Knowledge Graph Completeness: A Systematic Literature Review

Issa, Subhi; Adekunle, Onaopepo; Hamdi, Fayçal; Cherfi, Samira Si-Saïd; Dumontier, Michel; Zaveri, Amrapali

doi:10.1109/access.2021.3056622

Cited by 46 publications

(23 citation statements)

References 53 publications

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“…The authors introduce an interesting model with a knowledge-aware encoding layer obtained from a knowledge graph constructed by mental health experts demonstrating its efficacy through ablation studies. A downside of this kind of handcrafted database approach is the quality analysis, namely how to evaluate the completeness and correctness of the graph ( 50 ). In contrast, we propose a purely data-driven system where any existing relationships between concept words is extracted directly from the semantic and syntactic information present in the data.…”

Section: Discussionmentioning

confidence: 99%

A machine learning approach to identifying suicide risk among text-based crisis counseling encounters

et al. 2023

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IntroductionWith the increasing utilization of text-based suicide crisis counseling, new means of identifying at risk clients must be explored. Natural language processing (NLP) holds promise for evaluating the content of crisis counseling; here we use a data-driven approach to evaluate NLP methods in identifying client suicide risk.MethodsDe-identified crisis counseling data from a regional text-based crisis encounter and mobile tipline application were used to evaluate two modeling approaches in classifying client suicide risk levels. A manual evaluation of model errors and system behavior was conducted.ResultsThe neural model outperformed a term frequency-inverse document frequency (tf-idf) model in the false-negative rate. While 75% of the neural model’s false negative encounters had some discussion of suicidality, 62.5% saw a resolution of the client’s initial concerns. Similarly, the neural model detected signals of suicidality in 60.6% of false-positive encounters.DiscussionThe neural model demonstrated greater sensitivity in the detection of client suicide risk. A manual assessment of errors and model performance reflected these same findings, detecting higher levels of risk in many of the false-positive encounters and lower levels of risk in many of the false negatives. NLP-based models can detect the suicide risk of text-based crisis encounters from the encounter’s content.

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Section: Discussionmentioning

confidence: 99%

A machine learning approach to identifying suicide risk among text-based crisis counseling encounters

et al. 2023

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“…We first searched for recent survey papers to compare different survey methodologies [136,65,131,2] to find the most suitable survey methodology for this paper. We follow the methodology of Kitchenham B.…”

Section: Survey Methodologymentioning

confidence: 99%

Declarative RDF graph generation from heterogeneous (semi-)structured data: A systematic literature review

Assche

Delva

Haesendonck

et al. 2023

Journal of Web Semantics

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“…RDF quality tools aim at investigating and quantifying the quality of a KG and the parameters influencing such quality [45]. To this end, the literature counts with proposals of different nature: from tools [13] to metrics [80], and other approaches [83].…”

Section: Knowledge Graph Curationmentioning

confidence: 99%

Helio: A framework for implementing the life cycle of knowledge graphs

Cimmino

García‐Castro

2024

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Building and publishing knowledge graphs (KG) as Linked Data, either on the Web or in private companies, has become a relevant and crucial process in many domains. This process requires that users perform a wide number of tasks conforming to the life cycle of a KG, and these tasks usually involve different unrelated research topics, such as RDF materialisation or link discovery. There is already a large corpus of tools and methods designed to perform these tasks; however, the lack of one tool that gathers them all leads practitioners to develop ad-hoc pipelines that are not generic and, thus, non-reusable. As a result, building and publishing a KG is becoming a complex and resource-consuming process. In this paper, a generic framework called Helio is presented. The framework aims to cover a set of requirements elicited from the KG life cycle and provide a tool capable of performing the different tasks required to build and publish KGs. As a result, Helio aims at providing users with the means for reducing the effort required to perform this process and, also, Helio aims to prevent the development of ad-hoc pipelines. Furthermore, the Helio framework has been applied in many different contexts, from European projects to research work.

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Knowledge Graph Completeness: A Systematic Literature Review

Cited by 46 publications

References 53 publications

A machine learning approach to identifying suicide risk among text-based crisis counseling encounters

A machine learning approach to identifying suicide risk among text-based crisis counseling encounters

Declarative RDF graph generation from heterogeneous (semi-)structured data: A systematic literature review

Helio: A framework for implementing the life cycle of knowledge graphs

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