The semantic snake charmer search engine: A tool to facilitate data science in high-tech industry domains

conference paper
2019
Grappiolo, C.

Gerwen, M.J.A.M. van

Verhoosel, J.P.C.

Somers, L.
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 knowledge-based 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. © 2019 Copyright held by the owner/author(s).
Topics
Document ClassificationHuman-computer CollaborationNatural Language ProcessingReinforcement LearningSearch EngineSemantic GraphData miningData ScienceEmbedded systemsGraphic methodsInformation retrieval systemsKnowledge based systemsLearning algorithmsMachine learningSemantic WebSemanticsCore competenciesDocument ClassificationDomain knowledgeHigh tech industryHuman-computer collaboration
TNO Identifier
865905
Repository link
https://resolver.tno.nl/uuid:f0217d73-2803-4067-8c1e-31b26b7ee326
ISBN
9781450360258
Publisher
Association for Computing Machinery, Inc
Source title
CHIIR 2019 - Proceedings of the 2019 Conference on Human Information Interaction and Retrieval, 4th ACM SIGIR Conference on Information Interaction and Retrieval, CHIIR 2019, 10 March 2019 through 14 March 2019
Pages
355-359
Files
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