Knowledge Graph Generation for Financial Databases

People who use financial databases are aware of the hardships of ensuring information is structured and legible. Don’t worry! Knowledge graphs are here to help.

Data volume, nowadays, continues to grow uncontrolled and those datasets are hard to process and draw insights from. Most of the current data comes from diverse sources in unstructured formats.

Information databases based on financial reports can include more than ten years of historical data from several companies. Moreover, these datasets differ from one another regarding, for instance, creation date, formats, and even languages.

Sorting this information isn´t easy, and information is not the same as knowledge. Therefore, people are struggling to solve this challenge through big data solutions based on the latest technologies. Artificial intelligence and linguistics help tag and classify this data into semantic representations called knowledge graphs. 

Knowledge graphs can represent meaning by narrowing down terms from large amounts of unstructured data.

Computational linguistics in combination with knowledge graphs process documents such as financial reports, news, and other text documents to provide a clear view of business and products.

 

Graphical representation of knowledge about Microsoft startup acquisitions extracted from news.

Bitext is partnering with one a leading financial information provider to accelerate the creation of a financial information database.

By using our multilingual entity extraction and event detection technology, the system can extract structured information from text-based big data sources, including news articles, legal documents, and financial reports, storing the resulting entity relation information in a large-scale graph database.

In addition to powering the gathering of data, Bitext is also adding Natural Language Query capabilities to the system, allowing users to ask questions such as ‘what suppliers does Apple have in China?’ The system translates this question into a structured query, answered by the graph database, that allows users to perform sophisticated searches without having to be experts in the underlying technology.

Bitext knowledge graphs are not only considered very accurate in terms of semantics and syntax, even with subtle differences in meaning, but also are seen as a quick and multilingual solution to organize large amounts of data. Bitext can build the richest automated graph in the market.

For machines to interact with humans, both must have the same context or references and share the same world knowledge. 

For example, in a US context: George Washington rings a bell as a former US President; Washington is also a state and the capital city of the US; there are also streets called George Washington… The Warriors are the Golden State Warriors in the US, but a rugby team in the UK.

Bitext provides this world knowledge, typically as knowledge graphs, so it can be integrated with any AI application, as well as ad-hoc knowledge extraction capabilities to scan user-specific sources (news, social media, patents…).

Knowledge graphs are the best alternative to draw insights from various sources of financial data that grow in volume and complexity with time. Bitext’s Knowledge Graphs extract, process, and link concepts from various data sources. Take a look at our solution and contact us to get a free quote customized for your business.

• Request a Demo: Our NLP framework offers a variety of services that can be combined to achieve the best results.