Why KG? Because a knowledge graph turns your data into knowledge

Knowledge plays indispensable role in deeper understanding of content

The information of the real world isn’t made up of a set of column names that we can put into a table. Tabulation is a method we’ve been using for many years to de-complicate the problem domain. Unfortunately, this process removes the important concept called context which means the inter-relations between data-points. 

In modern machine learning, raw data is the preferred input for our models. Operations on data without context is practically useless to a user in another business function who needs to understand and analyze that data. To address this problem today, many IT and data science teams spend 80% of their time curating, integrating, and preparing data for analysis rather than actually analyzing it. Data engineers write custom Python scripts for each integration. These are hard to maintain, time-consuming, and often insecure. As long as we assume that all relevant and irrelevant information is present in the input data, we can design deep models that build up intermediate representations to sift out relevant features. However, these models are often domain specific and tailored to the task at hand, and therefore unsuited for learning on heterogeneous knowledge: information of different types and from different domains. Despite interconnected tabular data linked together in some way for ML input as features, the challenges are, increased dimensionality, normalization (1D-Norm) of data which is not natural representation, repetition of data on merging different aggregated data across tables.

With the advent of big data technologies, healthcare data captured and stored at multiple granular levels and in multiple formats. In the healthcare realm which includes hospitals, pharmaceuticals, insurance companies, digital promotion channels etc., have an enormous amount of data in structured tables. However, significant amounts of the big data remain underutilized due to data isolation, distribution, and heterogeneity. Machine learning models supposes the observations are not dependent however, the real-world information is interconnected. Now the representation learning is the biggest challenge to address.

Knowledge graph

The above said problems can be addressed with one solution which is Knowledge graph (KG), it enables to capture, organize, and query a large amount of multi-relational data and making it possible to infer new knowledge by reasoning engine. Graphs often referred as Knowledge graph can share data seamlessly and uncover relationships hidden in broad ranges of data sets, making it invaluable to business teams. The fundamental difference between graph databases and relational databases lies in how relationships are handled. In a graph database, relationships are driven by data points. In a relational database, relationships are driven by columns in data tables.

Commercial team is always insight hungry. you can choose a graphs for uncovering insights that could otherwise stay hidden forever if you choose to stick with the relational database. Knowledge graphs are used solely for deriving insights.

Benefits