• Galaxy - SQL Tools - Repository - Installation
• Galaxy - SPARQL Tools - Repository - Installation
• Galaxy - Cypher Tools - Installation
• Interactive Viewer - Repository - Installation

• Demo - Instruction
• Galaxy (to convert & load) - http://sem4j.org/galaxy/
• Viewer (to search & view) - http://sem4j.org/

Various omics datasets are publicly available in RDF via their data repositories or endpoints and this makes it easier for us to obtain integratable datasets from different data sources. Meanwhile, when we use a part of linked data on our own applications for data analyses or visualization, the data format does not have to be RDF but can be processed into appropriate formats according to the usages. In fact, most web applications handle table format data rather than RDF.

We can think of two reasons to convert semantic networks into other data models rather than keeping original RDF in backend databases. One reason is the difficulty of understanding complex RDF schema and writing SPARQL queries, and another reason is that the data model described in RDF is not always optimized for search performance. Consequently, we need practical methods to convert RDF into the data model optimized for each application in order to build an efficient database using a part of linked data.

The simplest method to optimize RDF data for most applications is to convert it into table format data and storing it into relational databases. In this method, however, we need to consider not only table definition but also de-normalization and indices to reduce the cost of table join operations. As a result, we are focusing on graph databases instead. In graph databases, their data models can naturally describe semantic networks and enable network search operations such as traversal as well as in triplestores.

Although the data models in graph databases are similar in structure to RDF-based semantic networks, they are different in some aspects. For example, in the graph database management system we used, Neo4j, relationships can hold properties, while edges in RDF-based semantic networks do not have properties. We are therefore researching how to fit RDF data to effectively use graph database features for better search performance and efficient application development.

We developed the tools to convert RDF data (as well as table format data) and load the data into graph databases. Also, we are developing demo applications to search and visualize graph data such as pathway networks.