratings of corresponding items. However, these methods ignore the relationships among items and cannot make full use of the complicated relations between reviews and ratings. Motivated by this observation, we integrate ratings, reviews, user connections and item relations to improve recommendations by combining matrix factorization with the Latent Dirichlet Allocation (LDA) model. Experimental results on two real-world datasets prove that item–item relations contain useful information for recommendations, and our model effectively improves recommendation quality.

In order to explore the mental retardation of patients with depression, the visual search paradigm was used in this study. The emotional expression (happy, sad) and neutral expression were used as interdependent or search targets in this paradigm. Subjects were asked to search the target face form a matrix containing 16 emotional faces after watching the target face. The measurement indices of the search process - scanpath duration (SPD), scanpath length (SPL), convex hull area (CHA)-were collected and analyzed. The three indexes of patients were all larger than those in control group and there were significant differences between the groups. It can be seen that two kinds of emotional faces reduce the search efficiency of depression patients and there is a slow response and mental retardation phenomenon for depression in the search process.

As the ϵ4 allele of apolipoprotein E (APOE4) is proved a high risk factor of Alzheimer's disease (AD), numerous studies have used modalities of neuroimaging data to investigate the alterations of brain caused by APOE4. A recent study has shown that APOE4-related pathological changes of cortical networks during rest exist in APOE4 carriers. However, the interrelationship among the resting intrinsic networks (ICNs) has not been adequately explored. In the present study, seven ICNs were detected in both of APOE4 carriers and APOE4 non-carriers with spatial independent component analysis (ICA). Then the effective connectivity patterns of the two groups were acquired using multivariate Granger causality analysis (mGCA) the results of which reflect the information flow among ICNs. Compared with APOE4 non-carriers, significant difference in activity was found within default mode network in APOE4 carriers. Moreover, the significantly reduced intensity of causal interaction from auditory network to cerebellum network was found in APOE4 carriers. And we found the significantly causal relationship from cerebellum network to default mode network only existed in the connectivity pattern of APOE4 carriers but not in that of APOE4 non-carriers. In addition, the subcortical network and cognitive control network in APOE4 carriers were found less influenced by other ICNs. The findings in our study suggest that APOE4 indeed modulates the activities and interactions of ICNs, which might be the genetic cause of the dysfunctional process in APOE4 carriers. Our findings may shed new light on the ways how APOE4 affects the functions of nervous systems and provide a perspective to understand genetic basis of pathogenesis of AD.

Top-N recommendation tasks aim to solve the information overload problem for users in the information age. As a user's decision may be affected by correlations among items, we incorporate such correlations with the user and item latent factors to propose a Poisson-regression-based method for top-N recommendation tasks. By placing priori knowledge and using a sparse structure assumption, this method learns the latent factors and the structure of the item-item correlation matrix through the alternating direction method of multipliers (ADMM). The preliminary experimental results on two real-world datasets show the improved performance of our approach.

Entity recognition is the basis of text mining. With the further development of knowledge-driven applications, types of target entities are increasingly subdivided. The lack of corpus and the limited number of entity have been the main challenges of entity recognition. Based on this observation, this paper proposes a weak-supervision method for recognizing entities from a specifically narrow domain by fusing domain relevance measurement and context information. The experimental result shows that the proposed method has high efficiency and accuracy without manual participation.

The query and selection of scientific literatures are knowledge driven. Researchers regard public literature resources as target knowledge sources and use their own domain knowledge to explore in them. However, existing knowledge-driven methods of literature recommendation mainly focus on morphological matching and cannot effectively resolve polysemous phenomenon brought by "knowledge overload". Based on this observation, this paper presents a knowledge-driven approach for personalized literature recommendation. Domain ontology, synonyms and knowledge labels are integrated into a multidimensional domain knowledge map for modeling user knowledge requirements and literature contents based on deep semantic discrimination. The personalized recommendation is achieved by calculating knowledge distances between users and literatures. Experimental results on a real data set of PubMed show that the recommended relevance of the current method is 67%, better than other methods.

Matrix factorization (MF) models and their extensions are widely used in modern recommender systems. MF models decompose the observed user-item interaction matrix into user and item latent factors. In this paper, we propose mixture models which combine the technology of MF and the embedding. We show that some of these models significantly improve the performance over the state-of-the-art models on two real-world datasets, and explain how the mixture models improve the quality of recommendations.

In order to actualize the quantitative reasoning function into the portable brain and mental health-monitoring system under WaaS architecture, this study proposes a method named quantitative reasoning for depressive mood status based on portable EEG and self-rating scale data. 5 inpatients were recruited to join the experiment, from which the portable EEG data and clinical self-rating scale data of 2 weeks were collected. The principal component analysis method is adopted to process the self-rating data. The regression analysis based on random forest algorithm is used to generate the quantitative reasoning model for acquiring reasoning rules. In order to further implement the quantitative reasoning function, the Protege and Jena are adopted to build data ontologies and actualize an automatic reasoning function for objectively quantifying the depressive mood status respectively. The effectiveness of reasoning rules are validated, the preliminary results show that the expected quantitative value outputted from the quantitative reasoning model is highly correlated (the absolute value of correlation coefficient ≥0.7, P-value ≤0.05) with the actual self-rating scale data.

