Graphs are becoming a widespread data representing method because they offer flexibility. As a result, digital technologies for graph data management are increasing in popularity. By 2023, graph technologies will facilitate quicker data contextualization in 30% of organizations worldwide.
The most popular solutions for graph data management include graph databases and knowledge graphs.
A graph database is a database that contains information in the form of data entities and their relationships. It stores data using a graph architecture and allows information querying using a graph language.
Knowledge graphs are a more expressive variant of graph databases. They have the added capability to derive new knowledge from graph data stored in a graph database. Knowledge graphs assist in quick data analysis and obtaining useful insights from graph data.
In this article, we’ll discuss two types of knowledge graphs, i.e., RDF and property graphs. We’ll share our two cents on the RDF vs. property graph debate to help you understand which approach is better for graph data management.
What Is a Knowledge Graph?
A Knowledge Graph is a network of data entities and their relationships illustrated in the form of a graph. Data entities in knowledge graphs refer to real-world entities, e.g. objects, people, places, and situations. The storage layer of a knowledge graph is usually a graph database that is made for storing graph data in the form of data facts and their relations.
Knowledge graphs find their usage in the contextualization of any form of data (structured and unstructured) into a network of meaningful information. They are specially designed graphs that can deal with the constantly changing real-world information.
There are two essential elements of a knowledge graph:
- Entities: Entities refer to real-world objects such as a person, place, event, etc. These are represented as nodes in a knowledge graph.
- Relationships: Relationships are any information that defines how data entities interact. They are represented as edges between nodes in a knowledge graph. Edges can be unidirectional or bidirectional, based on the type of connection between nodes.
What Are Ways of Implementing Knowledge Graphs?
Knowledge graphs can manage and visualize heterogeneous data, integrate with new data sources, and map relationships from any data store. There are various graph databases categorized based on their underlying graph data models, including
- RDF (Resource Description Framework) Triple Stores
- Labeled Property Graphs
The purpose of both RDF stores and property graphs is to store graphically structured data and offer different ways to navigate through that data. But there are many differences in the structure and implementation of these two graph databases.
Let’s analyze both and present a comparative analysis to help you make an informed decision about choosing a graph database.
What Are RDF Graphs?
Since the 90s, the idea of a semantic web–keeping track of web pages and their links has been floating around. While the semantic web has not been implemented yet, the idea behind it stayed and became the foundation of RDF Triple Stores.
RDF stands for Resource Description Framework, a World Wide Web Consortium (W3C) standard created originally to model metadata. Triple Stores store and express information in a sentence-like structure of three; subject-predicate-object, denoted by two nodes connected by a single edge. For e.g. Josh likes bread. This information will be structured as triples Josh-likes-bread, where the subject in Josh, predicate is likes, and object in bread.
How Do RDF Triple Stores Work?
In the RDF Triple Stores model, the subject and object are depicted by two nodes, starting and ending, representing data entities. The predicate is depicted by an edge linking the subject and object nodes, representing the relationship between subject and object entities.
RDF Triple Stores is a standardized model where every element has a Uniform Resource Identifier (URI), allowing machines to identify every subject, predicate, and object uniquely.
RDF Triple Stores uses a standard querying language, SPARQL, to extract information from the database. The standard methods of representing and querying data allow RDF Triple Stores to be interoperable with any other knowledge graphs based on RDF.
Following are some advantages and disadvantages of implementing RDF-based knowledge graphs:
Advantages of RDF Graphs
- Standardization: All RDF-based knowledge graphs use the same standard framework and formal semantics for storing and representing data along with a standard querying language. Data sharing between RDF data stores on the web is simplified thanks to RDF’s web-native syntax.
- Interoperability: RDF Triple Stores follow a W3C-supported standard that allows interoperability among knowledge graphs. This interoperability allows RDF-based graphs to integrate and exchange information with each other.
- Extensibility: RDF Graphs allow users to add new nodes and relationships, or even substructures, without requiring rebuilding the database.
Disadvantages of RDF Graphs
- Deep Search Complexity: Performing a deep search in large RDF graphs is a complex task as it requires traversing through every relationship.
