Ryutaro Ichise

Integrating Know-How into the Linked Data Cloud

This paper presents the first framework for integrating procedural knowledge, or “know-how”, into the Linked Data Cloud. Know-how available on the Web, such as step-by-step instructions, is largely unstructured and isolated from other sources of online knowledge. To overcome these limitations, we propose extending to procedural knowledge the benefits that Linked Data has already brought to representing, retrieving and reusing declarative knowledge. We describe a framework for representing generic know-how as Linked Data and for automatically acquiring this representation from existing resources on the Web. This system also allows the automatic generation of links between different know-how resources, and between those resources and other online knowledge bases, such as DBpedia. We discuss the results of applying this framework to a real-world scenario and we show how it outperforms existing manual community-driven integration efforts.

Machine Learning Approach for Ontology Mapping Using Multiple Concept Similarity Measures

This paper presents a new framework for the ontology mapping problem. We organized the ontology mapping problem into a standard machine learning framework, which uses multiple concept similarity measures. We presented several concept similarity measures for the machine learning framework and conducted experiments for testing the framework using real-world data. Our experimental results show that our approach has increased performance with respect to precision, recall and F-measure in comparison with other methods.

Semantic and Event-Based Approach for Link Prediction

The scientific breakthroughs resulting from the collaborations between researchers often outperform the expectations. But finding the partners who will bring this synergic effect can take time and sometime gets nowhere considering the huge amounts of experts in various disciplines. We propose to build a link predictor in a network where nodes represent researchers and links - coauthorships. In this method we use the structure of the constructed graph, and propose to add a semantic and event based approach to improve the accuracy of the predictor. In this case, predictors might offer good suggestions for future collaborations. We will be able to compute the classification of a massive dataset in a reasonable time by under-sampling and balancing the data. This model could be extended in other fields where the research of partnership is important as in world of institutions, associations or companies. We believe that it could also help with finding communities of topics, since link predictors contain implicit information about the semantic relation between researchers.

Community mining tool using bibliography data

Research communities are very important for researchers undertaking new research topics. In this paper, we propose a community mining system using bibliography data in order to find communities of researchers. The basic concept of our study is to provide interactive visualization of both local and global research communities. We implement this concept using actual bibliography data and present a case study using the proposed system.

Similarity search on supergraph containment

A supergraph containment search is to retrieve the data graphs contained by a query graph. In this paper, we study the problem of efficiently retrieving all data graphs approximately contained by a query graph, namely similarity search on supergraph containment. We propose a novel and efficient index to boost the efficiency of query processing. We have studied the query processing cost and propose two index construction strategies aimed at optimizing the performance of different types of data graphs: top-down strategy and bottom-up strategy. Moreover, a novel indexing technique is proposed by effectively merging the indexes of individual data graphs; this not only reduces the index size but also further reduces the query processing time. We conduct extensive experiments on real data sets to demonstrate the efficiency and the effectiveness of our techniques.

Ontology Integration for Linked Data

The Linked Open Data cloud contains tremendous amounts of interlinked instances with abundant knowledge for retrieval. However, because the ontologies are large and heterogeneous, it is time-consuming to learn all the ontologies manually and it is difficult to learn the properties important for describing instances of a specific class. To construct an ontology that helps users to easily access various data sets, we propose a semi-automatic system, called the Framework for InTegrating Ontologies, that can reduce the heterogeneity of the ontologies and retrieve frequently used core properties for each class. The framework consists of three main components: graph-based ontology integration, machine-learning-based approach for finding the core ontology classes and properties, and integrated ontology constructor. By analyzing the instances of linked data sets, this framework constructs a high-quality integrated ontology, which is easily understandable and effective in knowledge acquisition from various data sets using simple SPARQL queries.

Research Community Mining with Topic Identification

Since research trends can change dynamically, researchers have to keep up with these new trends and undertake new research topics. Therefore, research communities for new research domains are important. In this paper, we propose a method to discover research communities. The key features of our method are a network model of papers and a word assignment technique for the communities obtained. We show our system based on the proposed method and discuss our system through case studies and experiments

Interlinking Linked Data Sources Using a Domain-Independent System

Linked data interlinking is the discovery of every owl:sameAs links between given data sources. An owl:sameAs link declares the homogeneous relation between two instances that co-refer to the same real-world object. Traditional methods compare two instances by predefined pairs of RDF predicates, and therefore they rely on the domain of the data. Recently, researchers have attempted to achieve the domain-independent goal by automatically building the linkage rules. However they still require the human curation for the labeled data as the input for learning process. In this paper, we present SLINT+, an interlinking system that is training-free and domain-independent. SLINT+ finds the important predicates of each data sources and combines them to form predicate alignments. The most useful alignments are then selected in the consideration of their confidence. Finally, SLINT+ uses selected predicate alignments as the guide for generating candidate and matching instances. Experimental results show that our system is very efficient when interlinking data sources in 119 different domains. The very considerable improvements on both precision and recall against recent systems are also reported.

Discovering Relationships Among Catalogs

When we have a large amount of information, we usually use categories with a hierarchy, in which all information is assigned. The Yahoo! Internet directory is one such example. This paper proposes a new method of integrating two catalogs with hierarchical categories. The proposed method uses not only the contents of information but also the structures of both hierarchical categories. In order to evaluate the proposed method, we conducted experiments using two actual Internet directories, Yahoo! and Google. The results show improved performance compared with the previous approaches.

Graph-based ontology analysis in the linked open data

The Linked Open Data (LOD) includes over 31 billion Resource Description Framework (RDF) triples interlinked by around 504 million SameAs links (as of September 2011). The data sets of the LOD use different ontologies to describe instances, that cause the ontology heterogeneity problem. Dealing with the heterogeneous ontologies is a challenging problem and it is time-consuming to manually learn big ontologies in the LOD. The heterogeneity of ontologies in the LOD can be reduced by automatically integrating related ontology classes and properties, which can be retrieved from interlinked instances. The interlinked instances can be represented as an undirected graph, from which we can discover the characteristics of instances and retrieve related ontology classes and properties that are important for linking instances. In this paper, we retrieve graph patterns from several linked data sets and perform ontology alignment methods on each graph pattern to identify related ontology classes and properties from the data sets. We successfully integrate various ontologies, analyze the characteristics of interlinked instances, and detect mistaken properties in the real data sets. Furthermore, our approach solves the ontology heterogeneity problem and helps Semantic Web application developers easily query on various data sets with the integrated ontology.