BACKGROUND: Understanding the working mechanism of the brain is one of the grandest challenges for modern science. Toward this end, the BigNeuron project was launched to gather a worldwide community to establish a big data resource and a set of the state-of-the-art of single neuron reconstruction algorithms. Many groups contributed their own algorithms for the project, including our mean shift and minimum spanning tree (M-MST). Although M-MST is intuitive and easy to implement, the MST just considers spatial information of single neuron and ignores the shape information, which might lead to less precise connections between some neuron segments. In this paper, we propose an improved algorithm, namely M-AMST, in which a rotating sphere model based on coordinate transformation is used to improve the weight calculation method in M-MST. RESULTS: Two experiments are designed to illustrate the effect of adapted minimum spanning tree algorithm and the adoptability of M-AMST in reconstructing variety of neuron image datasets respectively. In the experiment 1, taking the reconstruction of APP2 as reference, we produce the four difference scores (entire structure average (ESA), different structure average (DSA), percentage of different structure (PDS) and max distance of neurons' nodes (MDNN)) by comparing the neuron reconstruction of the APP2 and the other 5 competing algorithm. The result shows that M-AMST gets lower difference scores than M-MST in ESA, PDS and MDNN. Meanwhile, M-AMST is better than N-MST in ESA and MDNN. It indicates that utilizing the adapted minimum spanning tree algorithm which took the shape information of neuron into account can achieve better neuron reconstructions. In the experiment 2, 7 neuron image datasets are reconstructed and the four difference scores are calculated by comparing the gold standard reconstruction and the reconstructions produced by 6 competing algorithms. Comparing the four difference scores of M-AMST and the other 5 algorithm, we can conclude that M-AMST is able to achieve the best difference score in 3 datasets and get the second-best difference score in the other 2 datasets. CONCLUSIONS: We develop a pathway extraction method using a rotating sphere model based on coordinate transformation to improve the weight calculation approach in MST. The experimental results show that M-AMST utilizes the adapted minimum spanning tree algorithm which takes the shape information of neuron into account can achieve better neuron reconstructions. Moreover, M-AMST is able to get good neuron reconstruction in variety of image datasets.

Recommender systems aim to provide users with preferred items to tackle the information overload problem in the Web era. Social relations, item connections, and user-generated reviews on items contain abundant potential information. By combining matrix factorization with latent Dirichlet allocation, we integrate ratings, reviews, user similarity and item similarity in recommender systems. The experimental result on a real-world dataset proves that both item connection and user connection contain useful sources for recommendation, and our model can effectively improve recommendation quality.

With the increasing popularity of location-based services, location recommendation is one of important applications. In this paper, according to a user’s preference, we recommend locations to a user by extending an information retrieval model, namely, belief network model. We regard the user’s preference as a query, a location as a document, categories as index terms. And then, we use the belief network model to recommend locations to a user by adding expert information. Experimental results on a real world data set show that recommendation effectiveness can be improved.

One of the fundamental tasks of targeted marketing is to elicit associations between customers and products. Based on the results from information retrieval and utility theory, this article proposes a unified framework of targeted marketing. The customer judgments of products are formally described by preference relations and the connections of customers and products are quantitatively measured by market value functions. Two marketing strategies, known as the customer‐oriented and product‐oriented marketing strategies, are investigated. Four marketing models are introduced and examined. They represent, respectively, the relationships between a group of customers and a group of products, between a group of customers and a single product, between a single customer and a group of products, and between a single customer and a single product. Linear and bilinear market value functions are suggested and studied. The required parameters of a market value function can be estimated by exploring three types of information, namely, customer profiles, product profiles, and transaction data. Experiments on a real‐world data set are performed to demonstrate the effectiveness of the proposed framework.

Lack of understanding of users' underlying decision making process results in the bottleneck of EB-HCI (eye movement-based human-computer interaction) systems. Meanwhile, considerable findings on visual features of decision making have been derived from cognitive researches over past few years. A promising method of decision prediction in EB-HCI systems is presented in this article, which is inspired by the looking behavior when a user makes a decision. As two features of visual decision making, gaze bias and pupil dilation are considered into judging intensions. This method combines the history of eye movements to a given interface and the visual traits of users. Hence, it improves the prediction performance in a more natural and objective way. We apply the method to an either-or choice making task on the commercial Web pages to test its effectiveness. Although the result shows a good performance only of gaze bias but not of pupil dilation to predict a decision, it proves that hiring the visual traits of users is an effective approach to improve the performance of automatic triggering in EB-HCI systems.

Aiming at the characteristics of human-thinking-centric studies, brain informatics (BI) emphasizes a systematic methodology, in which systematic brain data analysis is a key issue. In this paper, a multiagent system, namely, the global learning scheme for BI, is proposed to perform a data-brain driven process planning. Utilizing the obtained three types of mining workflows, a three-step analytical process can be carried out for multiaspect brain data analysis. The proposed work presents a practical approach toward the data-brain driven systematic brain data analysis. A scenario is provided to demonstrate the significance and usefulness of the proposed approach.

Distance measure is quite important for pattern recognition. Utilizing invariance in image data, tangent distance is very powerful in classifying handwritten digits. For this measure a set of invariant transformations must be known a priori. But in many practical problems, it is very difficult to know these transformations. In this paper, an algorithm is proposed to approximate the invariant tangent distance exclusively from the data. By virtue of ideas arising from manifold learning, the algorithm needs no prior transformations and can be applied to more classification problems. k-nearest neighbor rule based on the new distance are implemented for classification problems. Experimental results on synthetic and real datasets illustrate its validity.

The development of brain science has led to a vast increase of brain data. To meet requirements of a systematic methodology of Brain Informatics (BI), this paper proposes a new conceptual model of brain data, namely Data-Brain, which explicitly represents various relationships among multiple human brain data sources, with respect to all major aspects and capabilities of human information processing systems (HIPS). A multidimension framework and a BI methodology-based ontological modeling approach have been developed to implement a Data-Brain. The Data-Brain, Data-Brain-based BI provenances, and heterogeneous brain data can be used to construct a Data-Brain-based brain data center which provides a global framework to integrate data, information, and knowledge coming from the whole research process for systematic BI study. Such a Data-Brain modeling approach represents a radically new way for domain-driven conceptual modeling of brain data, which models a whole process of systematically investigating human information processing mechanisms.

Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase)-based approaches should perform better than the term-based ones, but many experiments do not support this hypothesis. This paper presents an innovative and effective pattern discovery technique which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Substantial experiments on RCV1 data collection and TREC topics demonstrate that the proposed solution achieves encouraging performance.

