Semantic query transformations for increased parallelization in distributed knowledge graph query processing

Kim, HyeongSik; Bhattacharyya, Abhisha; Anyanwu, Kemafor

doi:10.1145/3295500.3356212

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…Due to the large and intertwined search space that is induced by KGs, querying and reasoning is computationally expensive. However, in recent years, the implementation of KGs to add semantics to large data repositories has been gaining momentum in the industry, given their need for interoperability that allows cross-querying data repositories with heterogeneous information models that have been developed in silos [34]. KGs are being used as a common data model for homogenizing such repositories, and for enhancing query results with richer descriptions.…”

Section: B Knowledge Graphmentioning

confidence: 99%

Proactive Digital Companions in Pervasive Hypermedia Environments

García

Mayer

Ricci

et al. 2020

2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC)

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Artificial companions and digital assistants have been investigated for several decades, from research in the autonomous agents and social robots areas to the highly popular voice-enabled digital assistants that are already in widespread use (e.g., Siri and Alexa). Although these companions provide valuable information and services to people, they remain reactive entities that operate in isolated environments waiting to be asked for help. The Web is now emerging as a uniform hypermedia fabric that interconnects everything (e.g., devices, physical objects, abstract concepts, digital services), thereby enabling unprecedented levels of automation and comfort in our professional and private lives. However, this also results in increasingly complex environments that are becoming unintelligible to everyday users. To ameliorate this situation, we envision proactive Digital Companions that take advantage of this new generation of pervasive hypermedia environments to provide assistance and protection to people. In addition to Digital Companions perceiving a person's environment through vision and sound, pervasive hypermedia environments provide them with means to further contextualize the situation by exploiting information from available connected devices, and give them access to rich knowledge bases that allow to derive relevant actions and recommendations.

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Section: B Knowledge Graphmentioning

confidence: 99%

Proactive Digital Companions in Pervasive Hypermedia Environments

García

Mayer

Ricci

et al. 2020

2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC)

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“…Among these contents, graph data have constituted an imperative component because of their capability to capture both semantic and structural information [2] . Typical instances of graph data include social interactions [3,4] , bioinformatic contents [5,6] , semantic webs [7] , road Xu Zheng, Lizong Zhang, and Kaiyang Li are with the School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China. E-mail: fxzheng, lzhangg@uestc.edu.cn; kaiyang.li@ outlook.com.…”

Section: Introductionmentioning

confidence: 99%

Efficient publication of distributed and overlapping graph data under differential privacy

Zheng

Zhang

et al. 2022

Tsinghua Sci. Technol.

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Graph data publication has been considered as an important step for data analysis and mining. Graph data, which provide knowledge on interactions among entities, can be locally generated and held by distributed data owners. These data are usually sensitive and private, because they may be related to owners' personal activities and can be hijacked by adversaries to conduct inference attacks. Current solutions either consider private graph data as centralized contents or disregard the overlapping of graphs in distributed manners. Therefore, this work proposes a novel framework for distributed graph publication. In this framework, differential privacy is applied to justify the safety of the published contents. It includes four phases, i.e., graph combination, plan construction sharing, data perturbation, and graph reconstruction. The published graph selection is guided by one data coordinator, and each graph is perturbed carefully with the Laplace mechanism. The problem of graph selection is formulated and proven to be NP-complete. Then, a heuristic algorithm is proposed for selection. The correctness of the combined graph and the differential privacy on all edges are analyzed. This study also discusses a scenario without a data coordinator and proposes some insights into graph publication.

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“…Some community efforts [14,28,38,45,53,54,59] have been made to efficiently process and analyze these data via exploiting highperformance accelerators under a heterogeneous scale-up and scaleout setup which has become mainstream node architecture for Top500 supercomputers. Among these important graph analytics, uncertainty is often intrinsic to a wide spectrum of graph applications, which applies to graph data such as noisy measurement in inter-node connection in supercomputing center [38,55], database querying [7,12,25,26,29], probability in peer-to-peer network [25], bioinformatics [3,26,42], relationship influence in social networks [2,10,11], congestion prediction in traffic network [24], etc. In the literature, uncertain graphs (also known as probabilistic graphs) have been widely utilized to represent these uncertainties [5,47].…”

Section: Introductionmentioning

confidence: 99%