Understanding Ontologies in Data Science: What? Why?

The rapid advancement of Artificial intelligence and its branches like machine learning, deep learning, which function on extracting relevant information and generating insights from data to find sustainable and decisive solutions, is nothing new. But to run these algorithms, organizations need data and code. To translate this necessity into something meaningful, we need data science. While this discipline proliferates into an exciting and diverse technology that incorporates a mixture of deep specialization and broad applications, we also realize the value it brings to the table. Further, data science helps organizations communicate with stakeholders, customers, track and analyze trends, and determine if the collected data is actually of any help or simply a waste of a database farm. So, having an ontology consisting of the relevant terms and connections from a specific domain, the process of identifying core concepts, improving classification results, and unifying data to collate critical information becomes streamlined.

An ontology is a set of concepts and categories in a subject area or domain that possesses the properties and relations between them. Ontological Modeling can help the cognitive AI or machine learning model by broadening its' scope. They can include any data type or variation and set each diver data to a specific task. Furthermore, it supports unstructured, semi-structured, or structured data format—thus enabling smoother data integration. It can include each aspect of the data modeling process, beginning as schemas at the initial level. Therefore they can address the vast data used as input for machine learning training or spew as results. Besides, ontology fits every organization's goal, which can be either mathematical, logical, or semantic-based approaches. Basically, while the idea behind ontologies is relatively simple, it has some profound implications.

Additionally, ontologies also help to improve the data quality for training datasets. They provide more coherent and easy navigation as users move from one concept to another in the ontology structure. Interestingly, an ontology data model can also be applied to a set of individual facts to create a knowledge graph. A Knowledge graph is a collection of entities, where nodes and edges between these nodes express the types and the relationships between them. Meanwhile, in recent years, there has been an uptake of expressing ontologies using ontology languages such as the Web Ontology Language (OWL). While OWL assists in representing rich and complex knowledge about things and the relations between them, it also offers detailed, consistent, and meaningful distinctions between classes, properties, and relationships.

A domain-specific ontology that is actually a combination with AI-driven tools for data analytics can sift through the relevant data and uncover new data patterns and trends. It can help in removing word-sense disambiguation too. In Pharmaceuticals, ontology can facilitate early hypotheses testing by categorizing identified direct relationships to a causality relation ontology. An automated ontology can enhance Machine learning and Artificial intelligence algorithms' accuracy by providing a dynamic knowledge-base that would be far superior to static frameworks.