Natural language question‐and‐answering is one of the most convenient means for communicating with the Semantic Web, which is typically in the form of online knowledge bases encoded in Web Ontology Language (OWL). To understand a natural language question, it is essential that it can be translated into a query that is understandable by the knowledge bases. This article is concerned with the task of semantic mapping from natural language questions to OWL queries, and proposes an automatic and domain‐independent mapping framework, called Three‐Phases Semantic Mapping (TPSM). The TPSM framework approaches the task of semantic mapping in three interrelated phases: (i) formalizing knowledge, (ii) building semantic mapping, and (iii) combining OWL queries. First, formalizing knowledge formalizes the units of mapping in the natural language and OWL knowledge. Second, semantic mapping builds the transverse mapping between questions and OWL knowledge, as formalized in the first phase, by means of working with the so‐called Fuzzy Constraint Satisfaction Problems (FCSP). Third, combining OWL queries obtains valid Resource Description Framework (RDF) models by applying predefined templates and their corresponding combining methods. We have implemented a prototype semantic mapping system based on the framework, and have conducted a series of experimental validations involving OWL knowledge bases in different domains as queried by various types of questions.

As a model for knowledge description and formalization, ontologies are widely used to represent user profiles in personalized web information gathering. However, when representing user profiles, many models have utilized only knowledge from either a global knowledge base or a user local information. In this paper, a personalized ontology model is proposed for knowledge representation and reasoning over user profiles. This model learns ontological user profiles from both a world knowledge base and user local instance repositories. The ontology model is evaluated by comparing it against benchmark models in web information gathering. The results show that this ontology model is successful.

Many communication systems, e.g., internet, can be modeled as complex networks. For such networks, immunization strategies are necessary for preventing malicious attacks or viruses being percolated from a node to its neighboring nodes following their connectivities. In recent years, various immunization strategies have been proposed and demonstrated, most of which rest on the assumptions that the strategies can be executed in a centralized manner and/or that the complex network at hand is reasonably stable (its topology will not change overtime). In other words, it would be difficult to apply them in a decentralized network environment, as often found in the real world. In this paper, we propose a decentralized and scalable immunization strategy based on a self-organized computing approach called autonomy-oriented computing (AOC) [1], [2]. In this strategy, autonomous behavior-based entities are deployed in a decentralized network, and are capable of collectively finding those nodes with high degrees of conductivities (i.e., those that can readily spread viruses). Through experiments involving both synthetic and real-world networks, we demonstrate that this strategy can effectively and efficiently locate highly-connected nodes in decentralized complex network environments of various topologies, and it is also scalable in handling large-scale decentralized networks. We have compared our strategy with some of the well-known strategies, including acquaintance and covering strategies on both synthetic and real-world networks.

Spontaneous brain activity studies have revealed `small-world' property in functional networks based on correlated or positively-correlated relationships. However, studies neither investigated negatively-correlated functional networks, nor checked the `dynamic' properties of the whole functional organization. After subjects performed a specific task, what changes will be caused in the intrinsic organization? In this study, we examined pre-task and post-task resting brains using functional MRI (fMRI). Then we suggested two perspectives with positively-correlated brain functional network (PCBFN) and negatively-correlated brain functional network (NCBFN), by testing whether correlation coefficients were positive or negative. The major findings were: 1) the PCBFN was with small-world architecture but the NCBFN was not, with different motifs, and the PCBFN showed stronger small-world effect at the post-task resting state; 2) Both PCBFN and NCBFN followed an exponentially truncated power law degree distribution. This study may offer a framework to investigate the intrinsic organization of spontaneous brain activity.

Aiming at the characteristics of human thinking centric studies, Brain Informatics (BI) adopts a multi-aspect data analysis approach for realizing systematic human brain data analysis. However, the existing expert-driven and individual capabilities based approach cannot realize a holistic multi-aspect brain data analysis because of the limitation of individual capabilities of researchers. In this paper, we propose a Data-Brain driven approach for multi-aspect mining process planning, by which various brain data and analytical methods in BI community can be dynamically integrated to get all of the possible mining workflows. This Data-Brain driven approach can reduce the dependency on individual capabilities and guide the holistic multi-aspect data analysis.

It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of the large number of terms, patterns, and noise. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern-based methods should perform better than term-based ones in describing user preferences, but many experiments do not support this hypothesis. The innovative technique presented in paper makes a breakthrough for this difficulty. This technique discovers both positive and negative patterns in text documents as higher level features in order to accurately weight low-level features (terms) based on their specificity and their distributions in the higher level features. Substantial experiments using this technique on Reuters Corpus Volume 1 and TREC topics show that the proposed approach significantly outperforms both the state-of-the-art term-based methods underpinned by Okapi BM25, Rocchio or Support Vector Machine and pattern based methods on precision, recall and F measures.

Collaborative Web search improves search quality by users' working in cooperation and is a subset of social search. Current Web browsers and search engines provide limited support for it. However, it is easier for experts, who are familiar with some topics, to fulfill they needs through search engines due to their backgrounds, domain knowledge and so on. A sharing experts' experiences approach should be struck based on today's Web browsers and major search engines. This paper presents a convenient way for users to share and utilize experts' experiences through a Web browser toolbar for collaborative Web search. The toolbar an catch search histories and favorites and display recommendations for every user in a popular Web browser through integrating with mainstream search engines like Google, Yahoo!, et al. These collected users' data are uploaded to a recommendation server, in which recommendations are built according to some rules based on an utilizing experts' experiences approach. The toolbar can download some valuable recommendations merging into default search list for prompting a searcher. The core of our proposed approach is a scalable method to measure "to what degree a user is an expert" for a given topic and to detect an expert's experiences based on a hierarchical user profile. Experiments showed that the novel collaborative Web search way is acceptant to users and experts' experiences improved search quality when compared to standard Google rankings. More importantly, results verified our hypothesis that a significant improvement on search quality can be achieved by utilizing experts' experiences.

