Cross-domain recommender systems (CDRSs) enhance recommendations by transferring knowledge of overlapping users across two domains. Deep canonical correlation analysis (DCCA) shows promising results in CDRSs by maximizing correlations between representations of overlapping users, enabling cross-domain knowledge transfer that depends on the degree of relationship between domains. As a result, DCCA selectively shares only relevant knowledge, alleviating the problem of noisy representation found in traditional CDRSs, where they transfer knowledge regardless of the correlation strength between domains. Although DCCA is used for user transfer, item transfer, referring to the transfer of explicit knowledge of the same items between domains, is impossible due to the absence of overlapping items to facilitate direct knowledge transfer. Meanwhile, graph neural networks (GNNs) embed users and items from separate user and item graphs in each domain. Therefore, better representations are obtained from captured complex relationships and collaborative signals. To construct graphs of overlapping items, latent linkages among items between domains could be discovered by the neural topic model (NTM), forming new graphs representing the latent relationships. Therefore, COLANet, a GNN-based CDRS, is proposed to solve the DCCA limitation on item transfer by proposing the extraction of item representations that do not exist in another domain using latent characteristics. First, user-user graphs are constructed using user similarity, and the item-topic graph is constructed using latent topics learned from item descriptions with NTM. Hence, user and item graphs of each domain are constructed separately, preventing domain relationship misalignment. Second, these graphs are fed to GNN to obtain user and item representations. Third, these representations are fed to DCCA to transfer knowledge between user-user and item-item. Finally, correlated user and item representations of each domain are used to predict ratings. The experiments demonstrate that COLANet outperforms the baselines across four pairs of domains, including both similar and different domains.

Many recommender systems leverage Graph Neural Networks to capture user–item relations for delivering recommendations. However, the representation of nodes heavily relies on neighbors, causing limited neighbor nodes (sparse nodes) to lack expressive representation. Existing works discovered latent neighbors for sparse nodes but ignored node sparsity, resulting in the neighbor misallocation problem due to overlooking quality and quantity aspects. We propose discovering high-quality latent neighbors by progressively transferring knowledge from dense nodes by categorizing user–item interactions into four quadrants (dense user–dense item, dense user–sparse item, sparse user–dense item, and sparse user–sparse item). We leverage the node sparsity to determine the optimal quantity of latent neighbors. We propose a Domain Adaptation Network for transferring knowledge from dense to sparse quadrants without encountering the domain misalignment problem arising from the distinct representations between dense and sparse quadrants. An Enrichment Network is proposed to address the inexpressive representation problem due to limited observed interactions by enriching the sparse node representation. A Heterogeneous Graph Neural Network architecture is proposed to capture multiple relations between dense/sparse users and items. Experimental results on three benchmark datasets demonstrate the superiority of the proposed method over Graph Neural Network baselines, both with and without latent neighbors.

Tables are often used to summarize accurate values in academic papers, while graphs are used to show them visually. Automatic graph generation from a table is therefore a topic of research interest. Given that the way tables are written varies depending on the author, in earlier work we proposed a cell-detection-based table-structure recognition method. Our method achieved fair performance in experiments using the ICDAR 2013 table competition dataset, but could not outperform the top-ranked participant in the competition. This paper proposes an improved method using two neural networks: one estimates implicit ruled lines that are necessary to separate cells but are undrawn, and the other estimates cells by merging detected tokens in a table. We demonstrated the effectiveness of the proposed method by experiments using the same ICDAR 2013 dataset. It achieved an F-measure of 0.955, thereby outperforming the other methods including the top-ranked participant.

If tables are automatically recognized to extract the numerical values in them, digital documents containing such tables can be augmented with graphs generated using the recognized tables. In this paper, we propose a cell-detection-based table-structure recognition method for such automatic graph generation from tables. In detecting cells in a table, ruled lines are crucial but do not necessarily surround all cells. We therefore propose a method to detect cells by estimating implicit ruled lines, where necessary, to recognize the table structure. We demonstrate the effectiveness of the proposed method by experiments using the ICDAR 2013 table competition dataset.

