Mobile link prediction: Automated creation and crowdsourced validation of knowledge graphs

Ballandies, Mark Christopher; Pournaras, Evangelos

doi:10.1016/j.micpro.2021.104335

Cited by 5 publications

(3 citation statements)

References 72 publications

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“…Generally, semi-automated and human leveraging mechanisms, which can be applied to ensure the quality of knowledge graphs, are essential tools for the evaluation of knowledge graph completion. Specifically, human supervision is currently considered the gold standard evaluation in knowledge graph completion (Ballandies and Pournaras 2021 ).…”

Section: Technical Challengesmentioning

confidence: 99%

Knowledge Graphs: Opportunities and Challenges

et al. 2023

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With the explosive growth of artificial intelligence (AI) and big data, it has become vitally important to organize and represent the enormous volume of knowledge appropriately. As graph data, knowledge graphs accumulate and convey knowledge of the real world. It has been well-recognized that knowledge graphs effectively represent complex information; hence, they rapidly gain the attention of academia and industry in recent years. Thus to develop a deeper understanding of knowledge graphs, this paper presents a systematic overview of this field. Specifically, we focus on the opportunities and challenges of knowledge graphs. We first review the opportunities of knowledge graphs in terms of two aspects: (1) AI systems built upon knowledge graphs; (2) potential application fields of knowledge graphs. Then, we thoroughly discuss severe technical challenges in this field, such as knowledge graph embeddings, knowledge acquisition, knowledge graph completion, knowledge fusion, and knowledge reasoning. We expect that this survey will shed new light on future research and the development of knowledge graphs.

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Section: Technical Challengesmentioning

confidence: 99%

Knowledge Graphs: Opportunities and Challenges

et al. 2023

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“…The continuous expansion and evolution of real-world networks, such as social networks [13,26], communication networks [1,3], and biomedical networks [29,33,35], have introduced new challenges and complexities. These networks, due to their size and computational demands, have transformed traditional graph theory concepts [4,34] .…”

Section: Introductionmentioning

confidence: 99%

“…Numerous measures [7,11,18,23,25], methods [13, 15, 17, 23-26, 29, 32], and algorithms [3,5,10,14,[23][24][25] collectively known as "link prediction measures" in this article have been proposed to tackle this issue. These measures assign higher scores to edges with greater potential for communication or linkage.…”

Section: Introductionmentioning

confidence: 99%

Introducing new Link Prediction Measures and an Evaluation Metric

Javan,

Moeini,

Ghodousian

2024

Preprint

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Link prediction plays a critical role in network analysis as it tackles the task of predicting missing or future connections within a given network. A wide array of link prediction measures has been proposed to estimate the likelihood of link existence between nodes, aiming to uncover hidden relationships and anticipate the formation of new connections. This task holds particular significance in various domains, including social networks, biological networks, transportation networks, and recommender systems. Nonetheless, evaluating the effectiveness of these measures poses challenges, encompassing both the measures themselves and the employed evaluation metrics. This article offers a comprehensive overview of the existing link prediction measures, shedding light on their limitations and addressing the associated challenges in evaluating their efficacy. Moreover, we propose enhancements to overcome these challenges, entailing modifications to existing measures and the introduction of novel evaluation metrics. By tackling these issues head-on, our objective is to enhance the accuracy and reliability of link prediction in network analysis.

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To Incentivize or Not: Impact of Blockchain-Based Cryptoeconomic Tokens on Human Information Sharing Behavior

Ballandies

2022

IEEE Access

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Cryptoeconomic incentives in the form of blockchain-based tokens are seen as an enabler of the sharing economy that could shift society towards greater sustainability. Nevertheless, knowledge of the impact of these tokens on human sharing behavior is still limited and this poses a challenge to the design of effective cryptoeconomic incentives. This study applies the theory of self-determination to investigate the impact of such tokens on human behavior in an information-sharing scenario. By utilizing an experimental methodology in the form of a randomized control trial with a 2x2 factorial design involving 132 participants, the effects of two token incentives on human information-sharing behavior are analyzed. Individuals obtain these tokens in exchange for their shared information. Based on the collected tokens, individuals receive a monetary payment and build reputation. Besides investigating the effect of these incentives on the quantity of shared information, the study includes quality characteristics of the information, such as accuracy and contextualization. The focus on quantity while excluding quality has been identified as a limitation in previous work. In addition to confirming previously known effects such as a crowding-out of intrinsic motivation by incentives, which also exists for blockchain-based tokens, the findings of this paper point to a hitherto unreported interaction effect between multiple tokens when applied simultaneously. The findings are critically discussed and put into the context of recent work and ethical considerations. The theory-basedempirical study is of interest to those investigating the effect of cryptoeconomic tokens or digital currencies on human behavior and supports the community in the design of effective personalized incentives for sharing economies.

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Mobile link prediction: Automated creation and crowdsourced validation of knowledge graphs

Cited by 5 publications

References 72 publications

Knowledge Graphs: Opportunities and Challenges

Knowledge Graphs: Opportunities and Challenges

Introducing new Link Prediction Measures and an Evaluation Metric

To Incentivize or Not: Impact of Blockchain-Based Cryptoeconomic Tokens on Human Information Sharing Behavior

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