Machine learning; a booming industry is ready to make an impact in the way we have been living and doing things. It is a technique evolved from AI when machines were imbibed with intelligence to learn and comprehend the data further executing “smart” decisions.
Machine learning is a specific field of application of AI and has been extensively using artificial intelligence and making it possible to teach machines how to evaluate and comprehend the critical data points, identifying a pattern and making decisions based on the data which can otherwise seem to be impractical for human intelligence.
The ability to make machines learn something is powered by AI and hence these two work coherently. Ever since its origin, machine learning is evolving and exploring new dimensions about how one can play and execute inconceivable data. But how do machines comprehend data? Do we feed excels to machines?
We provide data in the form of Knowledge Graphs.
What are knowledge Graphs?
Knowledge graphs, also known as semantic networks, are the representation of critical data points linked within a relation and domains of hefty data. These are robust tools as they are susceptible to automated modifications and updates thereby mitigating the need for repeated updation.
In knowledge graphs, the knowledge of critical and complicated data points are represented and stored in such a way as to deliver the real context and relationships amidst data points. Thus, are also considered as derivate or secondary datasets.
The rounds and rounds of filtering and derivation on original data-keeping intact the originality and integrity of data generate knowledge graphs. This makes it easy and doable for machines to run relationship comprehension on data and also in taking critical and significant decisions.
Being structured and segregated with optimum representation, precision, and context of data, Knowledge graphs are dominantly used in Machine Learning and AI. Let’s understand the applications of knowledge Graphs in Machine Learning.
Knowledge Graphs: Establishing relations in Machine Learning
The foundation of machine learning resides in systemic and well-organized data classification with the aid of Deep Learning. Deep learning makes data classification easier and achievable. It can run operations on loads and loads of data without compromising the quality, value, and integrity of the data. It achieves the classification by simple examples such as images, documents, spreadsheets, speech, video, and whatnot! It can process a vast range of data thereby leaving no limitation on data types and providing different perspectives of specific domains. This roots for varied dimensions and smart decisions by “Machines”.
The classification is done to create subsets of relevant data points that belong to the same class. This relationship and subsets never existed before and are highlighted now through classification. These data points will then be used to generate a knowledge graph.
The knowledge graphs generated after the process of classification from deep learning propound robust applications. This assists in accession and finding the hidden classes of data that would otherwise be impossible to establish. All this fuels easy collaboration and eliminates possible hurdles to achieve the desired outcomes.
Knowledge Graphs mitigating limitations in Machine Learning
Often creating classes in data is cumbersome and machine learning specifically uses these classes to operate and make conclusions from the data. But, during the process of knowledge graph generation, data is optimally scanned and crawled to detect elements and then classify them in classes with the assistance of deep learning. This helps in precise ontology (meaning the cases, properties of data, and relationships among them). The classified and segregated data can then be processed and leveraged for critical processes and outcomes.
Knowledge Graphs in Machine Learning: A pragmatic vision
Machine Learning is exploring and extrapolating applications in other domains of the real world. With AI getting some new dimensions, Machine learning is in no way terminating. Technology is believed to bring revolutions to the way machines interpret and process data. But to extract most of the data, the data must be properly displayed. Thanks to the administration of knowledge graphs in Machine Learning.
These graphs or semantic networks represent and distribute data to machines in a rectified and classified way. These establish and highlight the relationship between distant arenas of data and provenances. This enables machines to extract the most value from data by combining, comprehending, and contrasting the critical data from different sources as well.
The use of knowledge graphs in Machine learning can transform and revolutionize the way machine learning has been evolving and finding new applications. The future will witness some inevitable and unachievable tasks being done by the amalgamation of machine learning and knowledge graphs.
Ankita Sinha, Co- Founder & CTO, Gravitas AI