Normalization Techniques For Improving The Performance Of Knowledge Graph Creation Pipelines

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dc.identifier.uri http://dx.doi.org/10.15488/10081
dc.identifier.uri https://www.repo.uni-hannover.de/handle/123456789/10143
dc.contributor.advisor Vidal, Maria-Esther
dc.contributor.advisor Jozashoori, Samaneh
dc.contributor.author Torabinejad, Mohammad ger
dc.date.accessioned 2020-10-01T14:00:17Z
dc.date.available 2020-10-01T14:00:17Z
dc.date.issued 2020
dc.identifier.citation Torabinejad, Mohammad: Normalization Techniques For Improving The Performance Of Knowledge Graph Creation Pipelines. Hannover : Gottfried Wilhelm Leibniz Universität Hannover, Master-Thesis, 2020, X, 61 S. DOI: https://doi.org/10.15488/10081 ger
dc.description.abstract With the rapid growth of data within the web, demands on discovering information within data and consecutively exploiting knowledge graphs rise much more than we think it does. Data integration systems can be of great help to meet this precious demand in that they offer transformation of data from various sources and with different volumes. To this end, a data integration system takes advantage of utilizing mapping rules-- specified in a language like RML -- to integrate data collected from various data sources into a knowledge graph. However, large data sources may suffer from various data quality issues, being redundant one of them. Regarding this, the Semantic Web community contributes to Knowledge Engineering with techniques to create a knowledge graph efficiently. The thesis reported in this document tackles creating knowledge graphs in the presence of data sources with redundant data, and a novel normalization theory is proposed to solve this problem. This theory covers not only the characteristics of the data sources but also mapping rules used to integrate the data sources into a knowledge graph. Based on this, three normal forms are proposed and an algorithm for transforming mapping rules and data sources into these normal forms. The proposed approach's performance is evaluated in different testbeds composed of real-world data and synthetic data. The observed results suggest that the proposed techniques can dramatically reduce the execution time of knowledge graph creation. Therefore, this thesis's normalization theory contributes to the repertoire of tools that facilitate the creation of knowledge graphs at scale. eng
dc.language.iso eng ger
dc.publisher Hannover : Gottfried Wilhelm Leibniz Universität Hannover
dc.rights Es gilt deutsches Urheberrecht. Das Dokument darf zum eigenen Gebrauch kostenfrei genutzt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden. ger
dc.subject Database eng
dc.subject Normalization eng
dc.subject Mapping rules eng
dc.subject Knowledge Graph eng
dc.subject Data Integration System eng
dc.subject Normalisierung ger
dc.subject Mapping-Regeln ger
dc.subject Wissensdatenbank ger
dc.subject Informationsintegration ger
dc.subject Datenbank ger
dc.subject.classification Datenbank ger
dc.subject.classification Information Retrieval ger
dc.subject.classification Semantisches Netz ger
dc.subject.classification Netzwerk ger
dc.subject.classification Wissensbasiertes System ger
dc.subject.ddc 004 | Informatik ger
dc.title Normalization Techniques For Improving The Performance Of Knowledge Graph Creation Pipelines eng
dc.type masterThesis ger
dc.type Text ger
dc.description.version publishedVersion ger
tib.accessRights frei zug�nglich ger


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