Many context-aware recommendation methods extract contexts from reviews using supervised methods. However, this requires the optimal values for contexts to be predefined, which is not a trivial task. Although some approaches have avoided this by utilizing unsupervised methods, the extracted contexts have been limited to a unigram format. Moreover, most methods consider only the influence of context on the entire dataset, ignoring the fact that context might be relevant to individual users or items unequally. This work proposes a novel unsupervised context extraction method that uses predictive models for future ratings. Unlike previous work, we extract context from reviews automatically in the form of skip-grams by applying a region embedding technique. The predictive models utilize the interaction between contexts and users (and items) to model their influence on ratings. Experiments demonstrate that our models can outperform existing review-based recommendations that ignore contexts.

Ratings, which are explicit feedback, are the most popular form that is often used in Recommender System (RSs). However, using the actual ratings from neighbors to predict ratings of target user toward target item often leads to low accuracy prediction due to the improper rating range problem. Rating conversion methods are proposed to solve this problem over the past few years. To propose rating conversion method, each user’s preference or rating pattern is needed. Some studies adopt the idea from translation-based embedding model and represent user’s preference in graph form. Although some studies represent users, items, and relations in embedding vector form, their representation may be improper and inaccurate if the rating pattern of each user is not in the same range. These vectors still suffer from the improper rating range as well. In this work, we propose a translation-based embedding model with rating conversion in RSs. We aim to solve the improper rating range problem in translation-based embedding model. Our challenges are 1) representing the relation (rating) between a pair of user and item in vector form, instead of scalar form and 2) dealing with rating conversion of user’s rating in vector form. The FilmTrust and MovieLens dataset are used in experiments comparing the proposed method with the existing methods. The evaluation showed that the proposed rating conversion method provides better accuracy results in term of both rating prediction and ranking recommendation.

We study an efficiently distributed index structure and parallel processing methods for a top-k frequent spatiotemporal terms query, a basic analytic query on geo-tagged social data. The key challenge is to improve query performance on huge geo-tagged social datasets with minimum storage requirement while guaranteeing query accuracy. We propose a method of distributing the data across a cluster and parallel algorithms to process the aggregating computation. Experimental results on real datasets showed improvement with our proposed methods in both space requirement and query performance compared with baselines.

The Internet of Things (IoT) - sensors and actuators connected via internet infrastructure to computing systems - has received enormous research attention. It has found applications in nearly every field, especially, in transportation where bridge monitoring systems are well known examples. This paper proposes a vehicle detection method from acceleration signals acquired from sensors of the bridge monitoring systems. The accelerometers based on micro-electro-mechanical system (MEMs) technology, are sensitive to environmental disturbances such as shock or temperature changes, so that the generated signal is contaminated by various types of noise. Our method first performs noise reduction, retaining only signal components that represent real measurements of forces induced by passing vehicles. The signal is then processed in the second phase using a wavelet-based pattern matching technique. Experimental results on real-world data demonstrate the high performance of both phases of the proposed method.

Popular search engines have recently utilized the power of knowledge graphs (KGs) to provide specific answers to queries in a direct way. Search engine result pages (SERPs) are expected to provide facts in response to queries that satisfy semantic meaning. This encourages researchers to propose more influential knowledge graph generation techniques. To achieve and advance the technologies related to actionable knowledge graph presentation, creating action recommendations (ARs) is an essential step and a relatively new research direction to nurture research on generating KGs that are optimized for facilitating an entity's actions. An action represents the physical or mental activity of an entity. For example, for the entity "Donald J. Trump", typical potential actions could be "won the US presidential election" or "targets US journalists". In this paper, we describe the generation of relevant action recommendations based on entity instance and entity type. We propose two models that employ different approaches. Our first model exploits semisupervised learning and we introduce entity context vector (ECV) as an entity's distinguishing features for capturing the context of entities to reveal the similarity between entities, grounded on the prominent word2vec model. The second model is a probabilistic approach based on the Naive Bayes Theorem. We extensively evaluate our proposed models. Our first model significantly outperforms probabilistic and supervised learning-based models.