- Strict Adherence to Standards: All information stored in RDF should be in the form of triples meaning only two objects can be linked at a time, which can be limiting for many use cases.
What Are Property Graphs?
Property Graphs, also called Labeled Property Graphs (LPG), are a variant of graph databases where entities and their relationships have associated attributes. Attributes can be any property that gives details of a data entity or a relationship. Property graphs get their name from their capability to include properties associated with nodes and edges denoted as key-value pairs. For e.g. Mark wrote a book. In this case Mark is a data entity represented as a node in the graph. Its associated key-value pair could be Person: Author.
Property graphs are focused on offering faster querying and extensive storage.
How Do Property Graphs Work?
In property graphs, information is stored in nodes containing source and target entities and edges containing their relationships. Nodes and edges in property graphs are recognized by their internal structure, including their unique identifiers and properties. Any node or edge can include properties representing data attributes as key-value pairs.
Property graphs do not have a standard representation or querying of information. Organizations working with property graphs create their own semantics as there are no standard ontologies. Every property graph has its unique querying language.
Following are some advantages and disadvantages of implementing knowledge graphs based on property graphs:
Advantages of Property Graphs
- Simplicity: Property graphs are simple and quick to set up and use. Knowledge graphs based on property graphs can be an excellent start for new users.
- Easy Navigation: Property graphs are easier to traverse without limitations or standard querying languages.
- Detailed: Properties associated with relationships in property graphs offer more detail about the data entities and their relationships without having to create extra nodes for every detail. The interpretation of the information is left up to the user.
Disadvantages of Property Graphs
- Lack of Interoperability: The lack of standardization in property graphs makes it difficult to share or exchange data with different data stores. The unique identifiers are local to the property graphs and have no meaning to any other database.
- Vendor Lock-in: Organizations using a knowledge graph based on property graphs cannot integrate their information across multiple tools or systems. The possibility of being locked into a single property graph vendor is very high.
RDF vs. Property Graphs
While both RDF and property graphs act as a storage layer for knowledge graphs, they differ in the following aspects:
|Differences||RDF Triple Stores||Labeled Property Graphs|
|Representation||Entities and relationships are represented in a subject-predicate-object structure.||Entities and relationships have associated attributes represented as key-value pairs.|
|Querying Languages||Standard querying language SPARQL.||Every property graph implementation generally has its own language.|
|Internal Structure||Entities and relationships in RDF do not have any internal structures and are only recognized by their URIs.||Entities and relationships in property graphs have an internal structure that goes beyond labels and includes properties as a part of their identity.|
|Focus||RDFs are focused on offering standardization and interoperability.||Property graphs are focused on data entities to enhance storage and speed up querying.|
|Use Cases||Useful for any use case requiring knowledge graphs with slow-changing datasets. Perfect for scenarios requiring reasoning or inference or where information from other data stores is required. E.g., testing or evaluation.||Useful for any use case requiring large knowledge graphs with dynamic datasets that need deep traversal from time to time, e.g., social graphs.|
Which Approach Is Better for Implementing Knowledge Graphs?
Knowledge graphs deal with massive amounts of information they store and query using graph databases. With the increasing growth and usage of data globally, the demand for knowledge graphs and their underlying graph databases will only rise in the future.
When creating a knowledge graph, you will have a choice between multiple graph databases, most commonly RDF Triple Stores and property graphs. Our analysis of their individual advantages and use cases highlights their unique values.
However, the better approach for implementing your knowledge graph is the one that better suits your organizational use cases and requirements. These requirements can include the type of data and storage your organization needs and what your organization requires from the knowledge graph. For example a social media application would find property graphs more suitable to implement their friend suggestion feature. Whereas a finance company would prefer RDF-based knowledge graphs for implementing fraud detection.
Both RDFs and property graphs are beneficial but also bring their own limitations that can be easily worked around. But an even more fruitful approach would be to combine the benefits of RDFs and property graphs. Combining these two approaches can be a significant step towards optimizing graph databases for creating interoperable and flexible knowledge graphs.
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