There is a growing demand for data-driven insights to help businesses make better decisions and stay competitive. To meet this need, organizations are turning to knowledge graphs as a way to access and analyze complex data sets. In this blog post, I will discuss what knowledge graphs are, what graph databases are, how they differ from hierarchical databases, the benefits of graphical representation of data, and more. Lastly, we'll discuss some of the challenges of graph databases and how they can be overcome.

What Is a Knowledge Graph?

A knowledge graph is a visual representation of data or knowledge. In order to make the relationships between various types of facts and data easy to see and understand, facts and data are organized into a graph structure. A knowledge graph typically consists of nodes, which stand in for entities like people or objects, and edges, which stand in for the relationships among these entities.

Each node in a knowledge graph has characteristics and attributes that describe it. For instance, the node of a person might contain properties like name, age, and occupation. Edges between nodes reveal information about their connections. This makes knowledge graphs a powerful tool for representing and understanding data.

Benefits of a Knowledge Graph

There are a number of benefits to using knowledge graphs. 

• Knowledge graphs(KG) provide a visual representation of data that can be easily understood. This makes it easier to quickly identify patterns, and correlations. 
• Additionally, knowledge graphs make it simple to locate linkage data by allowing us to quickly access a particular node and obtain all of its child information.
• These graphs are highly scalable, meaning they can support huge volumes of data. This makes them ideal for applications such as artificial intelligence (AI) and machine learning (ML).
• Finally, knowledge graphs can be used to connect various types of data, including text, images, and videos, in addition to plain text. This makes them a great tool for data mining and analysis.‍

‍What are Graph Databases?

Graph databases are used to store and manage data in the form of a graph. Unlike traditional databases, they offer a more flexible representation of data using nodes, edges, and properties. Graph databases are designed to support queries that require traversing relationships between different types of data.

Graph databases are well-suited for applications that require complex data relationships, such as AI and ML. They are also more efficient than traditional databases in queries that involve intricate data relationships, as they can quickly process data without having to make multiple queries.

Comparing Graph Databases to Hierarchical Databases

It is important to understand the differences between graph databases and hierarchical databases. But first, what is a hierarchical database? Hierarchical databases are structured in a tree-like form, with each record in the database linked to one or more other records. This structure makes hierarchical databases ideal for storing data that is organized in a hierarchical manner, such as an organizational chart. However, hierarchical databases are less efficient at handling complex data relationships. To understand with an example, suppose we have an organization with a CEO at the top, followed by several vice presidents, who are in turn responsible for several managers, who are responsible for teams of employees.

In a hierarchical database, this structure would be represented as a tree, with the CEO at the root, and each level of the organization represented by a different level of the tree. For example:

In a graph database, this same structure would be represented as a graph, with each node representing an entity (e.g., a person), and each edge representing a relationship (e.g., reporting to). For example:

(Vice President A) -- reports_to --> (CEO)

(Vice President B) -- reports_to --> (CEO)

(Vice President A) -- manages --> (Manager A1)

(Vice President B) -- manages --> (Manager B1)

(Manager A1) -- manages --> (Employee A1.1)

(Manager B1) -- manages --> (Employee B1.1)

(Manager B1) -- manages --> (Employee B1.2)‍

As you can see, in a graph database, the relationships between entities are explicit and can be easily queried and traversed. In a hierarchical database, the relationships are implicit and can be more difficult to work with if the hierarchy becomes more complex. Hence the reason graph databases are better suited for complex data relationships is that it gives them the flexibility to easily store and query data.

Creating a Knowledge Graph from Scratch

We will now understand how to create a knowledge graph using an example below where we'll use a simple XML file that contains information about some movies, and we'll use an XSLT stylesheet to transform the XML data into RDF format along with some python libraries to help us in the overall process.

Let’s consider an XML file having movie information:

CODE: https://gist.github.com/velotiotech/f9293cba81c6a7816a15ea61d39dbd0f.js

As discussed, to convert this data into a knowledge graph, we will be using an XSL file, now a question may arise that what is an XSL file? Well, XSL files are stylesheet documents that are used to transform XML data. To explore more on XSL, visit here, but don’t worry as we will be starting from scratch. 

Moving ahead, we also need to know that to convert any data into graph data, we need to use an ontology; there are many ontologies available, like OWL ontology or EBUCore ontology. But what is an ontology? Well, in the context of knowledge graphs, an ontology is a formal specification of the relationships and constraints that exist within a specific domain or knowledge domain. It provides a vocabulary and a set of rules for representing and sharing knowledge, allowing machines to reason about the data they are working with. EBUCore is an ontology developed by the European Broadcasting Union (EBU) to provide a standardized metadata model for the broadcasting industry (OTT platforms, media companies, etc.). Further references on EBUCore can be found here.

We will be using the below XSL for transforming the above XML with movie info.

