Businesses are switching to automation, accomplishing their day-to-day tasks via software, in order to make work easier, faster, and more cost-effective. But what you may not know is that software does have its limitations. It is hard for software to process unstructured data, such as images, speech and even text. This is where Knowledge Base Construction (KBC) comes to the fore.
What is Knowledge Base Construction?
Knowledge Base Construction refers to the delicate process of populating a knowledge base by extracting data from unstructured sources such as texts, PDF, images, audios, and others. One of the challenges of integrating software in your business activities is its inability to directly deal with unstructured data.
KBC systems help solve this problem by providing storage for data in an easy-to-use format. Once the system stores data in a structured format, it becomes easy for applications to access and use the data that was previously impossible to use.
Building a KBC system requires multiple processes of information extraction. You could consider each individual process as an independent system, hence, the whole process requires a collaborative effort. The general architecture of a KBC system has the following layers:
This layer has all the elements needed in the process of extracting information from the data lake. These elements are as follows:
- Deep learning/ Machine learning: They classify objects into categories, giving meaning to each category. It would be difficult to extract information from images without deep or machine learning.
- Natural Language Processing (NLP): It processes and analyzes natural languages, thanks to special tools that deal with text that perform tasks such as part-of-speech tagging, stemming, lemmatization, relationship extraction, named entity recognition, and parsing.
- Graph database: It helps to store data as nodes, establishing relationships between the nodes. It is essential in writing natural queries because it is a natural mechanism to store data.
- First-order logic (FOL): This element enables the system to think like humans do, paving the way for probabilistic reasoning. The system gets the power to determine if a sentence is valid or not.
Reasoning over the data stored in the graph database and FOL takes place in this layer. It provides reasoning capabilities over the stored data. It consists of the following:
- Knowledge representation: It is responsible for storing information in a probabilistic format so that it can gather evidence from various sources to prove its validity.
- Knowledge query: It performs probabilistic inference to find the necessary information which an application can extract and use.
- And knowledge learning: It gathers new facts, validates old facts, and increases its information arsenal.
This is the application layer of any KBC system. Applications in this level include:
- Chatbots: KBS converts unstructured data to structured data, making it possible for the creation of chatbots that uses the knowledge gathered to reason and answer questions like humans.
- Expert system: Expert systems in KBC take care of dark data and are useful in healthcare where there is lots of dark data to decode before making decisions.
- Search: It provides a medium to find any required data. Structured data is easy to search and find, as opposed to unstructured information. Since knowledge bases help in reasoning over facts, it is possible to find the correct information you want from the KBC system.
Learning and harnessing Knowledge Base Construction can be a tricky affair, especially if you are new to the field. But here at Helios, we are here to help you manage your data in an efficient and reliable manner to help you make profits. Contact us today and our data experts will do everything for you.