Ontotext Knowledge Graph Platform: The Modern Way of Building Smart Enterprise Applications

This article was originally published in a special report “Empowering Machine Learning with Knowledge Graphs” by Database Trends and Applications magazine. DBTA covers data and information management, big data, and data science. In addition, their website, dbta.com, connects visitors with white papers, webinars and other learning opportunities in the field.

March 19, 2020 4 mins. read Milen Yankulov

According to an article in Harvard Business Review, cross-industry studies show that, on average, big enterprises actively use less than half of their structured data and sometimes about 1% of their unstructured data. Consequently, many data leaders today are striving to overcome these barriers by streamlining their enterprise knowledge management processes and practices.

The knowledge graph model is one way of doing it and, not surprisingly, it has been in increasing demand in the last decade.

This model represents a collection of interlinked descriptions of entities (real-world objects, events, situations or abstract concepts) where:

  • descriptions have a formal structure that allows both people and computers to process them in an efficient and unambiguous manner; and
  • entity descriptions contribute to one another, forming a network, where each entity represents part of the description of the entities related to it.

Why Enterprise Knowledge Graphs?

The main market driver generating demand for knowledge graphs is that B2B clients are on the lookout for intelligent knowledge management solutions that work the same way as the solutions Apple, Amazon, Google and Microsoft provide to their B2C users. The most common questions business owners ask are: “Can I have a chatbot that can access my enterprise information? Can my internal enterprise services have the same scope of intelligent features as popular consumer web services?”

The first challenge here is how to enable agile enterprise information management. The many data warehouse systems designed in the last 30 years present significant difficulties in that respect. When designing a system’s architecture, it’s impossible to know from the start all the ways data will be used. Enterprises need flexible systems that can evolve as their business and data evolve.

The second challenge is how to unlock the knowledge that resides in various siloed systems. Historically, most transactional data systems have been designed to solve a particular business problem. To access this knowledge, enterprises need tools to help them integrate the data scattered across the organization.

The third challenge is how to combine data management with analytics. In today’s business climate, data quality and governance are no longer enough. Enterprises also need to incorporate machine learning algorithms for the smart interpretation of this data.

Knowledge graphs offer a smart way out of these challenges. Due to their native graph structure, they can be extended easily with new data, which provides the necessary agility. They use semantics – it means that data is organized by meaning and put into the right context, which prevents data silos. Finally, they combine classical technologies like data governance and data management with modern analytics.

Ontotext Knowledge Graph Platform

Ontotext Platform is a platform for organizing enterprise knowledge into knowledge graphs. It consists of a set of databases, machine learning algorithms, APIs and tools for building various solutions for specific enterprise needs. Its architecture is based on open interfaces and standards.

The platform enables simpler and faster graph navigation. The GraphQL user-centric API exposes an additional interface to start consuming complex information fast and at a low cost.

The main benefits of using Ontotext Platform are:

  • automatic generation of GraphQL access from ontologies;
  • text analysis that extracts knowledge from unstructured documents and generates semantic metadata;
  • efficient SPARQL query generation; • easy application integration, including non-semantic sources;
  • developer-friendly tooling;
  • authorization and authentication;
  • high availability, query and search via GraphDB, featuring reasoning, semantic similarity and ranking;
  • the ability to scale data, query and transaction loads via integration with ElasticSearch and MongoDB;
  • cloud-agnostic deployment with Kubernetes and easy extension with custom services packaged as Docker containers.

Do you want to learn more about how Ontotext Knowledge Graph Platform can help you minimize your technology and architectural risk and save time and effort when delivering your enterprise solutions or PoC?

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Article's content

Marketing Manager at Ontotext

Milen Yankulov has a vast experience in both traditional and digital marketing communications. His professional interests are related but not limited to Web and News Medias, Semantic Search and Social channels and all digital disruptions that change the way we communicate and do business.

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