PathQuery Pregel: high-performance graph query with bulk synchronous processing

Arsintescu, B.G.; Deo, Shardul; Harris, Warren

doi:10.1007/s10044-019-00841-z

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…The high-performance computing power of graph databases can help reduce the size of large data applications. The growing interest in the use of graph databases has come with the increasing complexity of real-world data and the growing need for graph querying [Arsintescu et al 2019]. In addition to extracting high-dimensional characteristics, graphs also make it possible to perform a variety of high dimensional analysis (e.g.…”

Section: Recommendation Of Tripadvisor Museums Based On Graph Databasementioning

confidence: 99%

Towards a Semantic Graph-based Recommender System. A Case Study of Cultural Heritage

Qassimi¹,

Abdelwahed²

2021

jucs

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Research on digital cultural heritage has raised the importance of providing visitors with relevant assistance before and during their visits. With the advent of the social web, the cultural heritage area is affected by the problem of information overload. Indeed, a large number of available resources have emerged coming from the social information systems (SocIS). Therefore, visitors are swamped with enormous choices in their visited cities. SocIS platforms use the features of collaborative tagging, named folksonomy, to commonly contribute to the management of the shared resources. However, collaborative tagging uses uncontrolled vocabulary which semanti- cally weakens the description of resources, consequently decreases their classification, clustering, thereby their recommendation. Therefore, the shared resources have to be pertinently described to ameliorate their recommendations. In this paper, we aim to enhance the cultural heritage visits by suggesting semantically related places that are most likely to interest a visitor. Our proposed approach represents a semantic graph-based recommender system of cultural heritage places through two steps; (1) constructing an emergent semantic description that semantically augments the place and (2) effectively modeling the emerging graphs representing the semantic relatedness of similar cultural heritage places and their related tags. The experimental evaluation shows relevant results attesting the efficiency of the proposed approach.

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Section: Recommendation Of Tripadvisor Museums Based On Graph Databasementioning

confidence: 99%