Knowledge Base in Natural Language Processing
Updated: Jul 2
There are enterprises , institutions, and large organizations are building their own knowledge base system because they are having large amount of digital data from several years and they are continuously accessing it for their employees and workers, students etc.. and knowledge management is essential nowadays for every organization.
While knowledge management services that organizations are facing the challenges in this digital era.
Capturing the contextual and semantic information in the knowledge base.
Ability to instantly access the information from data sources.
Need for automated and sustained technology to create such systems.
Then, what is the knowledge base system?
The knowledgebase is a repository of a large amount of knowledge graph, which contains information needed to understand requests and answers of questions from the user.
# Little bit complicated right..?
Let me simplify it. 🤔
First, let's understand what is knowledge graph then we will dive into the knowledge base.
so, the knowledge graph is not just like creating some kind of chart like a bar chart, pie chart or line chart.knowledge graph is a set of interconnected entities (i.e. person, organization, education..etc) and it will be in the proper format.
And the knowledge base is as simple as like knowledge graph but in a large size by implementing with Natural Language Processing giving a response of user input with validation. With human-like accuracy.
Lets deep dive little bit by creating a knowledge graph: -
Before going to deep dive let me show you a simple example.
This is an entity relation of node "a" and node "b" where node a is related to node b as "chief minister"
Node a and node b are two different nodes where it's connected by a relation of "chief minister"
This is can be a small knowledge graph.
A node and entity can have multiple relations with multiple entities.
This graph showing that Naveen Patnaik is the chief minister of Odisha as well as president of Biju Janata Dal as it showing all are connected in the main node but also we can connect as much as a node we want in the sub node.
But there is a problem, is it ok to build a knowledge graph manually?
it's not possible to build a knowledge graph manually by extracting the entities and relationships between them.
And also machines will not understand natural languages.
Now in this scenario, Natural language processing comes into the picture.
How the Knowledge graph works: -
Here is the pipeline for knowledge graph: -
Answer questions: -Providing relevant results and answers to the user queries using NLP by extracting entities using the knowledge base.
Validate questions: -Let's think we have a food delivery app if the user wants to order some food via our app and the app should validate the user requests that what the food the user wants to order or what the query user wants to ask. That the entities should be a match
Disambiguate entities: - like if we are ordering pizza it can happen there will be so many types of pizzas that will be available so it will be difficult to give response without knowing which pizza they are asking.
Suggest alternative: -if the user is asking something and that is not available at that time it should give an alternative suggestion to the user that will be similar kind.
Let's think of how it is working in the backend
Process in the background: -
Sentence segmentation: -in the first step of building a knowledge graph we should split the text document into sentences with finding that the sentence is related to which subject, then we will find out which one is the subject or object part in the sentence.
Entities extraction: - as we all know entity extraction is not a tough task we can do it using POS tagging using spacy but it matters to build with good accuracy.
Extract relations: -as we have already seen to build a knowledge graph we should extract the relation of the entities with others. to connect the nodes.
And finally, build the knowledge graph
Why do we need Knowledgebase?
Now, in this new era of technology, we have a large amount of context data where the enterprises and large organizations are trying to access and manage all the data by implementing various techniques in AI like making clusters to find easily or some useful solutions like chatbot and some question and answering system but as we are data scientist do we really thing its easy to manage that a large amount of context data. so, here the knowledge base comes into the picture.
we can just create a simple knowledge base to store our data with classifying the entities.
Where we need this Knowledgebase?
Before going to start building a knowledge base we should think do we really need knowledge base in our project..?
Let's think about it.
if we are thinking of building an application where we have a large-medium vocabulary content there having a knowledge base is critical. and can be the best for conversational applications.
For example, we are creating an automated application like a chatbot or we are building an application which will find the data from thousands or millions of record. and will give a response to the user.
There we can use the knowledge base which will provide information about important entities to fulfill user requests
Example: - Alexa type bots where we will need a large amount of dataset.