UniKG: A Unified Interoperable Knowledge Graph Database System

2021 ◽  
Author(s):  
Baozhu Liu ◽  
Xin Wang ◽  
Pengkai Liu ◽  
Sizhuo Li ◽  
Qiang Fu ◽  
...  
Keyword(s):  
Database System ◽  
Knowledge Graph ◽  
Graph Database

scholarly journals KGDB: Knowledge Graph Database System with Unified Model and Query Language

2021 ◽  
Vol 11 (1) ◽  
pp. 91-116
Author(s):  
Baozhu Liu ◽  
◽  
Xin Wang ◽  
Pengkai Liu ◽  
Sizhuo Li ◽  
...  
Keyword(s):  
Query Language ◽  
Database System ◽  
Unified Model ◽  
Knowledge Graph ◽  
Graph Database

Update the power project knowledge graph of power project using Neo4j Graph Database

2021 ◽  
Author(s):  
Haibo Liu ◽  
Yang Cao ◽  
Pengcheng Liu ◽  
Huimin Yang ◽  
Rui Sun ◽  
...  
Keyword(s):  
Knowledge Graph ◽  
Graph Database ◽  
Power Project

scholarly journals ROBOKOP: an abstraction layer and user interface for knowledge graphs to support question answering

2019 ◽  
Vol 35 (24) ◽  
pp. 5382-5384 ◽  
Author(s):  
Kenneth Morton ◽  
Patrick Wang ◽  
Chris Bizon ◽  
Steven Cox ◽  
James Balhoff ◽  
...  
Keyword(s):  
User Interface ◽  
Question Answering ◽  
Supplementary Information ◽  
Knowledge Graph ◽  
Graph Database ◽  
Database Queries ◽  
Complex Queries ◽  
Abstraction Layer ◽  
Software License ◽  
Knowledge Graphs

Abstract Summary Knowledge graphs (KGs) are quickly becoming a common-place tool for storing relationships between entities from which higher-level reasoning can be conducted. KGs are typically stored in a graph-database format, and graph-database queries can be used to answer questions of interest that have been posed by users such as biomedical researchers. For simple queries, the inclusion of direct connections in the KG and the storage and analysis of query results are straightforward; however, for complex queries, these capabilities become exponentially more challenging with each increase in complexity of the query. For instance, one relatively complex query can yield a KG with hundreds of thousands of query results. Thus, the ability to efficiently query, store, rank and explore sub-graphs of a complex KG represents a major challenge to any effort designed to exploit the use of KGs for applications in biomedical research and other domains. We present Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways as an abstraction layer and user interface to more easily query KGs and store, rank and explore query results. Availability and implementation An instance of the ROBOKOP UI for exploration of the ROBOKOP Knowledge Graph can be found at http://robokop.renci.org. The ROBOKOP Knowledge Graph can be accessed at http://robokopkg.renci.org. Code and instructions for building and deploying ROBOKOP are available under the MIT open software license from https://github.com/NCATS-Gamma/robokop. Supplementary information Supplementary data are available at Bioinformatics online.

A demonstration of the G∗ graph database system

2013 ◽  
Author(s):  
S. R. Spillane ◽  
J. Birnbaum ◽  
D. Bokser ◽  
D. Kemp ◽  
A. Labouseur ◽  
...  
Keyword(s):  
Database System ◽  
Graph Database

scholarly journals RDFFrames: knowledge graph access for machine learning tools

2021 ◽  
Author(s):  
Aisha Mohamed ◽  
Ghadeer Abuoda ◽  
Abdurrahman Ghanem ◽  
Zoi Kaoudi ◽  
Ashraf Aboulnaga
Keyword(s):  
Machine Learning ◽  
Database Systems ◽  
Database System ◽  
Sparql Query ◽  
Knowledge Graph ◽  
Learning Tools ◽  
Tabular Format ◽  
Knowledge Graphs ◽  
Rdf Database ◽  
Learning Software

AbstractKnowledge graphs represented as RDF datasets are integral to many machine learning applications. RDF is supported by a rich ecosystem of data management systems and tools, most notably RDF database systems that provide a SPARQL query interface. Surprisingly, machine learning tools for knowledge graphs do not use SPARQL, despite the obvious advantages of using a database system. This is due to the mismatch between SPARQL and machine learning tools in terms of data model and programming style. Machine learning tools work on data in tabular format and process it using an imperative programming style, while SPARQL is declarative and has as its basic operation matching graph patterns to RDF triples. We posit that a good interface to knowledge graphs from a machine learning software stack should use an imperative, navigational programming paradigm based on graph traversal rather than the SPARQL query paradigm based on graph patterns. In this paper, we present RDFFrames, a framework that provides such an interface. RDFFrames provides an imperative Python API that gets internally translated to SPARQL, and it is integrated with the PyData machine learning software stack. RDFFrames enables the user to make a sequence of Python calls to define the data to be extracted from a knowledge graph stored in an RDF database system, and it translates these calls into a compact SPQARL query, executes it on the database system, and returns the results in a standard tabular format. Thus, RDFFrames is a useful tool for data preparation that combines the usability of PyData with the flexibility and performance of RDF database systems.

scholarly journals Understanding Horizon 2020 Data: A Knowledge Graph-Based Approach

2021 ◽  
Vol 11 (23) ◽  
pp. 11425
Author(s):  
Nikolaos Giarelis ◽  
Nikos Karacapilidis
Keyword(s):  
Named Entity Recognition ◽  
Data Representation ◽  
Entity Recognition ◽  
Knowledge Graph ◽  
Graph Database ◽  
Keyphrase Extraction ◽  
Graph Analytics ◽  
Aggregated Data ◽  
Horizon 2020 ◽  
Named Entity

This paper aims to meaningfully analyse the Horizon 2020 data existing in the CORDIS repository of EU, and accordingly offer evidence and insights to aid organizations in the formulation of consortia that will prepare and submit winning research proposals to forthcoming calls. The analysis is performed on aggregated data concerning 32,090 funded projects, 34,295 organizations participated in them, and 87,067 public deliverables produced. The modelling of data is performed through a knowledge graph-based approach, aiming to semantically capture existing relationships and reveal hidden information. The main contribution of this work lies in the proper utilization and orchestration of keyphrase extraction and named entity recognition models, together with meaningful graph analytics on top of an efficient graph database. The proposed approach enables users to ask complex questions about the interconnection of various entities related to previously funded research projects. A set of representative queries demonstrating our data representation and analysis approach are given at the end of the paper.

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