Frequent string mining is widely used in text processing to extract text features. Most researchers have focused on text using single-byte characters. Consequently, their applications have problems when applied to text represented with multibyte characters such as Japanese and Chinese text. The main drawback is huge memory us-age for treating multibyte character strings. To solve this problem,we use wavelet tree-based compressed suffix arrays instead of the normal suffix array to reduce the memory usage, and a novel technique that utilizes the rank operation to improve runtime efficiency.Our experimental evaluation shows that the proposed method reduces the processing time by 45% compared with a method usingonly compressed suffix arrays. The proposed method also reduces the memory usage by 75%.

When new researchers read scholarly papers, they often encounter unfamiliar technical terms, which may require considerable time to investigate. We have been developing a user interface to support the browsing of scholarly papers, which can provide useful links to information about such technical terms. The interface displays "important terms" extracted from a paper on top of the image of the paper. In this study, we organize the important terms extracted from papers by using author keywords. We first identify the important terms and then associate them with author keywords by using a method based on the word2vec model. Experiments showed that our method improved the classification accuracy of important terms compared with a simple baseline method. It associated each author keyword with about 2.5 relevant important terms.

In this paper, we have extended the concept of unordered tree inclusion to take the costs of insertions and substitutions into account. The resulting algorithm, MinCostIncl, has the same time complexity as the original algorithm of [4] for unordered tree inclusion (O(22Dmn)). Computational experiments on a large synthetic dataset as well as real datasets showed that our proposed algorithm is fast and scalable. Source codes of the implemented algorithms are available upon request.

Collaborative filtering is a recommender technique that recommends items to an individual user based on the item ratings provided by similar users. However, current systems often do not acquire sufficient ratings to be able to generate recommendations. Trust-based recommender systems have been proposed that use additional trust values in generating recommendations. In this paper, we propose a trust-based ant recommender with two main improvements. First, we achieve better selection of higher-quality raters by our proposed trust-calculation method and an improved pheromone-update mechanism. Second, we can improve the prediction step by converting raters' ratings into a target user's perspective view and considering the influence level of each rater on the active user. The Epinions dataset was used in experiments comparing the proposed method with the ALT-BAR method. The evaluation showed that the proposed method provides better results in term of both accuracy and coverage.

Context-aware recommender systems that provide better recommendations for users by using their rating history in different situations have been proposed. Because incorporating all contextual information can make the data sparser and degrade the prediction accuracy, most context-aware methods focus on detecting and using only the most effective contextual factors. However, in addition to accuracy, the diversity of the recommendation is also a key to improving users' satisfaction with recommendation results. Moreover, most context-aware techniques have not considered directly the relationships among context, users, and items before predicting the ratings. In the real world, different contextual factors tend to affect users and items differently. This paper proposes a latent probabilistic model to incorporate the contextual information. By adopting a binary particle-swarm optimization technique, the relevant contextual factors for user classes and item classes are identified and incorporated into the model. We optimize our model for two cases, namely considering accuracy alone and considering the trade-off between accuracy and diversity. An evaluation shows that our proposed model performs better than 1) a model that considers only the relation of context to users alone or items alone, 2) a model that exploits all contextual factors, and 3) the traditional context-aware recommendation method.

Traffic congestion occurs frequently in urban settings, and is not always caused by traffic incidents. In this paper, we propose a simple method for detecting traffic incidents from probe-car data by identifying unusual events that distinguish incidents from spontaneous congestion. First, we introduce a traffic state model based on a probabilistic topic model to describe the traffic states for a variety of roads. Formulas for estimating the model parameters are derived, so that the model of usual traffic can be learned using an expectation–maximization algorithm. Next, we propose several divergence functions to evaluate differences between the current and usual traffic states and streaming algorithms that detect high-divergence segments in real time. We conducted an experiment with data collected for the entire Shuto Expressway system in Tokyo during 2010 and 2011. The results showed that our method discriminates successfully between anomalous car trajectories and the more usual, slowly moving traffic patterns.

