Evaluation approaches of personal knowledge graphs

Hanieh Khorashadizadeh; Frederic Ieng; Morteza Ezzabady; Soror Sahri; Sven Groppe; Farah Benamara

Evaluation approaches of personal knowledge graphs

Hanieh Khorashadizadeh^*, Frederic Ieng, Morteza Ezzabady, Soror Sahri, Sven Groppe, Farah Benamara

^*Corresponding author for this work

Abstract

Knowledge graphs (KGs) provide structured data for users' applications such as recommendation systems, personal assistants, and question-answering systems. The quality of the underlying applications relies deeply on the quality of the knowledge graph. However, KGs inevitably have inconsistencies, such as duplicates, wrong assertions, and missing values. The presence of such issues may compromise the outcome of business intelligence applications. Hence, it is crucial and necessary to explore efficient and effective methods for tackling the evaluation of KGs. These techniques get much tougher when the KG deals with personal data, which is referred to as Personal Knowledge Graph (PKG). This chapter covers PKG's creation, population, and more importantly their evaluation from a data quality perspective.

Original language	English
Title of host publication	Personal Knowledge Graphs (PKGs) : Methodology, tools and applications
Number of pages	17
Publisher	Institution of Engineering and Technology
Publication date	01.01.2023
Pages	277-293
ISBN (Print)	9781839537011
ISBN (Electronic)	9781839537028
Publication status	Published - 01.01.2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 4 Quality Education
SDG 9 Industry, Innovation, and Infrastructure
SDG 11 Sustainable Cities and Communities
SDG 12 Responsible Consumption and Production
SDG 14 Life Below Water
SDG 15 Life on Land

Research Areas and Centers

Research Area: Intelligent Systems
Centers: Center for Artificial Intelligence Luebeck (ZKIL)

Cite this

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AU - Khorashadizadeh, Hanieh

AU - Ieng, Frederic

AU - Ezzabady, Morteza

AU - Sahri, Soror

AU - Groppe, Sven

AU - Benamara, Farah

PY - 2023/1/1

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AB - Knowledge graphs (KGs) provide structured data for users' applications such as recommendation systems, personal assistants, and question-answering systems. The quality of the underlying applications relies deeply on the quality of the knowledge graph. However, KGs inevitably have inconsistencies, such as duplicates, wrong assertions, and missing values. The presence of such issues may compromise the outcome of business intelligence applications. Hence, it is crucial and necessary to explore efficient and effective methods for tackling the evaluation of KGs. These techniques get much tougher when the KG deals with personal data, which is referred to as Personal Knowledge Graph (PKG). This chapter covers PKG's creation, population, and more importantly their evaluation from a data quality perspective.

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Evaluation approaches of personal knowledge graphs

Abstract

UN SDGs

Research Areas and Centers

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