Mind the semantic gap: How "talking semantics" can help you perform better data science
Who is this presentation for?
- Data scientists and analytics professionals
Panos Alexopoulos frequently hears, “Now you’re talking semantics” from 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. He argues in favor of more semantic rigorousness in data modeling and science by describing real use cases where such rigorousness helped significantly improve the effectiveness of the data-intensive applications.
You’ll explore how dissecting and measuring the different types of ambiguity improved the effectiveness of an entity recognition and disambiguation system, how specializing and contextualizing the notion of semantic relatedness helped mine and deliver more accurate domain knowledge to the semantic search system, and how digging deeper and challenging the semantics of publicly available data sources helped avoid ingesting data into the systems that would be nonuseful or even harmful. You’ll leave with a good idea of how data semantics are capable of both benefiting and harming a data science task. More importantly, you’ll learn basic semantic analysis techniques that will help you improve the effectiveness of your own projects, not by increasing data volume or algorithm complexity, but just by paying a bit more attention to the data and task semantics.
- Experience with real-world (i.e., nonacademic) data, ideally for text and language analysis tasks
What you'll learn
- Identify ways data semantics are capable of benefiting and harming a data science task
- Learn basic semantic thinking techniques that will help you improve the effectiveness of your projects, not by increasing data volume or algorithm complexity, but just by paying a bit more attention to the data and task semantics
Panos Alexopoulos is the head of ontology at Textkernel, 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. Born and raised in Athens, Greece, and living in Amsterdam, Netherlands, he’s 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. Previously, he was a semantic applications research manager at Expert System and a semantic solutions architect and ontologist at IMC Technologies. Panos holds a PhD in knowledge engineering and management from the National Technical University of Athens, and has published ~60 papers at international conferences, journals, and books. He strives 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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