This paper considers the problem of identifying all locations of subtrees in a large tree or in a large collection of trees that are similar to a specified pattern tree, where all trees are assumed to be rooted and node-labeled. The tree edit distance is a widely-used measure of tree (dis-)similarity, but is NP-hard to compute for unordered trees. To cope with this issue, we propose a new similarity measure which extends the concept of unordered tree inclusion by taking the costs of insertion and substitution operations on the pattern tree into account, and present an algorithm for computing it. Our algorithm has the same time complexity as the original one for unordered tree inclusion, i.e., it runs in O(|T1∥T2|) time, where T1and T2denote the pattern tree and the text tree, respectively, when the maximum outdegree of T1is bounded by a constant. Our experimental evaluation using synthetic and real datasets confirms that the proposed algorithm is fast and scalable and very useful for bibliographic matching, which is a typical entity resolution problem for tree-structured data. Furthermore, we extend our algorithm to also allow a constant number of deletion operations on T1while still running in O(|T1∥T2|) time.

We have developed a real-time traffic incident detection system for the Tokyo Metropolitan Expressway. This system monitors current traffic using probe-car data and compares actual traffic in real time with the usual traffic, which is estimated in advance using batch processing.

This paper reports an empirical evaluation of a CRF-based bibliography parser we have developed for reference strings of research papers. The parser uses a conditional random field (CRF) to estimate the correct bibliographic label such as an author's name and a title for each token in a reference string. We applied the parser specifically designed for reference strings to three academic journals, an English one and two Japanese ones, published in Japan. Experiments showed (i) the parser correctly parsed from 90% to 94% of reference strings depending on the kinds of journals used and (ii) segmentation errors induced by tokenization considerably degraded the final parsing accuracies. This paper also discusses some future directions of the bibliography extraction based on a detailed analysis of the experiments.

In this paper, we address the text classification problem that a period of time created test data is different from the training data, and present a method for text classification based on temporal adaptation. We first applied lexical chains for the training data to collect terms with semantic relatedness, and created sets (we call these Sem sets). Semantically related terms in the documents are replaced to their representative term. For the results, we identified short terms that are salient for a specific period of time. Finally, we trained SVM classifiers by applying a temporal weighting function to each selected short terms within the training data, and classified test data. Temporal weighting function is weighted each short term in the training data according to the temporal distance between training and test data. The results using MedLine data showed that the method was comparable to the current state-of-the-art biased-SVM method, especially the method is effective when testing on data far from the training data.

Recommender systems (RS) are software tools that provide personalized recommendations of relevant items to individual users. However, most of them do not take into account additional contextual information that may affect user preferences, such as place, time, or weather. Context-aware recommender systems (CARS) have been proposed to solve this problem by providing recommendations for users based on their rating history in different situations. Although most have tried to identify the contextual variables that have the greatest effect on rating accuracy, they have not directly considered the relationships among context, users, and items before predicting the ratings. In the real world, different contextual factors tend to affect users and items differently. This work proposes a latent probabilistic model for contextual recommendation by extending the flexible mixture model to incorporate different contextual factors. This model has the flexibility to adjust the effects of contextual factors on users and items according to a variety of context-user-item relations to suit specific situations. Our evaluation has shown that the proposed model's recommendations are more accurate than those made by both latent probabilistic models and collaborative filtering-based CARS.