For most scientists, their research interests are dynamically changing all the time. Through an analysis of research interests, we find that all the changes are with some characteristics. Plus, the research interests in the dynamic changing process are not isolated, instead, they are interconnected as a whole to form a holistic structure. We introduce some measurement parameters to track and detect the evolution process, we analyze the structural and dynamic characteristics of research interests through statistical analysis, and we also investigate on how they affect each other. As a possible application, we use observed characteristics of research interests to refine literature search on the Web, which shows that diverse user needs can be satisfied using various observations from research interests as constraints for vague queries. Such effort may provide some hints and various methods to support personalized search, and can be considered as a step forward user centric knowledge retrieval on the Web.

In recent years, immunization strategies have been developed for stopping epidemics in complex-network-like environments. Yet it still remains a challenge for existing strategies to deal with dynamically-evolving networks that contain community structures, though they are ubiquitous in the real world. In this paper, we examine the performances of an autonomy-oriented distributed search strategy for tackling such networks. The strategy is based on the ideas of self-organization and positive feedback from Autonomy-Oriented Computing (AOC). Our experimental results have shown that autonomous entities in this strategy can collectively find and immunize most highly-connected nodes in a dynamic, community-based network within a few steps.

No abstract available.

Nowadays, how to gather useful and meaningful information from the Web has become challenging to all users because of the explosion in the amount of Web information. However, the mainstream of Web information gathering techniques has many drawbacks,

Peculiarity-oriented mining (POM) is a new data mining method consisting of peculiar data identification and peculiar data analysis. Peculiarity factor (PF) and local peculiarity factor (LPF) are important concepts employed to describe the peculiarity of points in the identification step. One can study the notions at both attribute and record levels. In this paper, a new record LPF called distance based record LPF (D-record LPF) is proposed, which is defined as the sum of distances between a point and its nearest neighbors. It is proved mathematically that D-record LPF can characterize accurately the probability density function of a continuous m-dimensional distribution. This provides a theoretical basis for some existing distance based anomaly detection techniques. More important, it also provides an effective method for describing the class conditional probabilities in the Bayesian classifier. The result enables us to apply peculiarity analysis for classification problems. A novel algorithm called LPF-Bayes classifier and its kernelized implementation are presented, which have some connection to the Bayesian classifier. Experimental results on several benchmark data sets demonstrate that the proposed classifiers are effective.

A Search Support Engine (SSE) is implemented based on the basic principles of Information Retrieval Support Systems (IRSS) and Information Seeking Support Systems (ISSS). An SSE aims at meeting the diversity needs from different users, providing various supporting functionalities, tools, etc. for users to perform various tasks beyond the traditional search and browsing provided by current search engines. As an illustrative example, we developed a DBLP search support engine (DBLP-SSE), and we discuss some concrete supporting functionalities, namely, search refinement support, domain analysis support, etc. Each of the functionality focus on a unique perspective supporting users finding useful information and knowledge from the DBLP dataset. The search support engine can be considered as a step towards Knowledge Retrieval (KR) and Web Intelligence (WI).

Although they have small-world and searchable characteristics, social networks always protect local structure which makes local search very hard. Social networks not only have power-law distribution, but also have higher connectivity or clustering characteristics. Meanwhile, several social networks are weighted and directed. Hence we should utilize these extra information. In this paper, we propose and theoretically analyze local search strategies such as SS, O1, O2, SPD and LPD which consider weights, single link characteristics and direction information. We finally demonstrate the results of these strategies on Enron Email networks. Experimental results show that weighted and directed information is able to enhance the efficiency of local search.

Peculiarity oriented mining (POM), aiming to discover peculiarity rules hidden in a dataset, is a new data mining method. In the past few years, many results and applications on POM have been reported. However, there is still a lack of theoretical analysis. In this paper, we prove that the peculiarity factor (PF), one of the most important concepts in POM, can accurately characterize the peculiarity of data with respect to the probability density function of a normal distribution, but is unsuitable for more general distributions. Thus, we propose the concept of local peculiarity factor (LPF). It is proved that the LPF has the same ability as the PF for a normal distribution and is the so-called µ-sensitive peculiarity description for general distributions. To demonstrate the effectiveness of the LPF, we apply it to outlier detection problems and give a new outlier detection algorithm called LPF-Outlier. Experimental results show that LPF-Outlier is an effective outlier detection algorithm.

This paper presents a case study on data-brain construction based on brain informatics (BI) methodology. The data-brain is a conceptual brain data model, which represents functional relationships among multiple human brain data sources, with respect to all major aspects and capabilities of human information processing system for systematic investigation and understanding of human intelligence. On one hand, developing such a data-brain is a core research issue in BI. On the other hand, BI methodology supports such a data-brain construction. A graphical modeling language, the view-fact-dimension-model (VFDM), is proposed. Using the VFDM, we can take part in the data-brain construction easily, which provides a long-term, holistic vision to understand the principle and mechanisms of human information processing system.

Web software systems provide information and service for end users through the Web interface. The interactive process between the user and the software is the process of software systems to perceive the environment and user, to adjust own configuration and provide appropriate services, monitor and eliminate untrustworthy factors, complete own evolvement and achieve trusted result finally. This paper aims to establishing the online trust evolution model and then building the corresponding trustworthy software framework during the human-Web interactive process. Our research will consider the human and software factors together and combine related methodologies and tools for developing trust model in a user-centric way. Our study can provide theoretical and technical support for developing trustworthy Web software systems.

Retrieving accurate information from the Web is a great challenge to users. The existing information retrieval systems are mostly term-based and thus need to be enhanced toward knowledge-based. User information needs need to be better captured in order to deliver personalized search results. In this paper, an ontology-based framework is proposed for capturing user information needs using a world knowledge base and the user's local instance repository. The framework aims to discover a user's background knowledge for knowledge retrieval. The evaluation result is encouraging, in which the proposed model achieved the same performance as a manual user model.

