Data Science Track

What do knowledge graphs and statistical learning techniques like word2vec have in common? One could argue that both techniques produce similar outcomes: models that identify and describe the semantics of real-world concepts and their context. However, the underlying technical approach is fundamentally different. Knowledge graphs are typically curated manually or extracted from larger unstructured knowledge bases; concepts are identified via URIs and their context is defined via explicitly modelled relationships. Statistical models, in contrast, are trained on large corpus of text and produce a space in which concepts are “identified” by a corresponding vector; concept relationships are represented implicitly and can be computed using vector arithmetic.

What can you expect?


This year’s data science track will again target the intersection between Data Science and Semantics research and kick-off with a keynote talk by Alan Hanbury who will shed some light on how lexical and statistical semantics can be used to improve search results.

Contributions to the data science track also recognize that even the most sophisticated analytics or machine learning task still obeys the fundamental computing law of “garbage in, garbage out”, which means that the best model will be limited by flaws or misinterpretations in the training dataset. Therefore, this track will also feature a number of papers highlighting the importance of well-defined structured vocabularies for data analytics and learning tasks.
 

Welcome


We, the chairs of the overall data science tracks are happy that the Data Science track attracted more than enough interest to be included in this year’s program of SEMANTiCS. We are looking forward to meeting and exchanging ideas with people who share similar interests and are convinced that this year’s SEMANTiCS will be a good place to start these discussions.

Bernhard Haslhofer and Laura Hollnik and Alexander Schindler
Data Science Track Chairs

The Track


Wednesday, 12.09.2018 [preliminary]
   
  Danube Suite 3
10:30- 12:00 Session 1.6: Data Science
  Data Science
  Chair
  R+I1002
  Allan Hanbury

Lexical and Statistical Semantics in Professional Search - TBD
  R+I55
  Said Fathalla, Sahar Vahdati, Sören Auer and Christoph Lange

SemSur: A Core Ontology for the Semantic Representation of Research Findings - University of Bonn, TIB Leibniz Information Center, Fraunhofer IAIS
  IND03
  Maarten Dammers

WIkidata: The Linked Open Data hub - Wikimedia
13:10 - 14:30 LUNCH BREAK
   
16:05 - 17:35 Danube Suite 3
  Session 2.6 Data Science
  Data Science & Knowledge Discovery
  Chair
  R+I43
  Andreas Ekelhart, Elmar Kiesling and Kabul Kurniawan

Taming the logs - Vocabularies for semantic security analysis - SBA Research, Technical University of Technology,
  R+I59
  Vincent Lully, Philippe Laublet, Milan Stankovic and Filip Radulovic

Exploring the synergy between knowledge graph and computer vision for personalisation systems - Sorbonne Université, Sépage
  R+I49
  Abdullah Ahmed, Mohamed Sherif and Axel-Cyrille Ngonga Ngomo

On the Effect of Geometries Simplification on Geo-spatial Link Discovery - Paderborn University