Explore the vast range of titles available.

The early concept of knowledge graph originates from the idea of the semantic Web, which aims at using structured graphs to model the knowledge of the world and record the relationships that exist between things. Currently publishing knowledge bases as open data on the Web has gained significant attention. In China, Chinese Information Processing Society of China (CIPS) launched the OpenKG in 2015 to foster the development of Chinese Open Knowledge Graphs. Unlike existing open knowledge-based programs, OpenKG chain is envisioned as a blockchain-based open knowledge infrastructure. This article introduces the first attempt at the implementation of sharing knowledge graphs on OpenKG chain, a blockchain-based trust network. We have completed the test of the underlying blockchain platform, and the on-chain test of OpenKG's data set and tool set sharing as well as fine-grained knowledge crowdsourcing at the triple level. We have also proposed novel definitions: K-Point and OpenKG Token, which can be considered to be a measurement of knowledge value and user value. 1,033 knowledge contributors have been involved in two months of testing on the blockchain, and the cumulative number of on-chain recordings triggered by real knowledge consumers has reached 550,000 with an average daily peak value of more than 10,000. For the first time, we have tested and realized on-chain sharing of knowledge at entity/triple granularity level. At present, all operations on the data sets and tool sets at OpenKG.CN, as well as the triplets at OpenBase, are recorded on the chain, and corresponding value will also be generated and assigned in a trusted mode. Via this effort, OpenKG chain looks forward to providing a more credible and traceable knowledge-sharing platform for the knowledge graph community.

Background The quality of Big Data analysis in medicine and surgery heavily depends on the methods used for clinical data collection, organization, and storage. The Knowledge Graph (KG) represents knowledge through a semantic model, enhancing connections between diverse and complex information. While it can improve the quality of health data collection, it has limitations that can be addressed by the Decentralized (blockchain-powered) Knowledge Graph (DKG). We report our experience in developing a DKG to organize data and knowledge in the field of emergency surgery. Methods and results The authors leveraged the cyb.ai protocol, a decentralized protocol within the Cosmos network, to develop the Emergency Surgery DKG. They populated the DKG with relevant information using publications from the World Society of Emergency Surgery (WSES) featured in the World Journal of Emergency Surgery (WJES). The result was the Decentralized Knowledge Graph (DKG) for the WSES-WJES bibliography. Conclusions Utilizing a DKG enables more effective structuring and organization of medical knowledge. This facilitates a deeper understanding of the interrelationships between various aspects of medicine and surgery, ultimately enhancing the diagnosis and treatment of different diseases. The system’s design aims to be inclusive and user-friendly, providing access to high-quality surgical knowledge for healthcare providers worldwide, regardless of their technological capabilities or geographical location. As the DKG evolves, ongoing attention to user feedback, regulatory frameworks, and ethical considerations will be critical to its long-term success and global impact in the surgical field.

With up to 30% of donated pharmaceutical aid products being diverted, stolen or going to waste in this session we will learn how blockchain technology is addressing some of the biggest trust challenges in the global supply chain.  Supply chain issues may prevent people that are in greatest need of life-saving medicines from being treated. Through the use of a Decentralized Knowledge Graph and blockchain technology firms and governmental aid organizations now have visibility to supply chain traceability and transparency of donated medicines. The session will additionally cover ancillary benefits with the implementation of such a solution.   

The integration of Internet of Things (IoT) devices in e-learning systems necessitates robust, scalable, and secure authentication procedures to provide dependable sharing of academic records among remote educational institutions. Conventional centralized systems experience scalability limitations, singular points of failure, and heightened susceptibility to hackers, especially in resource-limited IoT settings. This paper presents a decentralized authentication framework based on Hedera Hashgraph and Knowledge Graphs (KGs) to tackle these issues. The architecture incorporates a GAN-based cryptography module for the generation of dynamic symmetric keys, enhancing resistance against predictive and inference-based attacks. Knowledge Graphs facilitate semantic validation of identification features and improve interoperability among institutions via the Hedera Consensus Service (HCS). The quantitative assessment indicates that the Hedera + KG + GAN model attains a 17.1% increase in throughput, an 11–12% reduction in processing time, up to a 20% decrease in execution time for substantial data volumes, a 6–15% decline in energy consumption, and an approximate 23% reduction in authentication delay during periods of high network utilization relative to the leading competing frameworks. The suggested method provides a scalable, safe, and semantically enriched authentication mechanism for IoT-enabled e-learning ecosystems, creating a solid foundation for next-generation decentralized educational platforms.

Local differential privacy (LDP) is an emerging privacy-preserving data collection model that requires no trusted third party. Most privacy-preserving decentralized graph publishing studies adopt LDP technique to ensure individual privacy. However, existing LDP-based synthetic graph generation approaches focus on static graph publishing and can only republish synthetic graphs in a brute-force manner when dealing with dynamic graph problems, resulting in low synthetic graph accuracy. The main difficulties come from the two steps of dynamic graph publishing: excessive noise injection in initial graph generation and over-segmentation of the privacy budget in graph update. We address these two issues by presenting PPDU, the first dynamic graph publication approach under LDP. PPDU uses a privacy-preference-specifying mechanism to untie the noise injection and the graph size, significantly reducing noise injection. We then divide the privacy-preserving graph update problem into three subproblems: node insertion, edge insertion, and edge deletion, and propose update threshold-based dynamic graph releasing methods to avoid excessive segmentation of the privacy budget, thereby significantly improving the accuracy of synthetic graphs. Theoretical analysis and experimental results prove that our solution can continually yield high-quality dynamic graphs while satisfying edge LDP.

