Expression and efficient processing of fuzzy queries in a graph database context

Abstract

Graph databases have aroused a large interest in the last years thanks to their large scope of potential applications (e.g. social networks, biomedical networks, data stemming from the web). In a similar way as what has already been proposed in relational databases, defining a language allowing a flexible querying of graph databases may greatly improve usability of data. This paper focuses on the notion of fuzzy graph database and describes a fuzzy query language that makes it possible to handle such database, which may be fuzzy or not, in a flexible way. This language, called FUDGE, is an extension of the CYPHER language used for querying graph databases in a crisp way in the Neo4j graph database management system. It can be used to express preference queries on fuzzy graph databases. The preferences concern i) the content of the vertices of the graph and ii) the structure of the graph. The FUDGE language is implemented in a system called SUGAR, that we present in this article. We also discuss some implementation issues of the FUDGE language in SUGAR.

Quality Awareness over Graph Pattern Queries

“…A prototype implementing the framework in Neo4j supports the proposed approach. e present work opens many perspectives, including its generalization to more complex quality vocabularies [11] and the de nition of more complex and exible quality-based preference queries [23,20,21], for instance considering skyline queries or fuzzy quality preferences. Experimentations (benchmarking and users feedback analysis) of such approaches on possibly large databases have to be performed.…”

“…None of these works considers data annotated with quality problems and quality preferences based on it. Another close work is [21] in which authors de ne a Cypher extension that makes it possible to express preferences, based on fuzzy logic, in graph pa ern queries in order to allow a user to express exible pa erns in the Neo4j Cypher language. In this work, data does not embed quality information, and quality awareness of queries is not studied.…”

Data Science Recommendation System using Semantic Technology

“…This research gives total rules to any individual who needs to execute a graph database for the recommendation framework alongside different recommendation calculations. They extend their work towards a fuzzy query which has been proposed for cypher [12].…”

Improving data exploration in graphs with fuzzy logic and large-scale visualisation

“…Applying fuzzy logic to graphs has been previously proposed in literature, mainly by proposing fuzzy graphs, and describing its modelling, visualisation and analytics [36]. Our current work, however, is focused on performing fuzzy queries on crisp graphs.…”