The goal of an on-line ontology-based question-answering system is to automatically derive answers from ontology knowledge bases without demanding additional information or intervention from users. This paper focuses on the problem of automatically mapping the tokens of a question into OWL elements, as an important step towards the further construction of answers. This problem can be essentially viewed as that of question understanding. The basic ideas underlying our method can be stated as follows: first we translate the tokens of a question as well as their syntactical and semantic relations (as in NLP) into constrained question variables and functions, and thereafter, we utilize an optimization-based assigning mechanism to substitute the question variables with the corresponding constructs in OWL knowledge bases. In the paper, we discuss our preliminary studies using the questions collected from, and the knowledge base built at, the International WIC Institute (WICI).

Although some preliminary spatial localization results have been reported, the temporal characteristics of human inductive reasoning process have not been investigated. In the present study, event-related potential (ERP) was used to explore the time course of inductive reasoning process. Based on pilot studies and some other related research, we hypothesized that the process of numerical inductive reasoning is composed of number recognition, strategy formation, hypothesis generation and validation, and the above three stages are (partially) dissociable over time. A typical task of inductive reasoning, function-finding, was adopted. Induction tasks and calculation tasks were performed in the experiments, respectively. The mean reaction time of induction tasks was much longer than that of calculation tasks as expected. Statistical analysis revealed that induction showed no significant separations from calculation for the early ERP components and the slow waveforms after about 600ms, while marked dissociations appeared for the late components in the time window of about 250-600ms. It can be preliminarily concluded that, the early components before about 250ms may reflect the process of attention and number recognition, the late components may relate to strategy formation, and hypothesis generation and validation may be performed in about 600-1200ms. On the whole, the results on the behavioral data and ERP data support our hypothesis.

E-finance industry is rapidly transforming and evolving toward more dynamic, flexible and intelligent solutions. The proposed e-finance portal provides an integrated enterprise platform for retail banking services as well as for other financial services. In the meantime, the proposed multilevel solution will keep monitoring and analyzing the huge volume of dynamic information flowing through the portal. In this way, the portal is able to find the useful knowledge for refining business processes and providing personalized services, and detect hidden financial problems as well.

It is well accepted that ontology is useful for personalized Web information gathering. However, it is challenging to use semantic relations of "kind-of", "part-of", and "related-to" and synthesize commonsense and expert knowledge in a single computational model. In this paper, a personalized ontology model is proposed attempting to answer this challenge. A two-dimensional (Exhaustivity and Specificity) method is also presented to quantitatively analyze these semantic relations in a single framework. The proposals are successfully evaluated by applying the model to a Web information gathering system. The model is a significant contribution to personalized ontology engineering and concept-based Web information gathering in Web Intelligence.

With the ever-increasing growth of data and information, finding the right knowledge becomes a real challenge and an urgent task. Traditional data and information retrieval systems that support the current web are no longer adequate for knowledge seeking tasks. Knowledge retrieval systems will be the next generation of retrieval system serving those purposes. Basic issues of knowledge retrieval systems are examined and a conceptual framework of such systems is proposed. Theories and Technologies such as theory of knowledge, machine learning and knowledge discovery, psychology, logic and inference, linguistics, etc. are briefly mentioned for the implementation of knowledge retrieval systems. Two applications of knowledge retrieval in rough sets and biomedical domains are presented.

E-finance industry is rapidly transforming and evolving toward more dynamic, flexible and intelligent solutions. This paper describes a model with dynamic multilevel workflows corresponding to a multilayer Grid architecture. The mining-grid is used for multiaspect analysis in building e-finance portals on the Wisdom Web. The application and research demonstrate that mining-grid centric design is effective for developing intelligent risk management and decision making financial systems. This paper concentrates on how to develop a mining-grid centric e-finance portal (MGCFP), not only for supplying effective online financial services for both retail and corporate customers, but also for intelligent credit risk management and decision making for financial enterprises and partners.

As two related emerging fields of research, Web intelligence (WI) and brain informatics (BI) mutually support each other. Their synergy will yield profound advances in the analysis and understanding of data, knowledge, intelligence and wisdom, as well as their relationships, organization and creation process. When WI meets BI, it is possible to have a unified and holistic framework for the study of machine intelligence, human intelligence, and social intelligence. BI can be regarded brain sciences in IT age, which represents a potentially revolutionary shift in the way that research is undertaken. It emphasizes on a systematic approach for investigating human information processing mechanisms, including measuring, collecting, modeling, transforming, managing, and mining multiple human brain data obtained from various cognitive experiments by using fMRI and EEG. Multi-aspect analysis in multiple human brain data sources is an important methodology in BI. The proposed methodology attempts to change the perspective of cognitive/brain scientists from a single type of experimental data analysis towards a holistic view at a long-term, global field of vision to understand the principle, models and mechanisms of human information processing. New generations of WI research and development need to understand multi-nature of intelligence in depth. The recently designed instrumentation (fMRI etc.) and advanced IT are causing an impending revolution in both WI and BI, making it possible for us to understand and develop human-level Web intelligence

E-finance industry is rapidly transforming and evolving toward more dynamic, flexible and intelligent solutions. This paper describes a model with dynamic multi-level workflows corresponding to multi-layer grid architecture, for multi-aspect analysis in building e-finance portals on the Wisdom Web. The application and research demonstrate that mining-grid centric three-layer grid architecture is effective for developing intelligent risk management and decision making financial systems

Ant colony optimization (ACO) algorithm has been applied to data mining recently. Aiming at Ant Miner, a classification rule learning algorithm based on ACO, this paper presents an enhanced Ant Miner, which includes two main contributions. Firstly, a rule punishing operator is employed to reduce the number of rules and the number of conditions. Secondly, an adaptive state transition rule and a mutation operator are applied to the algorithm to speed up the convergence rate. The results of experiments on some data sets demonstrate that the enhanced Ant-Miner can quickly discover better classification rules which have roughly competitive predicative accuracy and short rules