This paper provides a topic model for extracting topic evolutions as a corpus-wide transition matrix among latent topics. Recent trends in text mining point to a high demand for exploiting metadata. Especially, exploitation of reference relationships among documents induced by hyperlinking Web pages, citing scientific articles, tumblring blog posts, retweeting tweets, etc., is put in the foreground of the effort for an effective mining. We focus on scholarly activities and propose a topic model for obtaining a corpus-wide view on how research topics evolve along citation relationships. Our model, called TERESA, extends latent Dirichlet allocation (LDA) by introducing a corpus-wide topic transition probability matrix, which models reference relationships as transitions among topics. Our approximated variational inference updates LDA posteriors and topic transition posteriors alternately. The main issue is execution time amounting to O(MK2), where K is the number of topics and M is that of links in citation network. Therefore, we accelerate the inference with Nvidia CUDA compatible GPUs. We compare the effectiveness of TERESA with that of LDA by introducing a new measure called diversity plus focusedness (D+F). We also present topic evolution examples our method gives.

The references of academic articles include important bibliographic elements such as authors' names and article titles. Automatic extraction of these elements is useful because they can be used for various purposes, including searching. In this paper, a method for automatically extracting bibliographic elements from the text of reference strings is proposed. The proposed method assigns bibliographic labels to reference strings by using linguistic information and conditional random fields. Experimental results indicated that the extraction accuracies of major bibliographies were more than 96%.

This paper proposes an algorithm for recommender systems that uses both positive and negative latent user models. In recommending items to a user, recommender systems usually exploit item content information as well as the preferences of similar users. Various types of content information can be attached to items and these are useful for judging user preferences. For example, in movie recommendations, a movie record may include the director, the actors, and reviews. These types of information help systems calculate sophisticated user preferences. We first propose a probabilistic model that maps multi-attributed records into a low-dimensional feature space. The proposed model extends latent Dirichlet allocation to the handling of multi-attributed data. We derive an algorithm for estimating the model's parameters using the Gibbs sampling technique. Next, we propose a probabilistic model to calculate user preferences for items in the feature space. Finally, we develop a recommendation algorithm based on the probabilistic model that works efficiently for large quantities of items and user ratings. We use a publicly available movie corpus to evaluate the proposed algorithm empirically, in terms of both its recommendation accuracy and its processing efficiency.

Conventional search techniques are mainly designed to return a ranked list of single objects that are relevant to a given query. However, they do not meet the criteria for retrieving a combination of objects that is close to the query. This paper presents top-k query processing in which Euclidean distance is used as the scoring function for combinatorial objects. We also propose a pruning method based on clustering and efficiently select object combinations by pruning clusters that do not contain potential candidates for the top-k results. We compared the proposed method with the method that enumerates all the combinatorial objects and calculates the distance to the query. Experimental results revealed that the proposed method improves the processing efficiency to about 95% at maximum.

We propose a partitioning scheme for similarity search indexes that is called Maximal Metric Margin Partitioning (MMMP). MMMP divides the data on the basis of its distribution pattern, especially for the boundaries of clusters. A partitioning surface created by MMMP is likely to be at maximum distances from the two cluster boundaries. MMMP is the first similarity search index approach to focus on partitioning surfaces and data distribution patterns. We also present an indexing scheme, named the MMMP-Index, which uses MMMP and small ball partitioning. The MMMP-Index prunes many objects that are not relevant to a query, and it reduces the query execution cost. Our experimental results show that MMMP effectively indexes clustered data and reduces the search cost. For clustered vector data, the MMMP-Index reduces the computational cost to less than two thirds that of comparable schemes.

This paper presents a new Bayesian topical trend analysis. We regard the parameters of topic Dirichlet priors in latent Dirichlet allocation as a function of document timestamps and optimize the parameters by a gradient-based algorithm. Since our method gives similar hyperparameters to the documents having similar timestamps, topic assignment in collapsed Gibbs sampling is affected by timestamp similarities. We compute TFIDF-based document similarities by using a result of collapsed Gibbs sampling and evaluate our proposal by link detection task of Topic Detection and Tracking.

