Recently, it is very important to educate about information literacy since information techniques are rapidly developed. However, common view and definition on information literacy are not established enough. Therefore, it is required to systematize concepts related to information literacy. This article discusses an experimental development of Information Literacy Ontology.

In this paper, we develop a physical activity recommendation system for lifestyle-related disease prevention. Biological information of users are acquired by compact heart rate mater. Then, ontology is defined about some useful index of evaluation in physical activities. In the developed system,, recommendation to deal with personal differences.is provided for each user. To evaluate recommendation, we experiment using heart rate mater and environment.

With the current semantic technology, Linked Data are generated as a set of triples, each of which consists of a subject, a predicate, and an object. Although it may be possible to encode any piece of information with a set of triples, there is no standard way of encoding complex information with triples. This paper proposes a method for generating Linked Information using an object-oriented method of describing semantic structures. In this method, any piece of information is described with a set of objects. From descriptions of these objects, Linked Information can be generated in a standard way.

In this paper, we propose an automatic concept addition method for ontology using Web mining. B y using Web mining , it is possible to make effect ive use as a source of knowledge appropriately large amount of information to be updated daily on the Web. Moreover, a lot of knowledge can be reflected in ontology at once by automating conceptual addition work. To evaluate the proposal method, we adopt a n ontology for elementary science.

This paper proposes a similarity measure for extremely short texts in legal domain, especially aiming at the task of retrieving related laws given a question text of the bar examination of Japan. Through this task we discuss the effectiveness of utilizing structured knowledge in text processing in the legal domain. We construct a lexical network providing both linguistic and domain-specific relations between legal terminologies, and evaluate the utilities and limitations for the application to this retrieval task.

Linked Open Data (LOD) has a graph structure, where nodes are represented by URIs, and thus LOD sets are connected and searched through different domains. In fact, however, 5% of values are literal (string without URI) even in DPpedia, which is a defacto hub of LOD. Therefore, this paper proposes a method to identify and aggregate the literal nodes in order to give an URI to the literals of the same meaning and to promote the data linkage. Our method regards part of the LOD graph structure as an block image, and then extracts image features based on SIFT, and performs an ensemble learning which is well known in the area of Computer-Vision. In the experiment, we created about 30,000 literal pairs from the Japanese music category of DPpedia Japanese and Freebase, and confirmed that the propsed method correctly determines the literal identity in F-measure of 99%.

This paper proposes RDF representation for the schedule of a local event. We investigated schedule expressions on web pages provided by two local tourist bureaus and found that they can be classified into four general patterns. We, then, developed RDF data structure for each pattern referring to RDFCalendar, an RDF version of iCalendar.