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

Corrado Grappiolo; Emile van Gerwen; J.P.C. Verhoosel; Lou Somers

doi:10.1145/3295750.3298915

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

Corrado Grappiolo
, Emile van Gerwen
, J.P.C. Verhoosel
, Lou Somers

Software Engineering and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

5 Citations (Scopus)

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 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.

Original language	English
Title of host publication	CHIIR '19 Proceedings of the 2019 Conference on Human Information Interaction and Retrieval
Publisher	Association for Computing Machinery, Inc.
Pages	355-359
Number of pages	5
ISBN (Electronic)	978-1-4503-6025-8
DOIs	https://doi.org/10.1145/3295750.3298915
Publication status	Published - 8 Mar 2019
Event	ACM SIGIR Conference on Human Information Interaction and Retrieval 2019 - Glasgow, United Kingdom Duration: 10 Mar 2019 → 14 Mar 2019 https://sigir.org/chiir2019/

Conference

Conference	ACM SIGIR Conference on Human Information Interaction and Retrieval 2019
Abbreviated title	CHIIR'19
Country/Territory	United Kingdom
City	Glasgow
Period	10/03/19 → 14/03/19
Internet address	https://sigir.org/chiir2019/

Keywords

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

Access to Document

10.1145/3295750.3298915

Cite this

Grappiolo, C., van Gerwen, E., Verhoosel, J. P. C., & Somers, L. (2019). The semantic snake charmer search engine: a tool to facilitate data science in high-tech industry domains. In CHIIR '19 Proceedings of the 2019 Conference on Human Information Interaction and Retrieval (pp. 355-359). Association for Computing Machinery, Inc.. https://doi.org/10.1145/3295750.3298915

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title = "The semantic snake charmer search engine: a tool to facilitate data science in high-tech industry domains",

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 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. ",

keywords = "Document Classification, Human-computer Collaboration, Natural Language Processing, Reinforcement Learning, Search Engine, Semantic Graph",

author = "Corrado Grappiolo and \{van Gerwen\}, Emile and J.P.C. Verhoosel and Lou Somers",

year = "2019",

month = mar,

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doi = "10.1145/3295750.3298915",

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pages = "355--359",

booktitle = "CHIIR '19 Proceedings of the 2019 Conference on Human Information Interaction and Retrieval",

publisher = "Association for Computing Machinery, Inc.",

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}

Grappiolo, C, van Gerwen, E, Verhoosel, JPC & Somers, L 2019, The semantic snake charmer search engine: a tool to facilitate data science in high-tech industry domains. in CHIIR '19 Proceedings of the 2019 Conference on Human Information Interaction and Retrieval. Association for Computing Machinery, Inc., pp. 355-359, ACM SIGIR Conference on Human Information Interaction and Retrieval 2019, Glasgow, United Kingdom, 10/03/19. https://doi.org/10.1145/3295750.3298915

The semantic snake charmer search engine: a tool to facilitate data science in high-tech industry domains. / Grappiolo, Corrado; van Gerwen, Emile; Verhoosel, J.P.C. et al.
CHIIR '19 Proceedings of the 2019 Conference on Human Information Interaction and Retrieval. Association for Computing Machinery, Inc., 2019. p. 355-359.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Grappiolo, Corrado

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AU - Verhoosel, J.P.C.

AU - Somers, Lou

PY - 2019/3/8

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N2 - 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.

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KW - Document Classification

KW - Human-computer Collaboration

KW - Natural Language Processing

KW - Reinforcement Learning

KW - Search Engine

KW - Semantic Graph

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DO - 10.1145/3295750.3298915

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PB - Association for Computing Machinery, Inc.

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ER -

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

Abstract

Conference

Keywords

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