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Scalable rule-based inference on Semantic Web data for event reasoning

Scientific Area:

  • Knowledge representation and reasoning

  • Semantic Web

  • Scalable Databases

  • Rule-based logical reasoning

Description of Activities:

Aim of this research activity is to investigate techniques for supporting efficient rule-based reasoning on complex event knowledge over large collections of Semantic Web data.

Event data may be automatically extracted from heterogeneous and diverse knowledge sources: thus modelling and inferring new knowledge starting from such data have to consider the challenge of dealing with possibly incomplete, contradictory and incorrect information.

The proposed activities will be carried on within the context of a platform for SPARQL based rule reasoning, called SPRINGLES (SParql-based Rule Inference over Named Graphs Layer Extending Sesame), currently developed in the DKM unit at FBK. The internship/thesis will be mostly aimed at the development and optimization of the platform supporting the inferences, thus experience in the implemented reasoning methods is not strictly required.

The internship/thesis will involve the extension of SPRINGLES in one or more of the following directions (to be discussed and agreed with the candidate):

  • SPR1: scalability over large quantity of (event) data. The data relative to events is not only complex and heterogeneous, but typically large quantity of knowledge has to be managed. One direction to the development of SPRINGLES regards the investigation and integration of efficient storage solutions (i.e. triple stores) as a backend to the asserted and inferred knowledge, also considering efficiency of execution of queries and rules.

  • SPR2: definition of rules for reasoning on complex events. The representation of complex event structures calls for the definition of structured (and efficient) rules for the computation of inferences. Thus, one of the needed extension to the framework is the definition of rulesets that are capable of inferring new facts from the input knowledge and their implementation in the SPARQL based rule language interpreted by SPRINGLES.

  • SPR3: integration of different reasoning components. Different rule based reasoners (other than the current SPARQL based solutions) can be plugged in SPRINGLES to actuate inferences over the input data. An activity towards the extension of SPRINGLES thus regards the identification of relevant reasoning components and implementing their integration in the current SPRINGLES architecture.

Required Skills and knowledge:

  • Good Java programming skills;

  • Interest (or basic knowledge) in Semantic Web languages and technologies: RDF, OWL, SPARQL;

  • Willingness to study new, challenging research topics and technologies;

  • Commitment to work in a research-driven environment;

  • Problem solving attitude and proactiveness in searching for solutions.

Competencies to be Acquired:

  • Participation to the R&D activities of a leading EU research institute;

  • Acquisition of advanced knowledge and skills in Semantic Web technologies;

  • Acquisition of notions of logical reasoning and logic programming solutions;

  • Contribution to the development of a state-of-the-art research-driven tool.

Duration: 3 to 6 months approximately, based on the planned activities.

Preferential Background: Computer Science, ICT

Selection Procedure: A short task-based assessment will be conducted at the beginning of the internship/thesis to assess the skills and capabilities of the student in accomplishing the planned activity.

Contact Person: Dr. Loris Bozzato (