Approximate text search is a basic technique to handle recognized text that contains recognition errors. This paper proposes an approximate string search for recognized texturing a statistical similarity model focusing on parameter estimation. The main contribution of this paper is to propose a parameter estimation algorithm using variational Bayesian expectation maximization technique. We applied the obtained model to approximate substring detection problem and experimentally showed that the Bayesian estimation is effective.

Search results for personal name queries often contain documents relevant to several people as a personal name is often shared by several people. In order to differentiate people in these search results, it is required to extract contexts relevant to people in documents. However, since Web documents are noisy and the texts related to people might be short, it is difficult to extract contexts of people effectively. We propose a new method that uses web directories as additional information in order to recognize topic terms in documents more easily and to extract contexts of people more effectively. First, we apply latent Dirichlet allocation method to extract latent topics in Web directories. Then, the extracted topics are used to recognize topics contained in name ambiguity documents so that common context measurements can be calculated more effectively. Our experiments, conducted with documents of real people in the Web and several well-known Web directories, show that our approach disambiguates personal names better than some other conventional approaches like vector space model approach and named entity recognition approach.

Tree edit distance is one of the most frequently used distance measures for comparing trees. When using the tree edit distance, we need to determine the cost of each operation, but this is a labor-intensive and highly skilled task. This paper proposes an algorithm for learning the costs of tree edit operations from training data consisting of pairs of similar trees. To formalize the cost learning problem, we define a probabilistic model for tree alignment that is a variant of tree edit distance. Then, the parameters of the model are estimated using the expectation maximization (EM) technique. In this paper, we develop an algorithm for parameter learning that is polynomial in time (O{mn2d6)) and space (O{n2d4)) where n, d, and m represent the size of the trees, the maximum degree of trees, and the number of training pairs of trees, respectively.

Document generation from low level data and its utilization is one of the most challenging tasks in document engineering. Word occurrence detection is a fundamental problem in the recognized document utilization obtained by a recognizer, such as OCR and speech recognition. Given a set of words, such as a dictionary, this paper proposes an efficient dynamic programming (DP) algorithm to find the occurrences of each word in a text. In this paper, the string similarity is measured by a statistical similarity model that enables a definition of the similarities in the character level as well as edit operation level. The proposed algorithm uses tree structures to measure similarities in order to avoid measuring similarities of the same substrings appearing in different parts of the text and words. The time complexity of the proposed algorithm is O(|W|⋅|S|⋅|Q|), where |W| (resp. |S|) denote the number of nodes in the trees representing the word set (resp. the text), and |Q| donotes the number of the states of the model used for string similarity. This paper shows the proposed algorithm is experimentally about six times faster than a naive DP algorithm.

When constructing a large document archive, an important element is the digitizing of printed documents. Although various techniques for document image analysis such as Optical Character Recognition (OCR) have been developed, error handling is required in constructing real document archive systems. This paper discusses the problem from the quality enhancement perspective and proposes a robust reference extraction method for academic articles scanned with OCR mark-up. We applied the proposed method to articles appearing in various journals, and these experiments showed that the proposed method achieved a recognition accuracy of more than 94%. This paper also discusses manual correction and investigates experimentally the relationship between extraction accuracy and cost reduction.

It is important to utilize retrospective documents. OCR is the most widely applied technology for this purpose; however, error-tolerant methods are essential for utilizing OCR-processed documents. This paper discusses a filtering problem for OCR-processed documents that enables the handling of large numbers of OCR-processed documents in an error-tolerant way. It proposes a systematic index design method for filtering and shows that the filtering method speeds up by about 360 times for a database consisting of about two million records, with little decrease in accuracy.