Federated social networks have become an appealing choice as alternatives to mainstream centralized platforms. In the current global context, where the user’s activity on various social networks is monitored, influenced and manipulated, alternative platforms that offer the possibility of owning and controlling one’s own data are of great importance. Mastodon stands out among decentralized alternatives in the fediverse. In this study, we conduct a time-based dynamics analysis of various Mastodon instances, from popular ones to country-specific servers. Moreover, we conducted an analysis of registration account dynamics based on certain topics, such as academic, political and activism in general. Throughout the paper, we reveal the user adoption of Mastodon from multiple instances and metrics. Our results show a growth pattern of instances in terms of accounts in certain periods of time, and due to social events, reinforcing our assumption of it being already trusted as a decentralized platform. Our work holds significance in the wider context of studying and understanding the adoption rates of decentralized networks as ethical alternatives to centrally controlled ones.

The rise of decentralized social networks (DSNs) necessitates robust trust models to ensure secure and reliable interactions in an increasingly dynamic online environment. While blockchain technology has revolutionized finance with cryptocurrencies and decentralized control, its potential to transform trust management in DSNs remains largely unexplored. This survey aims to fill this gap by offering a comprehensive analysis of recent advancements in blockchain-based trust models for social networks. Our work delves into the core concepts of trust, trust models, and trust prediction methodologies within the context of DSNs. We explore how trust is decentralized in social networks, their economic implications, and the architectural frameworks of these systems. We also examine the role of decentralization in fostering trust within these networks and how blockchain technology can further improve trust and security. The survey investigates the relationship between trust and blockchain technology, analyzing various blockchain types and their specific applications in DSNs. We categorize existing trust models based on relevant parameters and critically evaluate their strengths and weaknesses. Finally, we suggest promising future directions for researchers interested in advancing trust management in DSNs empowered by blockchain technology.

Anomaly detection plays a crucial role in data security and risk management across various domains, such as financial insurance security, medical image recognition, and Internet of Things (IoT) device management. Researchers rely on machine learning to address potential threats in order to enhance data security. In the financial insurance industry, enterprises tend to leverage the relation mining capabilities of knowledge graph embedding (KGE) for anomaly detection. However, auto insurance fraud labeling strongly relies on manual labeling by experts. The efficiency and cost issues of labeling make auto insurance fraud detection still a small-sample detection challenge. Existing schemes, such as migration learning and data augmentation methods, are susceptible to local characteristics, leading to their poor generalization performance. To improve its generalization, the recently emerging Decentralized Federated Learning (DFL) framework provides new ideas for mining more frauds through the joint cooperation of companies. Based on DFL, we propose a federated framework named DFLR for relation embedding aggregation. This framework trains the private KGE of auto insurance companies on the client locally and dynamically selects servers for relation aggregation with the aim of privacy protection. Finally, we validate the effectiveness of our proposed DFLR on a real auto insurance dataset. And the results show that the cooperative approach provided by DFLR improves the client’s ability to detect auto insurance fraud compared to single client training.

Media production and consumption behaviors are changing in response to new technologies and demands, giving birth to a new generation of social applications. Among them, crowd journalism represents a novel way of constructing democratic and trustworthy news relying on ordinary citizens arriving at breaking news locations and capturing relevant videos using their smartphones. The ARTICONF project as reported by Prodan (Euro-Par 2019: parallel processing workshops, Springer, 2019) proposes a trustworthy, resilient, and globally sustainable toolset for developing decentralized applications (DApps) to address this need. Its goal is to overcome the privacy, trust, and autonomy-related concerns associated with proprietary social media platforms overflowed by fake news. Leveraging the ARTICONF tools, we introduce a new DApp for crowd journalism called MOGPlay. MOGPlay collects and manages audiovisual content generated by citizens and provides a secure blockchain platform that rewards all stakeholders involved in professional news production. Besides live streaming, MOGPlay offers a marketplace for audiovisual content trading among citizens and free journalists with an internal token ecosystem. We discuss the functionality and implementation of the MOGPlay DApp and illustrate four pilot crowd journalism live scenarios that validate the prototype.

The integration of heterogeneous and weakly linked log data poses a major challenge in many log-analytic applications. Knowledge graphs (KGs) can facilitate such integration by providing a versatile representation that can interlink objects of interest and enrich log events with background knowledge. Furthermore, graph-pattern based query languages, such as SPARQL, can support rich log analyses by leveraging semantic relationships between objects in heterogeneous log streams. Constructing, materializing, and maintaining centralized log knowledge graphs, however, poses significant challenges. To tackle this issue, we propose VloGraph—a distributed and virtualized alternative to centralized log knowledge graph construction. The proposed approach does not involve any a priori parsing, aggregation, and processing of log data, but dynamically constructs a virtual log KG from heterogeneous raw log sources across multiple hosts. To explore the feasibility of this approach, we developed a prototype and demonstrate its applicability to three scenarios. Furthermore, we evaluate the approach in various experimental settings with multiple heterogeneous log sources and machines; the encouraging results from this evaluation suggest that the approach can enable efficient graph-based ad-hoc log analyses in federated settings.