Sep 23–26, 2019

Mind the Semantic Gap: How “talking semantics” can help you perform better data science

Panos Alexopoulos (Textkernel BV)
2:05pm2:45pm Wednesday, September 25, 2019
Location: 3B - Expo Hall
Secondary topics:  Text and Language processing and analysis

Who is this presentation for?

Data Science and Analytics professionals that work

Level

Intermediate

Prerequisite knowledge

The attendees should have some few years of experience working with real-world (i.e., non-academic) data, ideally with for text and language analysis tasks.

What you'll learn

The attendees of this talk will get a good idea of how data semantics are capable of both benefiting and harming a data science task. More importantly, they will learn basic semantic thinking techniques that will help them improve the effectiveness of their own projects, not by increasing data volume or algorithm complexity, but just by paying a bit more attention to the data and task semantics.

Description

“Now you are talking semantics” is a reaction I often get by some data practitioners who believe that statistical reasoning and machine learning are all that’s needed to tackle semantic-related data science tasks, and that spending too much time in understanding and modeling those semantics is a waste of effort and resources. In this talk, I argue in favour of more semantic rigorousness in data modeling and science by describing real use cases where such rigorousness helped us significantly improve the effectiveness of data-intensive applications. In particular, I will describe:

  • How dissecting and measuring the different types of ambiguity allowed us to improve the effectiveness of an entity recognition and disambiguation system.
  • How specializing and contextualizing the notion of semantic relatedness helped us mine and deliver more accurate domain knowledge to our Semantic search system.
  • How digging deeper and challenging the semantics of publicly available data sources, helped us avoid ingesting into our systems data that would be non-useful or even harmful for them.

The attendees of this talk will get a good idea of how data semantics are capable of both benefiting and harming a data science task. More importantly, they will .learn basic semantic analysis techniques that will help them improve the effectiveness of their own projects, not by increasing data volume or algorithm complexity, but just by paying a bit more attention to the data and task semantics.

Photo of Panos Alexopoulos

Panos Alexopoulos

Textkernel BV

Panos Alexopoulos has been working for more than 12 years at the intersection of data, semantics, language and software, contributing in building semantics-powered systems that deliver value to business and society. Born and raised in Athens, Greece, Panos currently works as Head of Ontology at Textkernel, in Amsterdam, Netherlands, where he leads a team of data professionals (Linguists, Data Scientists and Data Engineers) in developing and delivering a large cross-lingual Knowledge Graph in the HR and Recruitment domain. Prior to Textkernel, he worked at Expert System Iberia (former iSOCO) in Madrid, Spain, as a Semantic Applications Research Manager, and at IMC Technologies in Athens, Greece, as a Semantic Solutions Architect and Ontologist.

Academically, Panos holds a PhD in Knowledge Engineering and Management from National Technical University of Athens, and has published ~60 papers at international conferences, journals and books. He strives though to present his work and experiences in all kinds of venues, trying to bridge the gap between academia and industry so that they can benefit from one another.

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