Text categorization is one of the key functions for utilizing vast amount of documents. It can be seen as a classification problem, which has been studied in pattern recognition and machine learning fields for a long time and several classification methods have been developed such as statistical classification, decision tree, support vector machines and so on. Many researchers applied those classification methods to text categorization and reported their performance (e.g., decision tree[3], Bayes classifier[2], support vector machine[l]). Yang conducted comprehensive study of comparison or text categorization and reported that k nearest neighbor and support vector machines works well for text categorization[4].In the previous studies, classification methods were usually compared using single pair of training and test data However, classification method with more complex family of classifiers requires more training data and small training data may result in deriving unreliable classifier, that is, the performance of the derived classifier varies much depending on training data. Therefore, we need to take the size of training data into account when comparing and selecting a classification method. In this paper, we discuss how to select a classifier from those derived by various classification methods and how the size of training data affects the performance of the derived classifier.In order to evaluate the reliability of classification method, we consider the variance of accuracy of derived classifier. We first construct a statistical model. In the text categorization, each document is usually represented with a feature vector that consists of weighted frequencies of terms. In the vector space model, document is a point in high dimensional feature space and a classifier separates the feature space into subspaces each of which is labeled with a category.

This paper presents three probabilistic text retrieval methods designed to carry out a full-text search of English documents containing OCR errors. By searching for any query term on the premise that there are errors in the recognized text, the methods presented can tolerate such errors, and therefore costly manual post-editing is not required after OCR recognition. In the applied approach, confusion matrices are used to store characters which are likely to be interchanged when a particular character is missrecognized, and the respective probability of each occurrence. Moreover, a 2-gram matrix is used to store probabilities of character connection, i.e., which letter is likely to come after another. Multiple search terms are generated for an input query term by making reference to confusion matrices, after which a full-text search is run for each search term. The validity of retrieved terms is determined based on error-occurrence and character connection probabilities. The performance of these methods is experimentally evaluated by determining retrieval effectiveness, i.e., by calculating recall and precision rates. Results indicate marked improvement in comparison with exact matching.

This paper presents a fast approximate string matching method. In constructing information spaces such as digital libraries, we have to collect vast amount of information and convert it into uniformly organized data. Since much of the information must be converted from various media automatically, the space contains garbled text with various accuracy. For utilizing these texts, we need to satisfy the three requirements, i.e., high recall, high precision and fast matching process. In order to satisfy these requirements, we have been developing a two-phase matching system. The presented method is used for fast and high recall candidate word selection in the first phase. The key idea of the method is to use a portion of characters of a word and a distance pattern in order to use current index techniques. By experiments, we confirm that the presented method achieves high recall even for the poorly recognized texts.

The article describes a framework for electronic library systems incorporating automated generation of electronic library schema from paper printed materials, offering a hypertext style browsing interface. On our framework, document image understanding techniques are applied to table-of-content images of academic journals to automatically acquire bibliographic database schema and hypertext schema. The system should only provide typical hypertext links as implicit links in the form of "functions." Although the basic framework of automatic generation of hypertext schema has been explained before, we describe an overview of an experimental electronic library system CyberMagazine based on this framework to achieve basic electronic library facilities.

A syntactic rule learning method is presented for analyzing document images and constructing a database from them. This method is used in a digital library system named CyberMagazine, where document images are sequentially converted into database tuples by block segmentation, rough classification, and syntactic analysis. The syntactic rule has an ability to analyze symbols located in two dimensional plane, and has a syntax similar to an ordinal context free grammar except for the concatenation of symbols. In the presented learning method, the syntactic rules are generated from a set of parse trees by decomposing the trees according to non terminal symbols, generalizing the decomposed trees to a syntactic rule, and merging them.

A new method of collaborative unification between document image understanding and hypertext construction is presented. Document image understanding is indispensable to electronic library systems, but document understanding technologies are still immature. Moreover, hypertext links are difficult to acquire by hand. In the approach presented, document image understanding is taken as classification of text blocks to classes of bibliographic items which compose the understanding thesaurus. Hypertext links are obtained implicitly with functions corresponding to classes, and thus they are obtained automatically from understanding results. These classes include incompletely recognized classes distinctly to offer utilization of incompletely recognized text blocks as they are. Using this approach, large scale and practical electronic library systems can be offered.>