What is the Knowledge Graph?

  • Neelam Tyagi
  • Oct 19, 2020
  • Machine Learning
What is the Knowledge Graph? title banner

We are living in the age of Artificial Intelligence, where we are steadily transforming data into knowledge. On the other hand, the increasing amount of data and knowledge is implemented in terms of business benefits and advantages.

 

Today, the data-driven approaches, the fundamental perspective and prime building block are obviously data, this is the source from which meaningful information is derived in order to generate value for the business. Also, when the data available is not sufficient or don’t involve sufficient information content, we need to adopt an alternative for trying out exploiting data. The similar approach is the Knowledge Graph.

 

"While there have been many methods attempted to solve the disparate data problem, a knowledge graph is the most modern and best way to harmonize enterprise data. All data, data sources, and databases of every type can be represented and operationalized in a knowledge graph."-Steve Sarsfield

 

On the same note, let us discuss the concept of the knowledge graph in deep that includes its characteristics, definitions, examples, and use cases. Later on, we will discuss the role of the knowledge graph in the context of machine learning. 


 

Defining Knowledge Graph

 

Typically, a “graph” is a structure aggregating a set of objects where some combinations of the objects are related in some sense. Widening the concept of the graph, the knowledge graph portrays the assemblage of interconnected descriptions/representation of some real-world entities- objects, events, or concepts. Basically, the knowledge graph embeds data in the context through coupling and semantic metadata, this way it delivers a sustaining framework for data integration, alliance, analytics and distribution.      

 

On a core note, the Knowledge Graph is the elementary resource for human-alike prudent augmentation and natural language processing and understanding, that incorporates rich knowledge regarding global’s entities, their attributes, and semantics connections among separate entities.


The image is highlighting some selected definition of the knowledge graph.

Some selected definitions of Knowledge Graph, Source


"Knowledge graphs are on the rise at enterprises that seek more effective ways to connect the dots between the data world and the business world. Paired with complementary AI technologies such as machine learning and natural language processing, knowledge graphs are enabling new opportunities for leveraging data and quickly becoming a fundamental component of modern data systems."- Joyce Wells

 

However, Knowledge Graph strategies are serviceable for heightening the performance of conventional techniques, employing contextual information usually in the form of catalogue items (films, books, songs, etc.)  and giving an insight into the correlation between different entities.

 

Knowledge graphs have developed by being the requirement of executing the task with or act upon the information depending on the context.  For examples, they can help in identifying fraud, keeping track of records, writing novels etc. In recent years, knowledge graphs are gaining more adhesion with machine learning so that the process of artificial intelligence can deploy the actual information for multiple scenarios, as per need. The same will be discussed in the next section. 

 

Some Famous Examples of Knowledge Graphs

Different companies have been developed various types of knowledge graphs that are being deployed for varying purposes, for instance, some companies use internal or smaller knowledge graph for online function and many more. Below table is listing some of the biggest knowledge graphs along with their functions and developers (Source);

 

S.No

Developer 

Purpose & Function

1

Microsoft

Deploy knowledge graph for the Bing search engine, LinkedIn data & Academics.

2

eBay

As of now, building a knowledge graph that gives relationships within users and products, provided on the website.

3

Google

The knowledge graph of Google has greatly adopted in the context of a bulky categorization function across Google’s devices and quickly embedded in the search engine

4

Facebook

For making connections among people, events and ideas, and essentially concentrating on news, people and events associated with the social network.

5

IBM

Renders a framework for various companies/industries in order to generate internal knowledge graphs.

 

Some Use Cases of the Knowledge Graph are following;

 

  1. Question: Responding is the major used application of the knowledge graph. Since it contains immense information and question-answering is the appropriate method to assist users more efficiently and can recover information effectively from the knowledge graph.

  2. Netflix: It deploys a Knowledge Graph to stockpile the vast quantities of information for its recommendation system that aids in determining linkage amongst movies, tv shows, person etc. However, these associations can be practised for anticipating what customers are looking for or what might they like to watch next. (Similar blog: Using Data Handling and Digital Marketing to maximise customer experience: A Netflix Case Study)

  3. Supply Chain Management: Knowledge graph benefits in managing supply chain smoothly, companies can easily administer track of inventories of various components, cadre involved, time etc by which companies shift items more rapidly and cost-effectively.

  4. Stocking Information of Research: It is another application of Knowledge Graph, for example, multiple organizations are adopting the knowledge graph for collecting information generated from different stages of research that can further be applied for developing convenient models, risk management, process supervision, etc.


The image is depicting the Use cases and key characteristics of the Knowledge Graph.

Use cases and key characteristics of Knowledge Graph


 

What are the Key Characteristics of Knowledge Graph?

