Knowledge Graphs for Public Service Description: Τhe Case of Getting a Passport in Greece

Promikyridis, Rafail; Tambouris, Efthimios

doi:10.1007/978-3-030-63396-7_18

Cited by 8 publications

(2 citation statements)

References 12 publications

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“…In [17], the authors propose to construct a knowledge graph for the description of public services, the objective of the knowledge graph is to provide users with personalized information about different public services based on their profile and circumstances. The implementation of the knowledge graph was achieved by creating the schema defining entities, attributes, and relationships and populating the graph using GRAQL queries.…”

Section: Related Workmentioning

confidence: 99%

Knowledge Graph based Representation to Extract Value from Open Government Data

DAHBI¹,

CHIADMI²,

LAMHARHAR³

2023

IJACSA

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Open government data refers to data that is made available by government entities to be freely reused by anyone and for any purpose. The potential benefits of open government data are numerous and include increasing transparency and accountability, enhancing citizens' quality of life, and boosting innovation. However, realizing these benefits is not always straightforward, as the usage of this raw data often faces challenges related to its format, structure, and heterogeneity which hinder its processability and integration. In response to these challenges, we propose an approach to maximize the usage of open government data and achieve its potential benefits. This approach leverages knowledge graphs to extract value from open government data and drive the construction of a knowledge graph from structured, semi-structured, and non-structured formats. It involves the extraction, transformation, semantic enrichment, and integration of heterogeneous open government data sources into an integrated and semantically enhanced knowledge graph. Learning mechanisms and ontologies are used to efficiently construct the knowledge graph. We evaluate the effectiveness of the approach using real-world public procurement data and show that it can detect potential fraud such as favoritism.

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Section: Related Workmentioning

confidence: 99%

Knowledge Graph based Representation to Extract Value from Open Government Data

DAHBI¹,

CHIADMI²,

LAMHARHAR³

2023

IJACSA

View full text Add to dashboard Cite

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“…DKGs are widely used in medical (Li et al, 2020 ; Gong et al, 2021 ), financial (Song et al, 2017 ; Zhan and Yin, 2018 ; Chen and Xiang, 2020 ; Mao et al, 2022 ), scholarly research (Liu J. et al, 2020 ; Zhou et al, 2020 ; Kanakaris et al, 2021 ), tourism (Gao et al, 2020 ), disaster prevention (Du et al, 2020 ), e-government (Promikyridis and Tambouris, 2020 ), and other fields to provide a structured network knowledge base for the corresponding professionals. The data source of DKGs generally comes from richly preserved professional data in the domain, including structured data, semi-structured data, and unstructured data, such as free text and images.…”

Section: Related Workmentioning

confidence: 99%

A solution and practice for combining multi-source heterogeneous data to construct enterprise knowledge graph

Yan,

Fang,

Huang

et al. 2023

Front. Big Data

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The knowledge graph is one of the essential infrastructures of artificial intelligence. It is a challenge for knowledge engineering to construct a high-quality domain knowledge graph for multi-source heterogeneous data. We propose a complete process framework for constructing a knowledge graph that combines structured data and unstructured data, which includes data processing, information extraction, knowledge fusion, data storage, and update strategies, aiming to improve the quality of the knowledge graph and extend its life cycle. Specifically, we take the construction process of an enterprise knowledge graph as an example and integrate enterprise register information, litigation-related information, and enterprise announcement information to enrich the enterprise knowledge graph. For the unstructured text, we improve existing model to extract triples and the F1-score of our model reached 72.77%. The number of nodes and edges in our constructed enterprise knowledge graph reaches 1,430,000 and 3,170,000, respectively. Furthermore, for each type of multi-source heterogeneous data, we apply corresponding methods and strategies for information extraction and data storage and carry out a detailed comparative analysis of graph databases. From the perspective of practical use, the informative enterprise knowledge graph and its timely update can serve many actual business needs. Our proposed enterprise knowledge graph has been deployed in HuaRong RongTong (Beijing) Technology Co., Ltd. and is used by the staff as a powerful tool for corporate due diligence. The key features are reported and analyzed in the case study. Overall, this paper provides an easy-to-follow solution and practice for domain knowledge graph construction, as well as demonstrating its application in corporate due diligence.

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Semantic Annotation of Parliamentary Debates and Legislative Intelligence Enhancing Citizen Experience

Gagnon

Azzi²

2022

Electronic Government and the Information Systems Perspective

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Knowledge Graphs for Public Service Description: Τhe Case of Getting a Passport in Greece

Cited by 8 publications

References 12 publications

Knowledge Graph based Representation to Extract Value from Open Government Data

Knowledge Graph based Representation to Extract Value from Open Government Data

A solution and practice for combining multi-source heterogeneous data to construct enterprise knowledge graph

Semantic Annotation of Parliamentary Debates and Legislative Intelligence Enhancing Citizen Experience

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