Introduction

The amount of openly available research data in machine-readable formats such as RDF is constantly growing, due to the rise of open research portals, on the one hand, and successively more powerful natural language processing tools offering the capability of transforming unstructured data into structured form, on the other. Being tailored to automated processing by machines and services, these data sources are often hard to access by human researchers, as research data in various domains are inherently highly complex, and the data representation formalisms impose high barriers to human domain experts regarding their ability to interact with the data source. Against this background, the need for low-level, intuitive access to research data in RDF or derived formalisms becomes more important than ever. Current approaches towards browsing linked data such as LODmilla [4] , Yuzu [1] , Disco [3] or Tabulator [2] closely follow the RDF data model. In case of complex knowledge structures as they are ubiquitous in research data, this may result in obfuscation due to a multitude of triples which need to be browsed.

In this work, we present SCIExplorer, an interactive web application for investigating the results of preclinical studies in the spinal cord injury (SCI) domain that are represented in RDF. Offering an intuitive exploration of the data in a visual interface based on the data-describing ontology, SCIExplorer is designed to overcome the aforementioned problems, thus enabling domain experts without profound technical knowledge to formulate complex queries that are meaningful from a research perspective. In particular, the tool provides filter-driven dynamic generation of SPARQL queries without the need to specify them manually, and offers drill-downs to individual preclinical results in order to visualize their results and core parameters at a glance. We demonstrate that SCIExplorer can be effectively used to answer relevant domain-specific competency questions provided by experts.

Background: Automatically Populating a Spinal Cord Injury Knowledge Base from Preclinical Studies

Our motivation to develop an exploration tool for efficiently browsing RDF research data arose in the context of the PSINK project ² , that aims at ontology-based information extraction from large amounts of preclinical studies in the SCI domain. The main project goal is to create the first comprehensive SCI knowledge base in order to foster translation from preclinicial research into therapeutic concepts that are likely to be effective in human patients. The resulting knowledge structures, i.e., the results of preclinical studies, are represented in an RDF database that can be queried via SPARQL. Both the information extraction process and the data representation scheme underlying the SCIExplorer are guided by the Spinal Cord Injury Ontology (SCIO) that is specifically developed in this project [5] . Currently, SCIO contains over 500 entities and relations describing a preclinical study in a very fine-grained and well-structured way.

SCIExplorer

This tool was developed to explore the complex structures of preclinical studies that follow the conceptualization provided by the Spinal Cord Injury Ontology. The interface is generated semi-automatically given the specifications of an ontology and a configuration file. The latter is used to enable different views to the data, thus focusing on different aspects of SCIO.

Technical Details. SCIExplorer is a stand-alone framework written in Java, HTML and PHP. ³ It is accessible though common web-browsers. The graphical interfaces relies on the JavaEE standard JavaServer Faces – Framework (JSF) that is based on Servlets and JavaServer Pages (JSP). The content management is realized with PrimeFaces.

System Architecture. Figure 1 shows the overall system architecture. This work focuses on the SCIExplorer (grey box) that is divided into three main parts:

1. The Exploration Interface serves as an entry point. It is aligned to the ontology and modified by a configuration file that specifies different views to the data. The data exploration is driven by various filter strategies that are automatically extracted from the ontology.

2. The Query Generator automatically transforms set filters (based on concepts and properties provided by SCIO) into SPARQL statements.

3. The extracted data is prepared and visualized in the Data Presentation module. The configuration file specifies the parts of the data that should be viewed.

In the following, all parts are described in more detail.

?OrganismModel rdfs:type scio:RatModel.

?OrganismModel scio:hasGender scio:Male.

?OrganismModel scio:hasWeight ?Weight.
?Weight scio-data:hasValue ?WeightValue.
       FILTER regex(?WeightValue, "300-400 gramm", "I")

Use Cases in the Spinal Cord Injury Domain

Common requirements proposed by researchers in the SCI domain involve answering competency questions aiming at comparative views of individual studies with a focus on certain key parameters, or treatment analytics at different levels of depth and granularity (e.g., detecting clusters of equivalent treatment concepts, predicting their prospective success in different model organisms, among others). Definitive answers to such information needs may require statistical analyses and meta studies. Yet, SCIExplorer in its current state is already capable of delivering valuable information regarding comparative views on treatments or the generation of new hypotheses or explanations about the observed (in-)efficacy of therapeutic concepts.

Consider the following competency question:

In which lesion models being tested in male rats or mice have no negative effects of erythropoietin been observed so far?

Answering this question requires the specification of three filters ( MouseModel , RatModel , Male ). As a result, the answer can be inferred from investigating the InjuryType pane on the dashboard (cf. Figure 5 ): The current data pool contains two different injury models treated with erythropoietin, viz., compressions and contusions. So far, negative outcomes have only been reported for compressions.

Additionally, inspection of the Dosage pane shows that the negative result is tied to a particular dosage of 0.01 ml/kg of erythropoietin; however, the same dosage has also led to positive and neutral outcomes. In order to explore this finding in more detail, a drill-down can be generated by adding an additional constraint for the specific dosage. The resulting table, presenting all individual results from the RDF data pool which satisfy the current set of ontological constraints at a glance, is displayed in Figure 6 .

As can be seen from the table, the difference in outcomes (positive vs. negative/neutral) is due to different time points of the measurement of the observation (four weeks vs. six weeks after treatment). Apparently, all other parameters being equal, erythropoietin in mice tends to induce early effects towards functional recovery after compression which are not sustainable over time, though.

Discussion

We have presented SCIExplorer as a tool that enables visual analytics over research data represented in RDF, based on an intuitive access layer that is guided by an underlying domain ontology. With a focus on the spinal cord injury domain, we have demonstrated that SCIExplorer is capable to highlight meaningful and interpretable information for domain experts, without the need for specifying complex queries in a formal language such as SPARQL. SCIExplorer is automatically derived from a data-describing ontology, assuming a particular view on the underlying RDF data pool. We envisage two main directions for further development of SCIExplorer: first, adapting the tool to different research domains; second, increasing the degrees of freedom in the exploration process (e.g., by providing alternative entry points), while keeping the complexity faced by the user at a tractable level.