It is a big challenge to apply data mining techniques for effective Web information gathering because of duplications and ambiguities of data values (e.g., terms). To provide an effective solution to this challenge, this paper first explains the relationship between association rules and rough set based decision rules. It proves that a decision pattern is a kind of closed pattern. It also presents a novel concept of rough association rules in order to improve the effectiveness of association rule mining. The premise of a rough association rule consists of a set of terms and a frequency distribution of terms. The distinct advantage of rough association rules is that they contain more specific information than normal association rules. It is also feasible to update rough association rules dynamically to produce effective results

Recently, research on social network and Web intelligence (WI) has shown that social intelligence techniques act as the imperative channel for automated email-centric tasks. This paper gives a complete picture of what can we do in the global social e-mail network and how to do. Our main contributions include: (1) we describe two mechanisms for implementing applications in the global social e-mail network; (2) we design operable e-mail for communicating in that network. To our best knowledge, it is the first time to discuss how to implement social intelligence in such a network under the notion of WI. We believe that this work consequentially explores a new and absolutely necessarily needed research field of WI

The traditional techniques rely on human effort to acquire training sets, which is expensive and inefficient. In this paper we present an alternative method to automatically acquire training sets without heavy investment of user efforts. The proposed method tends to fill a gap for effectiveness of using Web data in Web mining, and contributes to Web information gathering. The evaluation shows that the method is adequate to yield an promising achievement.

Collecting customer interaction data on the e-business Web sites and portals help to figure out customer behavior and build customer profile, and then perform personalized services. Traditional Web server log is hard to be associated with specific customer and impossible to log the complete actions and movements of customers across Web sites. Collecting clickstream log at the application layer with Web farming technology helps to seamlessly integrate Web usage data with other customer related data. This model can be developed as a common plugin for most existing e-business Web sites and portals.

The quality of discovery patterns is crucial for building satisfactory systems of Web text mining. It is no doubt that we can find numerous frequent patterns from Web documents. However, there are many meaningless frequent patterns. This paper presents a novel method to improve the quality of discovered patterns. It generalizes discovered patterns into interesting topics in order to acquire the necessary useful information. The experimental results also verify the proposed method is promising.

One of the key research challenges that we will face in developing the Wisdom Web is to make it capable of seamlessly offering solutions to users in dealing with their real-world problems. In order to make this possible, individual contents or services should be developed and written following the syntax and semantics of a pre-defined method for distributed problem solving on the Web. In this paper, we propose a new method for solving problems in a large-scale distributed Web environment. We will also give an illustrative example in this paper to show the service process of the new method.

Web personalization is one of the major concerns of Web intelligence. It is noticed that the two components of Web, users and services, can be understood from multiple views in forms of hierarchies, and each hierarchy is organized by a multilevel structure. A unified model is proposed for including all personalization styles. The unified model with multilevel hierarchy structures, add new understandings and insights into the Web personalization issues. More concise and precise recommendations can then be studied and pursued based on this multilevel model.

E‐science is about global collaboration in key areas of science such as cognitive science and brain science, and the next generation of infrastructure such as the Wisdom Web and Knowledge Grids. As a case study, we investigate human multiperception mechanism by cooperatively using various psychological experiments, physiological measurements, and data mining techniques for developing artificial systems which match human ability in specific aspects. In particular, we observe fMRI (functional magnetic resonance imaging) and EEG (electroencephalogram) brain activations from the viewpoint of peculiarity oriented mining and propose a way of peculiarity oriented mining for knowledge discovery in multiple human brain data. Based on such experience and needs, we concentrate on the architectural aspect of a brain‐informatics portal from the perspective of the Wisdom Web and Knowledge Grids. We describe how to build a data‐mining grid on the Wisdom Web for multiaspect human brain data analysis. The proposed methodology attempts to change the perspective of cognitive scientists from a single type of experimental data analysis toward a holistic view at a long‐term, global field of vision.

Personalized recommendation needs powerful Web Intelligence (WI) technologies to manage, analyze and employ various business data on the Web for e-business intelligence. This paper presents a novel recommendation framework on the Web, which is based on a multilevel customer model comprising three submodels, namely, the customer shopping model (CSM), the customer preference model (CPM), and the customer consumption model (CCM). These models capture a customer's information from different aspects. After preprocessing of raw data, we first build the CSM based on Bayesian networks by mining from customer shopping transactions, and then find the CPM by analyzing customer shopping history. Furthermore, the customer purchasing power can be formalized as a linear CCM. By combining the CSM with the present customer shopping action, a recommendation algorithm based on Bayesian probability inference is used to generate an individual recommendation set of commodities. A personalized filter including customization of the CPM and orientation of the CCM is also used to realize a more personalized recommendation. Experimental evaluation on real world data shows that the proposed approach can achieve personalized commodities recommendation efficiently and effectively.

A continuing problem with inductive logic programming (ILP) has been the poor handling of numbers. Constraint inductive logic programming (CILP) aims to solve this problem with ILP. We propose a new approach to generating numerical constraints in CILP, and describe an implementation of the CILP system (namely, BPU-CILP). In our approach, methods from pattern recognition and multivariate data analysis, such as Fisher's linear discriminant, dynamic clustering and principal component analysis, are introduced into CILP. The BPU-CILP can generate various forms of polynomial constraints of multiple dimensions, without additional background knowledge. As a result, the constraint logic program covering all positive examples and consistent with all negative examples can be derived automatically.

Peculiarity rules are a new type of interesting rules which can be discovered by searching the relevance among peculiar data. A main task of mining peculiarity rules is the identification of peculiarity. Traditional methods of finding peculiar data are attribute-based approaches. This paper extends peculiarity oriented mining to relational peculiarity oriented mining. Peculiar data are identified on record level, and peculiar rules are mined and explained in a relational mining framework. The results from preliminary experiments show that relational peculiarity oriented mining is very effective.

Developing an intelligent e-Business portal needs various WI technologies. In this demonstration, we present a conceptual model with dynamic multi-level workflows corresponding to a multi-layer Grid architecture for multi-aspect analysis in building an e-Business portal on the Wisdom Web, and for dynamically organizing status-based business processes that governs enterprise application integration.

