Title
The Semantic Snake Charmer Search Engine: A Tool to Facilitate Data Science in High-tech Industry Domains
Author
Grappiolo, C.
Verhoosel, J.
van Gerwen, E.
Somers, L.
Publication year
2018
Abstract
The booming popularity of data science is also affecting high-tech industries. However, since these usually have different core competencies — building cyber-physical systems rather than e.g. machine learning or data mining algorithms — delving into data science by domain experts such as system engineers or architects might be more cumbersome than expected. In order to help domain experts to delve into data science we designed the Semantic Snake Charmer (SSC), a domain knowledgebased search engine for Jupyter Notebooks. SSC is composed of three modules: (1) a human-machine cooperative module to identify internal documentation which contains the most relevant domain knowledge, (2) a natural language processing module capable of transforming relevant documentation into several semantic graph types, (3) a reinforcement-learning based search engine which learns, given user feedback, the best mapping between input queries and semantic graph type to rely on. We believe SSC can be a fundamental asset to allow the easy landing of data science in industrial domains.
Subject
Informatics
Industrial Innovation
Reinforcement learning
Natural language processing
Semantic graph
Search engine
Document classification
Human-computer collaboration
To reference this document use:
http://resolver.tudelft.nl/uuid:d0f8ca47-7fae-4840-9b91-4c1bac96ca9c
TNO identifier
843090
Publisher
ACM New York, New York
Bibliographical note
4th ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR)
Document type
conference paper