Doctoral Seminar: Bahar Sateli

Speaker: Bahar Sateli

Supervisors: Dr. R. Witte

Examining Committee: Drs. V. Haarslev, F. Khendek, G. Lapalme, J. Rilling, G. Vatistas (Chair)

Title: A Parallel Computing Architecture for High-Performance OWL Reasoning

Date: Monday, April 09, 2018

Time: 10:00 a.m.

Place: EV 3.309

ABSTRACT

The unprecedented rate of scientific publications is a major threat to the productivity of knowledge workers, who rely on scrutinizing the latest scientific discoveries for their daily tasks. Online digital libraries, academic publishing databases and open access repositories grant access to a plethora of information that can overwhelm a researcher, who is looking to obtain fine-grained knowledge relevant for her task at hand. This overload of information has encouraged researchers from various disciplines to look for new approaches in extracting, organizing, and managing knowledge from the immense amount of available literature in ever-growing repositories.

In this dissertation, we introduce a Personal Research Agent that can help scientists in discovering, reading and learning from scientific documents, primarily in the computer science domain. We demonstrate how a confluence of techniques from the Natural Language Processing and Semantic Web domains can construct a knowledge base of semantically-rich, inter-connected graph of scholarly artifacts, effectively transforming scientific literature from written content in isolation, into a queryable web of knowledge, suitable for machine interpretation.

The challenge of creating an intelligent research agent is manifold: The agent’s knowledge base, analogous to his brain, must contain accurate information about the knowledge ‘stored’ in documents. It also needs to know about its end-users’ tasks and background knowledge. In our work, we present a methodology to extract the rhetorical structure (e.g., claims and contributions) of scholarly documents. We combine our approach with entity linking techniques that allow us to connect the documents with the Linked Open Data (LOD) cloud in order to enrich them with additional information from the web of open data. Furthermore, we devise a novel approach for automatic profiling of scholarly users, thereby, enabling the agent to personalize its services, based on a user’s background knowledge and interests. Finally, as part of our contributions, we present a complete architecture providing an end-to-end workflow for the agent to exploit the opportunities of linking a formal model of scholarly users and scientific publications.