Knowledge Graphs have been touted as the future of Analytics in AI, they bring together Machine Learning and Graph technologies to give AI the context it needs.
‘Up to 50% of Gartner client inquiries around the topic of AI involve a discussion around the use of graph technology.’ – Gartner Top 10 Data and Analytics Trends for 2021
Knowledge Graphs answer complex queries through AI-based decision making by
- Understanding co-relations in multi-source data and bridging the data silos
- Undertaking a risk assessment
- Presenting the data in a context-based format
- Detecting potential regulatory & compliance issues
- Predicting the ultimate consequences with reasoning
Read this blog to find out why enterprises are using Knowledge Graphs, and their real world application across various industries.
Empowering Enterprise Data to Deliver Better Results
- Smarter Data Insights: With exponential growth in technology and automation, every large enterprise is wanting to exploit available data in order to bring more and more insights for conducting business at scale.
- Collate Data from Disparate Sources: For integrating the multiple unstructured and semi-structured sources of data coming from a variety of sources, we need a connected, reusable, and flexible data foundation to reflect the complexity of the real world. Knowledge Graphs can help us achieve this and also enable enterprise data transformation from columns to context.
- Intelligent Searches: Knowledge Graphs can transform data management through easy query resolution, information retrieval, explainable reasoning, and flexible analytics!
- Expedite Digital Transformation: Knowledge Graphs can help companies move away from the traditional databases and use the power of Natural Language Processing, Machine Learning and Semantics to better leverage data, the new oil. They also offer ‘digital twins’ technology which has the potential to accelerate enterprise-driven digital transformation.
Real World High-value Use Cases of Knowledge Graphs
Although graph technologies are not new to data and analytics, there has been a seismic shift in the way they are used currently. Here are few practical applications of Knowledge Graphs across various industries for your reference:
- Fraud Detection: Identifying fraudulent transactions is the most famous use case that has application in banking, mobile phone transactions, ‘benefits fraud’ and ‘tax fraud’ in government, claims fraud in insurance, etc.
Knowledge Graphs empowered by Machine Learning and reasoning capabilities allow companies to better identify fraudulent patterns by traversing many real-time interconnected entities in a large network. - Drug Discovery: Drug discovery is an extremely complex and costly process. In recent times, as compared to other Machine Learning techniques, Knowledge Graphs have shown a considerable promise across a wide range of tasks, including drug repurposing, drug-drug interactions, and target gene-disease prioritization.
A large number of open-source databases are integrated along with published literature to create huge biomedical Knowledge Graphs. These Knowledge Graphs help in mining the relations between entities like genes, drugs, diseases, etc. and draw valuable inference to add new relations in these entities. - Semantic Search: A Knowledge Graph stores meaning of the entities. Hence, Knowledge Graph-powered search is referred to as ‘semantic search’. It is used to improve the accuracy of search results while exploring the internet or the internal systems of an organization.
For semantic search to work, along with a well curated Knowledge Graph, the capabilities of text analytics and indexing techniques are utilized. - Recommendation Systems: Recommender systems are developed to model user preferences for personalized recommendations of products. There are a variety of modelling techniques used to develop the recommendation system. Although they have tremendous merit, these systems face challenges like data sparsity, cold start, and expandability of the recommendations. Knowledge Graph-based recommendation systems are able to solve these challenges to a certain extent. In this approach, user and item entities are connected through multiple relationships. These relations are used to obtain a probable candidate list for target user, & the path between target user and recommended item is used as an explanation.
In the next instalment of the blog we will understand about Knowledge Graphs in detail, when, why and where to use Knowledge Graphs and how to create them, so don’t miss it. At Persistent, we are developing transformational Knowledge Graph-based solutions in Health Care and Banking & Finance Industries. To learn more about Persistent’s Artificial Intelligence & Machine Learning offerings, connect with us at: airesearch@persistent.com
