"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
With the fast paced AI era, the increasing amount of data is implemented for business benefits and advantages, we are steadily transforming data into knowledge.
Today,the data-driven approaches, 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 available data is not sufficient or don’t involve sufficient informative content, we need to adopt an alternative for trying out exploiting data. The similar approach is the Knowledge Graph.
On the same note, we will discuss;
1. Definition of Knowledge Graph
2. Examples and Used-cases of Knowledge Graph
3. Characteristics of Knowledge Graphs
4. Objective of Knowledge Graph
5. How can it help in machine learning?
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.
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.
(Must read: Network graph and network topology)
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.
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);
Purpose & Function
Deploy knowledge graph for the Bing search engine, LinkedIn data & Academics.
As of now, building a knowledge graph that gives relationships within users and products, provided on the website.
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
For making connections among people, events and ideas, and essentially concentrating on news, people and events associated with the social network.
Renders a framework for various companies/industries in order to generate internal knowledge graphs.
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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.
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.
Supply Chain Management: Knowledge graph benefits in managing supply chain management 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.
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.
Use cases and 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.
(Must check: What is text mining?)
It outlines the accumulation of interconnected descriptions of diverse entities including objects, events and concepts, where
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.
Each passing year, Machine Learning and Knowledge representation through knowledge graph is advancing swiftly. Since the machine learning tools and 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;
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.
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.
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;
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.
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.
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.
(Also check: Exponential Smoothing and its types)
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. I
nitiating 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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