Takahira Yamaguchi

Coordinating Web Services based on business models

Coordinating Web Services dynamically revolutionizes how each business collaborates for its performance improvement, so that many IT researchers are recently focusing on them. In order to make the coordination really effective in business, we have to take viewpoints from business models. Though several specifications and approaches were proposed for the coordination, there are no clear relationships between them and business models yet. In this paper, we propose a framework for Web Services coordination based on business models. We have focused on integrating supports for business model management and Web Services based business application development. The framework provides two kinds of repositories for business model management and application development respectively. These repositories enable us to reuse existing models and manage relationships between them, so that we can rapidly reflect business model changes on Web Services coordination.

Evaluation of rule interestingness measures in medical knowledge discovery in databases

OBJECTIVE We discuss the usefulness of rule interestingness measures for medical KDD through experiments using clinical datasets, and, based on the outcomes of these experiments, also consider how to utilize these measures in postprocessing. METHODS AND MATERIALS We first conducted an experiment to compare the evaluation results derived from a total of 40 various interestingness measures with those supplied by a medical expert for rules discovered in a clinical dataset on meningitis. We calculated and compared the performance of each interestingness measure to estimate a medical experts interest using f-measure and correlation coefficient. We then conducted a similar experiment for hepatitis. RESULTS AND CONCLUSION The comprehensive results of experiments on meningitis and hepatitis indicate that the interestingness measures, accuracy, chi-square measure for one quadrant, relative risk, uncovered negative, and peculiarity, have a stable, reasonable performance in estimating real human interest in the medical domain. The results also indicate that the performance of interestingness measures is influenced by the certainty of a hypothesis made by the medical expert, and that the combinational use of interestingness measures will contribute to support medical experts to generate and confirm their hypotheses through human-system interaction.

Implementing an integrated time-series data mining environment based on temporal pattern extraction methods: a case study of an interferon therapy risk mining for chronic hepatitis

In this paper, we present the implementation of an integrated time-series data mining environment. Time-series data mining is one of key issues to get useful knowledge from databases. With mined time-series patterns, users can aware not only positive results but also negative result called risk after their observation period. However, users often face difficulties during time-series data mining process for data pre-processing method selection/construction, mining algorithm selection, and post-processing to refine the data mining process as other data mining processes. It is needed to develop a time-series data mining environment based on systematic analysis of the process. To get more valuable rules for domain experts from a time-series data mining process, we have designed an environment which integrates time-series pattern extraction methods, rule induction methods and rule evaluation methods with active human-system interaction. After implementing this environment, we have done a case study to mine time-series rules from blood and urine biochemical test database on chronic hepatitis patients. Then a physician has evaluated and refined his hypothesis on this environment. We discuss the availability of how much support to mine interesting knowledge for an expert.

Learning a Large Scale of Ontology from Japanese Wikipedia

Here is discussed how to learn a large scale of ontology from Japanese Wikipedia. The learned ontology includes the following properties: rdfs:subClassOf (IS-A relationships), rdf:type (class-instance relationships), owl:Object/DatatypeProperty (Infobox triples), rdfs:domain (property domains), and skos:altLabel (synonyms). Experimental case studies show us that the learned Japanese Wikipedia Ontology goes better than already existing general linguistic ontologies, such as EDR and Japanese WordNet, from the points of building costs and structure information richness.

DODDLE: A Domain Ontology Rapid Development Environment

This paper focuses on how to construct domain ontologies, in particular, a hierarchically structured set of domain concepts without concept definitions, reusing a machine readable dictionary (MRD) and making it adjusted to specific domains. In doing so, we must deal with concept drift, which means that the senses of concepts change depending on application domains. So here are presented the following two strategies: match result analysis and trimmed result analysis. The strategies try to identify which part may stay or should be moved, analyzing spell match results between given input domain terms and a MRD. We have done case studies in the filed of some law. The empirical results show us that our system can support a user in constructing a domain ontology.