TMS (Targeted Marketing System) is an integrated system and toolkit for profit-driven and cost-effective marketing. The system consists of three components: a Web-based user interface, a market value inference engine, and a presentation and evaluation module. It supports marketing decision making for a company or an organization by combining results from information retrieval, data mining, information theory, and utility theory.

Web Intelligence (WI) is a new and active research field in current AI and IT. Personalized recommendation in an intelligent B2C portal is an important research topic in WI. In this paper, we first investigate the architecture of a B2C portal from the viewpoint of conceptual levels of WI. Aiming at data mining of knowledge-level in a B2C portal, we present a new improved learning algorithm of Bayesian Networks, which consists of two major contributions, namely, making the best of lower order Conditional Independence (CI) tests and accelerating search process by means of sort order for parent nodes. By a number of experiments on ALARM datasets, we find that the proposed algorithm is both more efficient and effective than others. We have applied this algorithm to a commodity recommendation system in a B2C portal. Our experimental results demonstrate that the recommendation method based on a Customer Shopping Model (CSM) produced by the new algorithm outperforms some traditional ones in rates of coverage and precision.

An ontology-based Web mining model tends to extract an ontology from user feedback and use it to search the right data from the Web to answer what users want. It is indubitable that we can obtain numerous discovered patterns using a Web mining model. However, some discovered patterns might include uncertainties when we extract them. Also user profiles are changeable. Therefore, the difficult issue is how to use and maintain the discovered patterns. This paper presents a theoretical framework for this issue, which consists of automatic ontology extraction, reasoning on the ontology and capturing evolving patterns. The experimental results show that all objectives we expect for the theoretical framework are achievable.

The objective of the WIC Japan Research Centre is to carry out basic research concerning certain aspects of Web Intelligence (WI). Our research activities focus on investigating WI technologies for developing various portals that enable intelligence for e-science, e-business, e-government, and e-learning, as well as deal with the scalability and complexity of real world, efficiently and effectively. We observe that developing intelligent portals is one of the most sophisticated applications, which needs to be supported by WI technologies. Research work that has been carried out can be categorized and described as follows.

We present interpretations for association rules. We first introduce Pawlak's method, and the corresponding algorithm of finding decision rules (a kind of association rules). We then use extended random sets to present a new algorithm of finding interesting rules. We prove that the new algorithm is faster than Pawlak's algorithm. The extended random sets are easily to include more than one criterion for determining interesting rules. We also provide two measures for dealing with uncertainties in association rules.

The linear model of market value functions is a new method for direct marketing. Just like other methods in direct marketing, attribute reduction is very important to deal with large databases. We apply the algorithm of attribute reduction, which is based on the combination of rough set theory with the boosting algorithm, to the linear model of market value functions. Experimental results compared with the ELSA/ANN model show that the proposed algorithms can be used effectively in the linear model of market value functions.

As e-commerce developing rapidly, it is becoming a research focus about how to capture or find customer's behavior patterns and realize commerce intelligence by use of Web mining technology. Recommendation system in electronic commerce is one of the successful applications that are based on such mechanism. We present a new framework in recommendation system by finding customer model from business data. This framework formalizes the recommending process as knowledge representation of the customer shopping information and uncertainty knowledge inference process. In our approach, we firstly build a customer model based on Bayesian network by learning from customer shopping history data, then we present a recommendation algorithm based on probability inference in combination with the last shopping action of the customer, which can effectively and in real time generate a recommendation set of commodity.

Web mining is used to search the right information from the Web to meet user information needs. Acquiring correct user profiles is difficult, since users may be unsure of their interests and may not wish to invest a great deal of effort in creating such a profile. Our aim is to present a foundation for representations of user profiles on ontology for designing efficient Web mining models. We assume the user concept can be constructed from some primary ones; hence, we use "part-of" relation to describe the relationships between classes. We also present set-valued relevance functions on such ontology to unravel the relationships between facts and the existing classes. A numerical interpretation is also presented for the set-valued relation functions.

Peculiarity rules are a new class of rules which can be discovered by searching relevance among a relatively small number of peculiar data. Peculiarity oriented mining in multiple data sources is different from, and complementary to, existing approaches for discovering new, surprising, and interesting patterns hidden in data. A theoretical framework for peculiarity oriented mining is presented. Within the proposed framework, we give a formal interpretation and comparison of three classes of rules, namely, association rules, exception rules, and peculiarity rules, as well as describe how to mine interesting peculiarity rules in multiple databases.

Targeted marketing typically involves the identification of customers or products having potential market values. We propose a linear model for solving this problem by drawing and extending results from information retrieval. It is assumed that each object is represented by values of a finite set of attributes. A market value function, which is a linear combination of utility functions on attribute values, is used to rank objects. Several methods are examined for mining market value functions. The main advantage of the model is that one can rank objects of interest according to their market values, instead of classifying the objects. Both the theoretical and experimental results are reported in this paper. It establishes a basis on which further studies and experimental evaluation can be carried out.

Targeted marketing typically involves the identification of customers or products having potential market values. We propose a linear model for solving this problem by drawing and extending results from information retrieval. It is assumed that each object is represented by values of a finite set of attributes. A market value function, which is a linear combination of utility functions on attribute values, is used to rank objects. Several methods are examined for mining market value functions. The main advantage of the model is that one can rank objects. of interest according to their market values, instead of classifying the objects. The theoretical results reported in this paper establish a basis on which further studies and experimental evaluation can be carried out.

Many real-world problems deal with ordering objects instead of classifying objects, although the majority of the research in machine learning and data mining has been focused on the latter. For the modeling of ordering problems, we generalize the notion of information tables to ordered information tables by adding order relations on attribute values. The problem of mining ordering rules is formulated as finding associations between the orderings of attribute values and the overall ordering of objects. An ordering rule may state, for example, that "if the value of an object x on an attribute a is ordered ahead of the value of another object y on the same attribute, then x is ordered ahead of y". For mining ordering rules, we first transform an ordered information table into binary information, and then apply any standard machine learning and data mining algorithms. As an illustration, we analyze in detail the Maclean's university ranking for the year 2000.

