Recently, network analysis has been intensively investigated in several fields of science. Link prediction is a problem of predicting the existence of a link between two entities based on observed links, and it is one of the popular link mining tasks. Although many link prediction methods have been proposed, they have their merits and demerits. In this paper, we present two topics as follows: 1) In order to obtain the strategies of selecting the best link prediction methods, we perform experiments of six link prediction methods (Common Neighbors (CN) , Jaccard's Coefficient (JC) , Adamic/Adar (AA) , Shortest Path (SP) , Preferential Attachment (PA) and Hierarchical Random Graph (HRG) ) for 39 real networks. 2) We propose a new similarity that is the summation of similarities based on the logistic regression. We used 10-fold cross validation and bagging for model selection of proposed method. We estimate the accuracy and computation time of HRG, proposed method (bagging) and proposed method (10-fold cross validation) for 28 data sets. As a result of 1) , CN, JC and AA achieve good performance for the networks that has higher clustering coefficient than 0.4. SP achieves good performance for the network that has higher average shortest path length than 3. PA underperforms the random predictor for the network has lower variance of degrees than 0.5. HRG performs consistently well. As a result of 2) , accuracy of proposed methods (both of bagging and 10-fold cross validation) are reached higher than the accuracy of HRG for 17 data sets and finishes the calculation faster than HRG. Proposed methods perform good accuracy for social network, citation network, dictionary network, biological network and transfer network (journey). Proposed methods underperform for trade network, circuit network, and food web network. Sometimes, proposed method (bagging) reaches higher accuracy than the accuracy of proposed method (10-fold cross validation). Proposed method (10-fold cross validation) finishes the calculation faster than proposed method (bagging). In conclusion, proposed methods finish the calculation faster than HRG and accuracy of proposed methods reaches higher than HRG.

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Extraction of named entitiy classes and their relationships from large corpora often involves morphological analysis of target sentences and tends to suffer from out-of-vocabulary words. In this paper we propose a semantic category extraction algorithm called Monaka and its graph-based extention g-Monaka, both of which use character n-gram based patterns as context to directly extract semantically related instances from unsegmented Japanese text. These algorithms also use ``bidirectional adjacent constraints,'' which states that reliable instances should be placed in between reliable left and right context patterns, in order to improve proper segmentation. Monaka algorithms uses iterative induction of instaces and pattens similarly to the bootstrapping algorithm Espresso. The g-Monaka algorithm further formalizes the adjacency relation of character n-grams as a directed graph and applies von Neumann kernel and Laplacian kernel so that the negative effect of semantic draft, i.e., a phenomenon of semantically unrelated general instances being extracted, is reduced. The experiments show that g-Monaka substantially increases the performance of semantic category acquisition compared to conventional methods, including distributional similarity, bootstrapping-based Espresso, and its graph-based extension g-Espresso, in terms of F-value of the NE category task from unsegmented Japanese newspaper articles.

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This paper presents a new way of formalizing the Coalition Structure Generation problem (CSG), so that we can apply constraint optimization techniques to it. Forming effective coalitions is a major research challenge in AI and multi-agent systems. CSG involves partitioning a set of agents into coalitions so that social surplus is maximized. Traditionally, the input of the CSG problem is a black-box function called a characteristic function, which takes a coalition as an input and returns the value of the coalition. As a result, applying constraint optimization techniques to this problem has been infeasible. However, characteristic functions that appear in practice often can be represented concisely by a set of rules, rather than a single black-box function. Then, we can solve the CSG problem more efficiently by applying constraint optimization techniques to the compact representation directly. We present new formalizations of the CSG problem by utilizing recently developed compact representation schemes for characteristic functions. We first characterize the complexity of the CSG under these representation schemes. In this context, the complexity is driven more by the number of rules rather than by the number of agents. Furthermore, as an initial step towards developing efficient constraint optimization algorithms for solving the CSG problem, we develop mixed integer programming formulations and show that an off-the-shelf optimization package can perform reasonably well, i.e., it can solve instances with a few hundred agents, while the state-of-the-art algorithm (which does not make use of compact representations) can solve instances with up to 27 agents.

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It is ideal to provide medical services as patient-oriented. The medical staff members share the final goals to recover patients. Toward the goals, each staff has practical knowledge to achieve patient-oriented medical services. But each medical staff has his/her own sense of value that comes from his/her expertness. Therefore the practical knowledge sometimes conflicts. The aim of this research is to develop an intelligent system to support externalizing practical knowledge, and sharing it among medical staff members. In this paper, the author propose a method to model the sense of value of each medical staff as his/her understanding about medical service workflow, and to obtain the practical knowledge using the models. The method was experimented by an implementation of knowledge-sharing system base on the method and by its trial use in Miyazaki University Hospital.

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Kernel method is one of the promising approaches to learning with tree-structured data, and various efficient tree kernels have been proposed to capture informative structures in trees. In this paper, we propose a new tree kernel function based on ``subpath sets'' to capture vertical structures in tree-structured data, since tree-structures are often used to code hierarchical information in data. We also propose a simple and efficient algorithm for computing the kernel by extending the Multikey quicksort algorithm used for sorting strings. The time complexity of the algorithm is O((|T_1|+|T_2|)log(|T_1|+|T_2|)) time on average, and the space complexity is O({|T_1|+|T_2|)}, where |T_1| and |T_2| are the numbers of nodes in two trees T_1 and T_2. We apply the proposed kernel to two supervised classification tasks, XML classification in web mining and glycan classification in bioinformatics. The experimental results show that the predictive performance of the proposed kernel is competitive with that of the existing efficient tree kernel proposed by Vishwanathan et al., and is also empirically faster than the existing kernel.

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