Why knowledge graphs are essential for working with data efficiently and powerfully

This post is written by Dr Mukta Paliwal, Senior Subject Matter Expert at Persistent Systems.

Up to 50% of inquiries from Gartner customers on the topic of artificial intelligence involve a discussion involving the use of graphics technology, the market research company said in its Top 10 Trends in Artificial Intelligence. data and analytics for 2021. Every large company wants to leverage the available data to provide more insight into doing business at scale. To achieve this, connected data has become a logical need, as it helps contextualize existing organizational data to create knowledge.

Businesses must keep pace with ever-changing data needs. Knowledge graphs can help businesses move away from traditional databases and use the power of natural language processing, machine learning, and semantics to better leverage data.

What is a knowledge graph?

Knowledge graphs represent a collection of interrelated facts about a domain. Essentially, entities and relationships are extracted from unstructured data and stored as a triple: subject-predicate-object. For example, the statement “Captain Marvel is the strongest Avenger” can be split into a subject (Captain Marvel), a predicate (is the strongest) and an object (Avenger) and stored as a triple (Captain Marvel -is strongest- Avenger) as well as other related entities in a knowledge graph of Avengers, popular characters from the Marvel movie.

Essentially, we can define knowledge graphs with these characteristics: 1) they define the real world entities of a domain; (2) they provide relationships with each other; (3) they define rules for possible classes of entities and relations via a certain schema; (4) they allow reasoning to infer new knowledge.

Knowledge graphs may be automatically generated or organized by humans, may have been designed with a rigid ontology or may change over time, may be of different shapes and sizes, and may have been developed by a business or by a community. open source. Regardless of these differences, they help organize unstructured data in such a way that information can be easily extracted when explicit relationships between multiple entities help in the process.

Why use knowledge graphs?

A knowledge graph is self-describing because it provides a single place to find data and understand what it is. As the meaning of the data is encoded alongside the data in the graph itself, the semantic word is associated with the knowledge graph. Knowledge graphs add additional value by providing:

• The context: Knowledge graphs provide context for algorithms by integrating various types of information into an ontology and offer the flexibility to add new derived knowledge on the move. Most TK graphs can simultaneously use various types of raw data.
• Efficiency: Once the desired entities and relationships are available, knowledge graphs provide computational efficiency to query stored data, allowing efficient use of data to generate information.
• Explainability: Large networks of entities and relations provide solutions to the problem of intelligibility by integrating the meaning of the entities available in the graph itself. As such, knowledge graphs become intrinsically explainable.

Where to use knowledge graphs

According to Gartner’s Top 10 Data and Analytics Trends for 2021, Knowledge Graphs are the foundation of modern data and analytics, with capabilities to improve and enhance user collaboration, machine learning models and explainable AI. Although graphics technologies are not new to data and analysis, there has been a change in the way they are used. A knowledge graph brings together machine learning and graphing technologies to give AI the context it needs.

To solve complex problems, where it is necessary to integrate multiple sources of unstructured and semi-structured data from various sources, we need a connected, reusable and flexible database to reflect the complexity of the real world. Connected data, enriched with meaning, allows multiple interpretations from the same data, which is useful for obtaining answers to complex queries in order to obtain information more efficiently.

Organizations are identifying a growing number of use cases for knowledge graphs, including:

Fraud Detection: Identifying fraudulent transactions is the most prevalent use case and has applications in banking, mobile phone transactions, government benefits, and tax evasion. The use of knowledge graphs also improves the detection of fraud, waste and abuse on insurance claims. Machine learning-powered knowledge graphs and reasoning skills allow businesses to better identify fraudulent patterns by traversing many interconnected entities in real time in a large network.

Drug Discovery: Drug discovery is an extremely complex and expensive process. Knowledge graphs have shown great promise in a range of tasks, including drug reorientation, drug interactions, and prioritization of target genes and diseases. A large number of open source databases are integrated with the published literature to create huge graphs of biomedical knowledge. These KGs have become very useful for exploring the relationships between entities such as genes, drugs, diseases, etc. and use them in downstream applications.

Semantic search: a knowledge graph stores the meanings of entities; therefore, research based on a knowledge graph is called “semantic search” or meaningful search. Semantic search is used to improve the accuracy of search results when crawling the Internet or internal systems of an organization. For semantic search to work, with a well-organized knowledge graph, the capabilities of text analysis and indexing techniques are utilized.

Recommendation systems: Recommendation systems are developed to model user preferences for personalized product recommendations. There are a variety of modeling techniques used to develop the recommendation system. Despite their considerable merit, these systems suffer from challenges such as data scarcity, cold start, and recommendation scalability. Recommendation systems based on knowledge graphs can help solve these challenges to some extent. In this approach, user and item entities are connected through multiple relationships. Relationships are used to get a list of likely candidates for the target user, and the path from the target user to the recommended item is used as an explanation for recommended items.

Mukta Paliwal Ph.D. is Senior Domain Expert (Data Science) at Persistent Systems. She leads and consults teams to create and deliver cutting-edge AI / ML-based software solutions across multiple business areas. She has a doctorate. in Applied Machine Learning.