Intelligent humanoid robot with japanese Wikipedia ontology and robot action ontology

WioNA (Wikipedia Ontology NAo) is proposed to build much better HRI by integrating four elements: Japanese speech interface, semantic interpretation, Japanese Wikipedia Ontology and Robot Action Ontology. WioNA is implemented on a humanoid robot “Nao”. In WioNA, we developed two ontologies: Japanese Wikipedia Ontology and Robot Action Ontology. Japanese Wikipedia Ontology has a large size of concept hierarchy and instance network with many properties from Japanese Wikipedia (semi) automatically. By giving Japanese Wikipedia Ontology to Nao as wisdom, Nao can dialogue with users on many topics of various fields. Robot Action Ontology, in contrast, is built by organizing various performable actions of Nao to control and generate robot actions. Aligning Robot Action Ontology with Japanese Wikipedia Ontology enables Nao to perform related actions to dialogue topics. To show the validities of WioNA, we describe human-robot conversation logs of two case studies whose dialogue topics are sport and rock singer. These case studies show us how HRI goes well in WioNA with these topics.

Comparison between objective interestingness measures and real human interest in medical data mining

One of the difficulties of using Artificial Neural Networks (ANNs) to estimate atmospheric temperature is the large number of potential input variables available. In this study, four different feature extraction methods were used to reduce the input vector to train four networks to estimate temperature at different atmospheric levels. The four techniques used were: genetic algorithms (GA), coefficient of determination (CoD), mutual information (MI) and simple neural analysis (SNA). The results demonstrate that of the four methods used for this data set, mutual information and simple neural analysis can generate networks that have a smaller input parameter set, while still maintaining a high degree of accuracy.

DODDLE-OWL: Interactive Domain Ontology Development with Open Source Software in Java

In this paper, we propose an interactive domain ontology development environment called DODDLE-OWL. DODDLE-OWL refers to existing ontologies and supports the semi-automatic construction of taxonomic and other relationships in domain ontologies from documents. Integrating several modules, DODDLE-OWL is a practical and interactive domain ontology development environment. In order to evaluate the efficiency of DODDLE-OWL, we compared DODDLE-OWL with popular manual-building method. In order to evaluate the scalability of DODDLE-OWL, we constructed a large sized ontology over 34,000 concepts in the field of rocket operation using DODDLE-OWL. Through the above evaluation, we confirmed the efficiency and the scalability of DODDLE-OWL. Currently, DODDLE-OWL is open source software in Java and has 100 and more users from 20 and more countries.

Constructive meta-learning with machine learning method repositories

One of the difficulties of using Artificial Neural Networks (ANNs) to estimate atmospheric temperature is the large number of potential input variables available. In this study, four different feature extraction methods were used to reduce the input vector to train four networks to estimate temperature at different atmospheric levels. The four techniques used were: genetic algorithms (GA), coefficient of determination (CoD), mutual information (MI) and simple neural analysis (SNA). The results demonstrate that of the four methods used for this data set, mutual information and simple neural analysis can generate networks that have a smaller input parameter set, while still maintaining a high degree of accuracy.

A rule evaluation support method with learning models based on objective rule evaluation indexes

In this paper, we present a novel rule evaluation support method for post-processing of mined results with rule evaluation models based on objective indexes. Post-processing of mined results is one of the key issues to make a data mining process successfully. However, it is difficult for human experts to evaluate many thousands of rules from a large dataset with noises completely. To reduce the costs of rule evaluation procedures, we have developed the rule evaluation support method with rule evaluation models, which are obtained with objective rule evaluation indexes and evaluations of a human expert for each rule. Since the method is needed more accurate rule evaluation models, we have compared learning algorithms to construct rule evaluation models with the actual meningitis data mining result and actual rule sets from UCI datasets. Then we show the availability of our adaptive rule evaluation support method.