CODE: https://gist.github.com/velotiotech/10b897e657f342814bad072dc22b368e.js

To start with XSL, the first line, “<?xml version="1.0"?>” defines the version of document. The second line opens a stylesheet defining the XSL version we will be using and further having XSL, RDF, EBUCore as their namespaces. These namespaces are required as we will be using elements of those classes to avoid name conflicts in our XML document. The xsl:template match defines which element to match in the XML, as we want to match from the start of the XML. Since movies are the root element of our XML, we will be using xsl:template match="movies". 

After that, we open an RDF tag to start our knowledge graph, this element will contain all the movie details, and hence we are using xsl:apply-templates on “movie” as in our XML we have multiple <movie> elements nested inside <movies> tag. To get further details from <movie> elements, we define a template matching all movie elements, which will help us to fetch all the required details. The tag <ebucore:Feature> defines that all of our contents belong to a feature which is an alternate name for “movie” in EBUCore ontology. We then match details like title, year, genre, etc., from XML and define their corresponding value from EBUCore, like ebucore:title, ebucore:dateBroadcast, and ebucore:hasGenre respectively. 

Now that we have the XSL ready, we will need to apply this XSL on our XML and get RDF data out of it by following the below Python code:

CODE: https://gist.github.com/velotiotech/2c5d14c87cf22675364455bf11d825a6.js

The above code will generate the following output:

CODE: https://gist.github.com/velotiotech/c95958a0e3f7ae68535a6015ac4676a0.js

This output is an RDF XML, which will now be converted to a Graph and we will also visualize it using the following code:

Note: Install the following library before proceeding.

CODE: https://gist.github.com/velotiotech/96742d8dbe04ce987b1602044e5c5e2c.js

CODE: https://gist.github.com/velotiotech/cb52f039ba40811bedd0211c6feb6ff7.js

Finally, the above code will yield a movies.dot.png file in the Downloads folder location, which will look something like this:

This clearly represents the relationship between edges and nodes along with all information in a well-formatted way.

Examples of Knowledge Graphs

Now that we have knowledge of how we can create a knowledge graph, let’s explore the big players that are using such graphs for their operations.

Google Knowledge Graph: This is one of the most well-known examples of a knowledge graph. It is used by Google to enhance its search results with additional information about entities, such as people, places, and things. For example, if you search for "Barack Obama," the Knowledge Graph will display a panel with information about his birthdate, family members, education, career, and more. All this information is stored in the form of nodes and edges making it easier for the Google search engine to retrieve related information of any topic.

DBpedia: This is a community-driven project that extracts structured data from Wikipedia and makes it available as a linked data resource. It is primarily used for graph analysis and executing SPARQL queries. It contains information on millions of entities, such as people, places, and things, and their relationships with one another. DBpedia can be used to power applications like question-answering systems, recommendation engines, and more. One of the key advantages of DBpedia is that it is an open and community-driven project, which means that anyone can contribute to it and use it for their own applications. This has led to a wide variety of applications built on top of DBpedia, from academic research to commercial products.

As we have discussed the examples of knowledge graphs, one should know that they all use SPARQL queries to retrieve data from their huge corpus of graphs. So, let’s write one such query to retrieve data from the knowledge graph created by us for movie data. We will be writing a query to retrieve all movies’ Genre information along with Movie Titles.

CODE: https://gist.github.com/velotiotech/ac81639e9799fe79bcce4d3019520121.js

Challenges with Graph Databases:

Data Complexity: One of the primary challenges with graph databases and knowledge graphs is data complexity. As the size and complexity of the data increase, it can become challenging to manage and query the data efficiently.

Data Integration: Graph databases and knowledge graphs often need to integrate data from different sources, which can be challenging due to differences in data format, schema, and structure.

Query Performance: Knowledge graphs are often used for complex queries, which can be slow to execute, especially for large datasets.

Knowledge Representation: Representing knowledge in a graph database or knowledge graph can be challenging due to the diversity of concepts and relationships that need to be modeled accurately. One should have experience with ontologies, relationships, and business use cases to curate a perfect representation

Bonus: How to Overcome These Challenges:

• Use efficient indexing and query optimization techniques to handle data complexity and improve query performance.
• Use data integration tools and techniques to standardize data formats and structures to improve data integration.
• Use distributed computing and partitioning techniques to scale the database horizontally.
• Use caching and precomputing techniques to speed up queries.
• Use ontology modeling and semantic reasoning techniques to accurately represent knowledge and relationships in the graph database or knowledge graph.

Conclusion

In conclusion, graph databases, and knowledge graphs are powerful tools that offer several advantages over traditional relational databases. They enable flexible modeling of complex data and relationships, which can be difficult to achieve using a traditional tabular structure. Moreover, they enhance query performance for complex queries and enable new use cases such as recommendation engines, fraud detection, and knowledge management.

Despite the aforementioned challenges, graph databases and knowledge graphs are gaining popularity in various industries, ranging from finance to healthcare, and are expected to continue playing a significant role in the future of data management and analysis.