Tales & Tips from the Trenches: Two Types of Graph Databases

This column focuses on

1) two classes of graph databases – Property and Semantic

2) how Property and Semantic graph databases differ and

3) graph database common use cases. It is important to understand what Property and Semantic graph databases are before understanding where they differ.

Graph Database

A Property
Graph Database is an attributed, multi-relational graph where:

  • The edges are labeled
  • Both vertices and edges can have any number of key/value properties associated with them
  • Graph databases are more complex compared to the standard single-relational graphs
Figure 1 illustrates a property graph with two vertices and one edge.

Each node
represents a single person who is connected with others through relationships.
Figure 1 shows:

  • John works with Jane
    since year 2000.

Graph Database

  • A Semantic Graph Database is a Triplestore:
    • It is made up of semantic triples in the form of Subject-Predicate-Object.
    • It is capable of integrating heterogeneous data from many sources.
  • It is a type of NoSQL graph database.
Figure 2 illustrates a semantic graph integrating heterogeneous data from many sources and making links between datasets.

Figure 2 shows:

  • Lion eats Deer
  • Deer is a Mammal
  • Human is a Mammal
  • Mammals are Living things
  • Car is a Non-Living thing
  • Phone is a Non-Living thing
  • Laptop is a Non-Living thing
  • Human is not a Non-Living thing; but a human uses
    Non-Living things
  • Living and Non-Living things are part of Eco-System

Property vs
Semantic Graph Databases

The following section highlights areas where Property and Semantic graph
databases differ.

Standards Adherence/Compliance

  • Property graph
    databases use emerging Ad Hoc standards which apply to structure, properties, metadata,
  • Semantic graph
    databases use W3C standards.


  • Property graph
    databases are schema-based with the need to define all types, properties, and
    values upfront.
  • Semantic graph
    databases are schema-less.

Query Language

  • A query language
    is still in development for property graph databases. Cypher is an evolving
    query language for property graph databases, provided by Neo4j.
  • Full query
    languages like SPARQL are similar to SQL with query optimization and are
    available for semantic graph databases.

String Length

  • Property graph
    databases avoid long strings.
  • Semantic graph
    databases are optimized to deal with arbitrary length strings (Example: URLs).

Disparate Data

  • Property graph
    databases require effort to share disparate data.
  • Semantic graph
    databases are built to link disparate data.

Linked Open Data

  • It is very hard
    to read linked open data when using property graph databases.
  • Semantic graph
    databases are built to read linked open data.

Databases Common Use Cases

One of the biggest data challenges faced in
today’s world is not about how to handle disparate data but how to connect data
using the technologies available. Graph databases provide one solution for this
challenge. Here are some common use cases:

Social Networks

Social networking databases tend to store and
pull information that is connected but also not uniform. These databases are
focused more on building a network among users (“objects”) rather than neatly
storing data in tables. The relationships among those users can be friends,
friends of friends, coworkers, common interests, etc. which in turn make
everything even more complex.

Metadata Management

Graph databases offer the fundamental solid
foundation and processes needed to manage and gain benefits from enterprise
data. It is robust at modelling the complex relationships between
mission-critical data assets and answering data stewards’ questions. When the
business is in need of answers, it also needs to know the level of confidence
in the information that is provided. Graph databases enable a way to quickly
explore and measure trust to understand the data asset with confidence. Graph
database technology has proven effective in mastering information
across hundreds of data sources and it continues to gather pace as part of a
robust metadata management strategy.

Fraud Detection

Enterprise organizations rely on graph
database technology to strengthen their fraud detection capabilities to prevent
a variety of financial crimes like credit card fraud, insurance fraud, etc.
Traditional fraud prevention measures used to focus on individual data points;
however, today’s fraud presentation measures are improved by using graph
technology to look beyond individual data points to the connections that link

Network Analysis

The size and complexity of the network requires
a configuration management database more powerful than anything relational
databases have to offer. A graph database enables the connection of many
monitoring tools at once to gain critical insights into the complicated
relationships between different networks. The size and complexity of today’s networks
requires a configuration management database (CMDB) that is more powerful than
relational databases can support. Using graph database technology in network
analysis will tremendously help in troubleshooting, impact analysis, and
scheduling for planned outages.

Recommendation Engines

Real time
recommendation engines are critical to the success of any online business. Making
accurate, timely recommendations in real time takes advantage of a customer’s
browsing history and recent product purchases. Graph databases are the key
technology in real time for connecting masses of consumer and product data to
gain insight into customer needs and product trends. This is one of the
significant areas where graph databases outperform relational and other NoSQL
data stores.

This column has introduced you to the two types of graph databases and their common use cases.

In the next column in this series, we will address:

  • Graph database challenges
  • How to identify the right
    graph database

This quarter’s column was written by Anandhi Sutti with THE MITRE CORPORATION. Anandhi has over 20 years of experience in Data Management. She has helped public and private sector clients to oversee and strategize the implementation of Data Management and Business Intelligence (BI) projects. She has strong expertise in Business Intelligence, Data Analytics, Data Modeling, Data Architecture and Data Strategy along with architecting complex systems and applications using a Software Development Life Cycle (SDLC). Anandhi has a master’s degree in computer applications and bachelor’s degree in mathematics.

Approved for Public Release, Distribution Unlimited. Public Release Case Number 20-0029

The author’s affiliation with The MITRE Corporation is provided for identification purposes only and is not intended to convey or imply MITRE’s concurrence with, or support for, the positions, opinions, or viewpoints expressed by the author. ©2020 The MITRE Corporation. ALL RIGHTS RESERVED

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Bonnie O'Neil

Bonnie O'Neil

Bonnie O'Neil is a Principal Computer Scientist at the MITRE Corporation, and is internationally recognized on all phases of data architecture including data quality, business metadata, and governance. She is a regular speaker at many conferences and has also been a workshop leader at the Meta Data/DAMA Conference, and others; she was the keynote speaker at a conference on Data Quality in South Africa. She has been involved in strategic data management projects in both Fortune 500 companies and government agencies, and her expertise includes specialized skills such as data profiling and semantic data integration. She is the author of three books including Business Metadata (2007) and over 40 articles and technical white papers.

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