Knowledge Graph

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Turn Your Data into Knowledge

Turning data into knowledge is vital for strong customer partnerships, especially during a recession. It represents organisational knowledge, domains and deliverables that both humans and machines can understand. To this end, the knowledge graph serves as the foundation of AI, which provides potential cognitive advancements, along with optimised approaches to achieve comprehensive business goals.

What is the Knowledge Graph?

Data exists everywhere, and Artificial Intelligence is extremely valuable for deciphering unstructured or sorting large amounts of data. Manually, this can get taxing, but with the help of machine learning, you can transform complex data into knowledge graphs, which can easily be comprehended by both the business leaders and the customer.
Knowledge graphs are used everywhere, for example, in chatbots, recommended systems, and internet searches. In the eCommerce industry or the banking sector, knowledge graphs help analyse the products and demands of a specific product and help customers use virtual assistants. Converting domain-specific data sources into knowledge graphs is a sophisticated task that requires many resources and the development of a large amount of maintenance data.

Benefits - Make Better Decisions with Data

Enhanced context for better results

Knowledge graphs help code hierarchy, metadata, unstructured and complex data and various properties. It also helps query the integrated data, uncover patterns and discover integral connections to get clear and descriptive results.

Save time

Manually deciphering large amounts of data using traditional techniques can be time-consuming and expensive, involving many workforces in related tasks. AI-enabled knowledge graphs can help convert data into understandable material that saves time, effort and money.

Future-proof data model

There is always going to be a steady flow of data, such as a new external data stream, a new data source needed for the next version, a new acquisition with its data chaos, etc. You can use an extensible data model to easily merge the new sources while preserving the original schema and metadata, thus becoming a future-proof data model.

High performing graphs

Knowledge graph technology helps provide relevant facts and contextual answers to a particular question, rather than extensive search results that contain many relevant documents and news. A full-fledged knowledge graph can provide your organisation with a solid foundation and fundamental solutions for any intelligent application with this integration, enabling you to get good search results.

Designed for what's important to your data

Knowledge graphs represent real-world entities and help in understanding complex relationships. The knowledge graph can maintain multiple perspectives at the same time.

Reveal insights

Knowledge graphs reveal insights, search for information and diligently dig through the stack of documents to find specific phrases, numbers, etc. It compiles a network of “things” and the facts related to those "things," creating a strong point of information, facets and attributes of a business, such as a project, product, employee or skill. Each graph can create relatable databases and be connected to other graphs and relational databases.

Easily layered into existing infrastructure

Virtualisation provides resource-saving access to various data, both locally and in the cloud. Access data without displacing or duplicating the original content, preserving original ownership and governance while avoiding data spikes.

Use Cases

The knowledge graph helps to construe natural language queries and enables you to understand the terms, units, guidelines and associations that the search represents or implies.

The knowledge graph is considered a tool that works with unstructured content. These have proved to be great tools for deciphering data and authorising people, even in complex environments.
Once the knowledge graph is complete, you can introduce chatbots or a simple search interface to allow customers and other business users to better understand the charts, graphs, and reports. Enterprises use this AI and machine learning technology to extract and discover more profound and subtle patterns and simplify data to help them make decisions.

Let us take an example of data scientists working on 15000 data sets of a large government agency. Previously, the company created a data dictionary to find the datasets they needed and created a single platform for organising most datasets. This made access and reliability more difficult. Knowledge graphs gave them more control over the data, allowing data scientists to search records based on relationships between data items and movement between records. You can also establish who can view the records and how each record changes. The customer now has a set of forms and an application that allows them to traverse a very complex area, including the institution in which they work. With the help of knowledge graphs, you can now view data without the restrictions of the dataset you were previously using.

With the help of knowledge graphs, incorporating them into your strategic plan, you can move from proactive risk mitigation to proactive risk management. It helps reduce the risks and proactively reports them to regulatory agencies in case of non-compliance. Converting complex data into knowledge graphs can help enterprises:
• Identify any error/failure in the network
• Cover theoretical risk and answer complex questions
• Reuse the data
• Maintain processes with important people
• Assess the overall impact of each threat

What Makes Us Unique

• We convert complex data into effective knowledge graphs and help you differentiate between multifarious topics, for example - auto sensors vs biosensors, funds vs cash, etc.
• We help you identify key entities like projects, products, people, time, organisations, etc.
• We help you with meta context, pattern recognition, ontology and taxonomy
• Sentiment analysis and generation are our key agenda
• We help you decipher and convert structured and unstructured data

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