The Semantic Snake Charmer Search Engine A Tool to Facilitate Data Science in High-tech Industry Domains

被引：1

作者：

Grappiolo, Corrado ^{[1
]}

van Gerwen, Emile ^{[1
]}

Verhoosel, Jack ^{[2
]}

Somers, Lou ^{[3
]}

机构：

[1] ESI TNO, Eindhoven, Netherlands

[2] Data Sci Dept TNO, Soesterberg, Netherlands

[3] Oce Technol, Venlo, Netherlands

来源：

PROCEEDINGS OF THE 2019 CONFERENCE ON HUMAN INFORMATION INTERACTION AND RETRIEVAL (CHIIR'19) | 2019年

关键词：

Reinforcement Learning; Natural Language Processing; Semantic Graph; Search Engine; Document Classification; Human-computer Collaboration;

D O I：

10.1145/3295750.3298915

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：355 / 359

页数：5