 

The key characteristics of Knowledge graphs include combined aspects of sparse data management paradigms, such as;

  • Database, since the data could be explored or accessed through structured inquiries;

  • Graph, because graphs can be examined in the form of other network data structure;

  • Knowledge-base, since these bases carry formal semantics data that can be practised for interpreting the data and drag out new factual information.

It outlines the accumulation of interconnected descriptions of diverse entities including objects, events and concepts, where Description holds precise semantic that enables users and computers to process them in an expedient and clear format, and Entity Description, shared with each other, create a network-thread in which each entity depicts a part of the description of the entities and gives relation for their interpretation.


 

What is the Objective Behind Using Knowledge Graph?

 

While aiming to enhance the way people search for information, the knowledge graph eases the complex process of searching and exploration as a lot of information is there in the form of data, audio, videos, and images about a person, entity, or object. 

 

Therefore, the perception behind the knowledge graph is simply to make searching smart enough or identifying relation among two or more entities at ease in order to understand what an individual is looking for and provide tailored results accordingly. 

 

Since through the knowledge graph searches for authentic, and real-world things, so it augments distinct features on the basis of actual facts and associations to respond queries more effectively and enables us to identify easily what we are looking for. 

 

Such facts and information, inside the knowledge graph, are based on various sort of publicly accessible data that also provides possibilities for interconnecting different data and obtain intelligence from that.  For example, by analyzing customer reviews and ratings, it can help websites to deploy search engine optimization(SEO) and let businesses to get more search traffic on their websites, also help in establishing reliability for businesses.


 

How Knowledge Graph can Help in Machine Learning?

 

Each passing year, Machine Learning and Knowledge representation through knowledge graph is advancing swiftly. Since the ML techniques are improving by the time for performing various tasks with huge precision and accuracy, adopting knowledge graph can assist in reflecting the entities and relationships with immense reliability and explainability.

 

Bringing knowledge graph and machine learning together can improve the accuracy of the outcomes and augment the potential of machine learning approaches. Following are some possibilities that can be availed through implementing knowledge graph into machine learning;

 

  • Data Inadequacy

 

In order to train an ML model, there is a requirement of sufficient amount of data, but in the case of scanty data, Knowledge Graph can be deployed to advance training data, for example, substituting the name of the entity from the actual training data to the name of entity of similar data type. By this way, a various number of examples can be made using the knowledge graph.

 

  • Zero-shot Learning

 

Undoubtedly, machine learning algorithms are evolved as much intelligent, but if the properly labelled training data is not available for some classes, they are unable to distinguish amid two similar objects, this is called Zero-shot Learning in ML. To address this problem, Knowledge can serve as a big approach, the inferences made by ML models can be integrated with the consequences obtained from the knowledge Graph. For example, with the image data where the types of situation are missing under the training data.

 

  • Explicability

 

One of the major issues needs to be addressed in Machine Learning methods is explicating the prophecies yield by machine learning models, and the inferable description of these prophecies, where Knowledge Graph can relieve this issue through mapping the interpretations and explanations to some unrecognized nodes in the graph and encapsulating the process of decision-making.

 

Apart from this, some challenges can also be addressed;

 

  1. A consistent set of suitable applications, that could be involved while making the knowledge graphs, will add benefits in knowing and reusing of Knowledge Graphs amid engineers, developer, and researchers.

  2. Provided an assemblage of unstructured data and Knowledge Graph, while employing knowledge integration, an issue appears as to recognise whether the entities specified in the data will coordinate with the real world entities existing in Knowledge Graph. However, this might be solved through ML algorithms, but the insights obtained from these algorithms relied on the nature of the data, and due to wide range of diverse dataset, knowledge integration becomes pretty challenging. 

  3. Though Knowledge is not unvarying but constantly evolving. For instance, through a knowledge graph, a user keeps the records of patients’ health data, and it can be highly possible that the data reserved at a particular time can be invalid/mistaken for some later time, so how to entertain this evolving quality of knowledge.


 

Conclusion

 

Considerable research has been carried out into knowledge graph in recent years, it is a buzzword adopted by various organizations and academia in order to explain several knowledge representation applications. Initiating from defining the concept of the knowledge graph, its characteristics, the purpose of using it to the role of the knowledge graph in machine learning have been explained in the article. We have also seen some use cases of it in the form of real-world applications. 

 

"Using data faster means treating the data as a 'database of now, the now which will become the future'. Making data smarter can be tricky, but it is worth it."-Jim Sinur

 

However, the term knowledge graph is suitable for describing ample applications but should be used more carefully. Taking into account the assorted applications, the knowledge graph sustains more correspondence to an intellectual framework than to a mathematical edifice.

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