In order to discover new, surprising, interesting patterns hidden in data, peculiarity oriented mining and multidatabase mining are required. In the paper, we introduce peculiarity rules as a new class of rules, which can be discovered from a relatively low number of peculiar data by searching the relevance among the peculiar data. We give a formal interpretation and comparison of three classes of rules: association rules, exception rules, and peculiarity rules, as well as describe how to mine more interesting peculiarity rules in multiple databases.

This paper applies rough set theory to Inductive Logic Programming (ILP, a relatively new method in machine learning) to deal with imperfect data occurring in large real‐world applications. We investigate various kinds of imperfect data in ILP and propose rough problem settings to deal with incomplete background knowledge (where essential predicates/clauses are missing), indiscernible data (where some examples belong to both sets of positive and negative training examples), missing classification (where some examples are unclassified) and too strong declarative bias (hence the failure in searching for solutions). The rough problem settings relax the strict requirements in the standard normal problem setting for ILP, so that rough but useful hypotheses can be induced from imperfect data. We give simple measures of learning quality for the rough problem settings. For other kinds of imperfect data (noise data, too sparse data, missing values, real‐valued data, etc.), while referring to their traditional handling techniques, we also point out the possibility of new methods based on rough set theory.

How to increase both autonomy and versatility of a knowledge discovery system is a core problem and a crucial aspect of KDD (Knowledge Discovery and Data Mining). Within the framework of the KDD process and the GLS (Global Learning Scheme) system recently proposed by us, this paper describes a way of increasing both autonomy and versatility of a KDD system by dynamically organizing KDD processes. In our approach, the KDD process is modeled as an organized society of KDD agents with multiple levels. We propose an ontology to describe KDD agents, in the style of OOER (Object Oriented Entity Relationship) data model. Based on this ontology of KDD agents, we apply several AI planning techniques, which are implemented as a meta-agent, so that we might (1) solve the most difficult problem in a multiagent KDD system: how to automatically choose appropriate KDD techniques (KDD agents) to achieve a particular discovery goal in a particular application domain; (2) tackle the complexity of KDD process; and (3) support evolution of KDD data, knowledge and process. The GLS system, as a multistrategy and multiagent KDD system based on the methodology, increases both autonomy and versatility.

The 21st century is the age of the Internet and the World Wide Web. The Web revolutionizes the way we gather, process, and use information. At the same time, it also redefines the meanings and processes of business, commerce, marketing, finance, publishing, education, research, development, as well as other aspects of our daily life. The revolution is just beginning. Although individual Web based information systems are constantly being deployed, advanced issues and techniques for developing and for benefiting from Web intelligence still remain to be systematically studied. The article defines a new research field, namely Web Intelligence (WI) by giving a complete picture of WI related topics for systematic study on advanced Web technology and developing Web based intelligent information systems. Roughly speaking, WI exploits AI and advanced information technology on the Web and Internet. It is the key and the most urgent research field of IT for business intelligence.

We propose a new information retriever on the Web which: automatically classifies collected documents; and retrieves multi-lingual information (e.g., Japanese and/or English). This is attained by the mechanism of ontology processing. We set up a multi-lingual ontology as an index dictionary. The ontology manages the relations of keywords and the weights of keywords for domain categories to classify the documents. The system has the following features: users can use any keywords expressed in their native language to search for the Web documents, even if they contain multi lingual information; and the documents can be classified into specific domain categories by tuning the keyword weights up.

In our GLS (global learning scheme) system, KDD (knowledge discovery in databases) process is modeled as an organized society of intelligent agents (called KDD agents). KDD process planning is a meta-agent in this framework, and some basic AI planning techniques have been used to organize dynamically the KDD process so that the GLS system increases both autonomy and versatility. In this paper, we address some deeper issues concerning KDD process planning. Especially we propose a mechanism integrating process planning with process controlling to formalize process iteration (that is, the re-execution of some agents). In this mode, execution failures causing process iteration, feedback paths and re-execution of the agents in the paths (and even the iterating number for each loop) are determined dynamically and automatically. With this and other deeper issues (such as process evolution) being properly handled, the GLS system is more complete in KDD process modeling, and more flexible and robust for practical use.

The contents of most databases are ever changing; and erroneous data can be a significant problem in real-world databases. Hence, the process of discovering knowledge from databases is a process based on incipient hypothesis generation/evaluation and refinement/management. Although many systems for knowledge discovery in databases have been proposed, most systems have not addressed the capabilities of refining/managing the discovered knowledge. This paper describes a system called IIBR (inheritance inference based refinement) for managing/refining the knowledge discovered from databases. IIBR is one of subsystems of the GLS (global learning scheme) discovery system, and can be cooperatively used with other subsystems of GLS such as KOSI (knowledge-oriented statistical inference). By means of IIBR, the structural characteristics denoted by regression models, which are discovered from a database by KOSI, can be added to a knowledge base as deductive rules and data sets for showing the rule errors, and can be managed and refined easily. IIBR is based on inheritance inference and error analysis, as well as the model-based representation of knowledge in the knowledge-based system KAUS.>

Introduces an approach called HML (hierarchical model learning) for refining and managing knowledge discovered from databases. HML is implemented as one of subsystems of the GLS (Global Learning Scheme) discovery system developed by us for knowledge discovery in databases. It is used as a learning phase in multiple learning phases of GLS. By means of HML, concept clusters which are discovered from a database can be added to a knowledge base as classification knowledge with hierarchical models and can be easily refined/managed. HML is based on the model representation of multi-layer logic (MLL). A key feature of HML is the quantitative evaluation for selecting the best representation of the MLL formula by cooperatively using a criterion based on information theory and domain knowledge. Experience with a prototype of HML implemented by the knowledge-based system KAUS is discussed.