20 Março 2017, 13:21 - Ana Maria de Almeida Nogueira Marques
Title: How to talk to your data: Scalable semantic interpretation techniques for heterogeneous data
Speaker: André Freitas, University of Passau, Germany
Date and time: Monday, April 3, 14:00 pm
Location: CSE meeting room (Informática II - Alameda), videocast to DSI room in Tagus.
The recent evolution of approaches, data resources and tools in the Natural Language Processing (NLP) and Artificial Intelligence (AI) fields brings the opportunity for theconstruction of data analysis methods and information systems which are able towork over unstructured, complex and semantically heterogeneous data. The ability to systematically structure, integrate, query and operate over unstructured and highly variable data at scale emerges as a strong demand across different fields which are dependent on analytical reasoning. In this talk we will describe contemporary techniques to automatically interpret the meaning of unstructured data at scale and the emerging formal and methodological data science foundations which support addressing the data variety dimension for Big Data scenarios. A particular emphasis will be given to the description of information extraction, knowledge representation and semantic parsing models and how these models can be combined to build systems that perform complex interpretation tasks such as Question Answering under real-world data conditions.
André Freitas is a research group leader and lecturer at the Natural Language Processing & Semantic Computing research group at the University of Passau in Germany. Before joining Passau, he was part of the Digital Enterprise Research Institute (DERI) at the National University of Ireland, Galway where he did his PhD on Schema-agnostic Query Mechanisms for Large-Schema Databases. André holds a BSc. in Computer Science from the Federal University of Rio de Janeiro (UFRJ), Brazil. His main research areas include Question Answering, Schema-agnostic Database Query Mechanisms, Natural Language Query Mechanisms over Large-Schema Databases, Distributional Semantics, Hybrid Symbolic-Distributional Models, Approximate Reasoning and Knowledge Graphs.