Py_ape: Text Data Acquiring, Extracting, Cleaning and Schema Matching in Python

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10246988" target="_blank" >RIV/61989100:27240/20:10246988 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007%2F978-981-33-4370-2_6" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-981-33-4370-2_6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-33-4370-2_6" target="_blank" >10.1007/978-981-33-4370-2_6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Py_ape: Text Data Acquiring, Extracting, Cleaning and Schema Matching in Python

Popis výsledku v původním jazyce

Py_ape is a package in Python that integrates a number of string and text processing algorithms for collecting, extracting, and cleaning text data from websites, creating frames for text corpora, and matching entities, matching two schemas, mapping and merging two schemas. The functions of Py_ape help the user step-by-step perform data integration and data preparation, based on some popular Python libraries. Especially in the entity matching function of the schema matching and merging phase, we used the Hamming distance algorithm to identify similar string pairs, and the longest common substring similarity algorithm to map data between the columns of schemas. These algorithms help to increase the accuracy of the schema matching process. In addition, in the article, we present experimental results using Py_ape to scrape, clean, match, and merge two sets of data related to aviation crashes, taken from different sources of Kaggle and Wikipedia. The result of the experiment will be evaluated in detail in the rest of the paper. (C) 2020, Springer Nature Singapore Pte Ltd.

Název v anglickém jazyce

Py_ape: Text Data Acquiring, Extracting, Cleaning and Schema Matching in Python

Popis výsledku anglicky

Py_ape is a package in Python that integrates a number of string and text processing algorithms for collecting, extracting, and cleaning text data from websites, creating frames for text corpora, and matching entities, matching two schemas, mapping and merging two schemas. The functions of Py_ape help the user step-by-step perform data integration and data preparation, based on some popular Python libraries. Especially in the entity matching function of the schema matching and merging phase, we used the Hamming distance algorithm to identify similar string pairs, and the longest common substring similarity algorithm to map data between the columns of schemas. These algorithms help to increase the accuracy of the schema matching process. In addition, in the article, we present experimental results using Py_ape to scrape, clean, match, and merge two sets of data related to aviation crashes, taken from different sources of Kaggle and Wikipedia. The result of the experiment will be evaluated in detail in the rest of the paper. (C) 2020, Springer Nature Singapore Pte Ltd.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Communications in Computer and Information Science. Volume 1306

ISBN

978-981-334-369-6

ISSN

1865-0929

e-ISSN

1865-0937

Počet stran výsledku

12

Strana od-do

78-89

Název nakladatele

Springer

Místo vydání

Singapur

Místo konání akce

Quy Nhon

Datum konání akce

25. 11. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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