[CLOSED] Internship: Evaluating explanations for semantic e-Science applications

Topic: The digital facilitation of science known as e-Science is the process of carrying out scientific investigations by exploiting large computing infrastructures for processing very large scientific datasets, and for enabling global collaborations. Common requirements in e-Science include scientific data and method sharing, repurposing, and experiment reproducibility. In addition, understanding the meanings of the data, and relations among the concepts in the data are important to deal with the heterogeneous scientific data. In e-Science areas such as life science, marine science, and healthcare, semantic web technologies have been successfully applied to address the above mentioned issues [1]. In the context of e-Science, semantic web technologies facilitate scientific knowledge modeling, logic-based hypothesis checking, data integration, application composition, knowledge discovery, and data analysis for different domains, for use by scientists, students, and non-experts.

However, in collaborative e-Science, and more generally in the open environment of the web, users have various backgrounds - an expert scientist may not have the expert level knowledge of knowledge engineering. In such a scenario, a user may find it difficult to understand the reasoning process behind producing the data, or to identify the cause of an unexpected event. To address these problems, researchers [2] have proposed that semantic web applications should explain their reasoning - explaining why an application has performed a step or which information it has used to derive its results. Goals of these explanations can be aspects such as trust, transparency, user satisfaction, decision efficiency, decision effectiveness [3].
In the context of this internship, we will investigate and evaluate the explanation goals for a semantic e-Science scenario. This scenario would cover data selection and data integration from multiple life science data sources [4] using federated SPARQL query processing [5]. Explanations in this scenario would enable the end-users to better interpret the results of the data selection and data integration steps. More precisely, we will address the following questions: (a) what are the goals of the explanations in our e-Science scenario? (b) what type of information we should include in the explanations to achieve those explanation goals? (c) how can we evaluate the impact of the explanations on scientific decision making?
The internship includes the following high level tasks:

  1. Survey of the related literature on explanation goals, semantic e-Science, and distributed query solving
  2. Analyzing the explanation requirements of the selected scenario
  3. Implementing explanation user interfaces and integrating them with the existing prototype
  4. Designing scenarios for evaluating explanation goals and implementing user interfaces required for the evaluation scenarios
  5. User study to evaluate explanation goals


  1. Front end software engineering skills for the web: AJAX, HTML and CSS
  2. Deep understanding of the web architecture
  3. Proficiency in Java
  4. An interest in the semantic web
  5. Fluency in English
  6. Currently enrolled in a Master program in computer science or a related discipline

Start date: February 2014 (negotiable)
Duration: 6 months
Salary: Average net salary of 1100€/month
Location: Sophia Antipolis, France
Application:  Please send your CV and transcripts to Rakebul Hasan<hasan.rakebul@inria.fr>
About the team
Wimmics is a joint research team between INRIA Sophia Antipolis - Méditerranée and I3S (CNRS and Université Nice Sophia Antipolis). Our research areas are graph-oriented knowledge representation, reasoning and operationalization to model and support actors, actions and interactions in web-based epistemic communities. The application of our research is supporting and fostering interactions in online communities and management of their resources.
[1] Fox, Peter, and James A. Hendler. "Semantic eScience: encoding meaning in next-generation digitally enhanced science." The Fourth Paradigm 2 (2009).
[2] R. Hasan and F. Gandon, "A Brief Review of Explanation in the Semantic Web", Workshop on Explanation-aware Computing (ExaCt 2012), European Conference on Artificial Intelligence (ECAI 2012), Montpellier, France.
[3] Tintarev, Nava, and Judith Masthoff. "Evaluating the effectiveness of explanations for recommender systems." User Modeling and User-Adapted Interaction 22.4-5 (2012): 399-439.
[4] http://fedbench.fluidops.net/resource/Datasets#Life_Science_Domain
[5] http://wimmics.inria.fr/corese