Kai Lenz

Efficiently Finding Paths Between Classes to Build a SPARQL Query for Life-Science Databases

Many databases in life science are provided in Resource Description Framework (RDF) model with SPARQL Protocol and RDF Query Language (SPARQL) endpoints. However, it may be difficult for users who are not familiar with Semantic Web technologies to write a SPARQL query. Therefore, assisting users to build SPARQL queries is important task to expand the range of users of RDF databases. We developed a web application called SPARQL Builder (http://sparqlbuilder.org/) that enables users to access life-science RDF datasets by assisting them in writing SPARQL queries. One of the key technologies used in SPARQL Builder is to extract possible relationships in an RDF dataset between two classes of input and output data. We express such relationships by paths on a labeled graph called class graph representing class–predicate–class relations in a dataset. In addition, we present an efficient algorithm to compute all the possible paths between two classes on a class graph. To show the performance of the proposed algorithm, we compared our algorithm with a naive method using RDF datasets of various class sizes and confirmed that our algorithm runs much faster when the numbers of classes and relations are relatively large.

Semantic Data Acquisition by Traversing Class–Class Relationships Over Linked Open Data

Linked Open Data (LOD), a powerful mechanism for linking different datasets published on the World Wide Web, is expected to increase the value of data through mashups of various datasets on the Web. One of the important requirements for LOD is to be able to find a path of resources connecting two given classes. Because each class contains many instances, inspecting all of the paths or combinations of the instances results in an explosive increase of computational complexity. To solve this problem, we have proposed an efficient method that obtains and prioritizes a comprehensive set of connections over resources by traversing class–class relationships of interest. To put our method into practice, we have been developing a tool for LOD exploration. In this paper, we introduce the technologies used in the tool, focusing especially on the development of a measure for predicting whether a path of class–class relationships has connected triples or not. Because paths without connected triples can be predicted and removed, using the prediction measure enables us to display more paths from which users can obtain data that interests them.

RIKEN MetaDatabase: A Database Platform as a Microcosm of Linked Open Data Cloud in the Life Sciences

The amount and heterogeneity of life-science datasets published on the Web have considerably increased recently. However, biomedical scientists face numerous serious difficulties in finding, using and publishing useful databases. In order to solve these issues, we developed a Resource Description Framework-based database platform, called RIKEN MetaDatabase, which allows biologists to easily develop, publish and integrate databases. The platform manages metadata of both research data and individual data described with standardised vocabularies and ontologies, and has a simple browser-based graphical user interface for viewing data including tabular and graphical views. The platform was released in April 2015, and 110 databases including mammalian, plant, bioresource and image databases with 21 ontologies have been published through this platform as of July 2016. This paper describes the technical knowledge obtained through the development and operation of RIKEN MetaDatabase as a challenge for accelerating life-science data distribution promotion.

RIKEN MetaDatabase: A Database Platform for Health Care and Life Sciences as a Microcosm of Linked Open Data Cloud

Recently, the number and heterogeneity of life science datasets published on the Web have increased significantly. However, biomedical scientists face numerous serious difficulties finding, using and publishing useful databases. To address these issues, the authors developed a Resource Description Framework-based database platform, called the RIKEN MetaDatabase (http://metadb.riken.jp), that allows biologists to develop, publish and integrate multiple databases easily. The platform manages the metadata of both research and individual data described using standardised vocabularies and ontologies, and has a simple browser-based graphical user interface to view data including tabular and graphical forms. The platform was released in April 2015, and 113 databases, including mammalian, plant, bioresource and image databases, with 26 ontologies have been published using this platform as of January 2017. This paper describes the technical knowledge obtained through the development and operation of the RIKEN MetaDatabase to accelerate life science data distribution.