Ontotext Invents the Universe So You Don’t Need To

The newest version of Ontotext Platform brings the power of knowledge graphs to everyone.

November 23, 2020 7 mins. read Jarred McGinnis

If you wish to make an apple pie from scratch, you must first invent the universe.

                         Carl Sagan, Cosmos

The quote above eloquently expresses the idea that even something as quotidian as an apple pie is the last link of a chain going back billions of years. The corollary of this statement is that you do not ever actually wish to make an apple pie from scratch, from nothing. You want to start at the very tip of the universe’s innovation. You want elemental particles to have existed for billions of years, the sun and planets created, you want apple trees to be domesticated and the industrial revolution to have made home ovens possible. After all, you just want some pie.

As engineers we can sometimes get lost in the details of the implementation, the architecture, the construction, and we can lose sight of the business need that necessitated the development project in the first place. It is also easy to forget that most users are not going to have the same level of technical expertise, nor should they be expected to. A baker can make apple pie just fine without an understanding of the quantum-level mechanics at play in short crust.

Discoverability, Insights and Decision-Making

Knowledge graphs have been proven to be a powerful, scalable and intelligent technology for solving today’s complex business needs. The ability to define the concepts and their relationships that are important to an organization in a way that is understandable to a computer has immense benefits. Data and content are organized in a way that facilitates discoverability, insights and decision making rather than be bound by limitations of data formats and legacy systems.

Traditionally, semantic technology required specialist expertise that most developers didn’t have. Businesses wanted a way to make pie and not an in-depth understanding of forward-chaining, inferential explosion or SPARQL optimizations. Ontotext’s extensive experience of bringing enterprise-level to national and global brands understands this and has for over a decade strived to make the power of semantic technology accessible. Gartner’s Hype Cycle of Artificial Intelligence 2020, includes Ontotext as one of the exemplar vendors list for knowledge graphs. Ontotext is also on the list of vendors supporting knowledge graph capabilities in their “2021 Planning Guide for Data Analytics and Artificial Intelligence” report.

Developer-Friendly Semantic Technology

The aspiration behind Ontotext Platform 3 is to provide clients with a developer-friendly and an open-source approach to make the building and managing of knowledge graphs simpler, faster, and less expensive. From packaging and deployment to monitoring tools and report generations, the Platform has everything an enterprise needs. One of the key goals of the Platform is to enable developers without specialist knowledge in RDF or SPARQL to create knowledge graph-driven applications and websites. In order to achieve this, the Platform provides new APIs based on GraphQL and JSON, both of which are simple, declarative and capable of handling the majority of use cases. These open technologies have a large active community and a number of tools and frameworks to further support low-complexity, front-end friendly and scalable development to solve business needs using semantics.

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GraphQL has a number of advantages for developers, especially for data-centric applications. It is less demanding in terms of data compared with a REST approach and only loads what you need and when you need it. Data synchronization, microservice orchestration and domains with complex schemas are much easier in GraphQL. GraphQL’s JSON-like syntax will be familiar to developers and is part of the reason for its growth in popularity. By using GraphQL, developers will be happy to avoid the complexities of the knowledge graphs, inference and SPARQL queries.

The newest version comes with the Ontotext Platform Workbench – the web-based administration interface. It simplifies the work of the subject matter experts by lowering the burden of knowing all platform configuration endpoints and commands and streamlines the adoption process by introducing an intuitive graphical interface. The workbench has also simplified the install and configuration process to simplify licensing and OS-specific issues.

Business-User Friendly, Too

It is not only developers whose life will be made easier. With the introduction of Semantic Objects, the Platform provides a high-level of abstraction to business users who are unlikely to have the technical expertise it takes to build a knowledge graph from scratch. The goal is to make it easier to encode the business knowledge of personnel such as business analysts who have the best understanding of the business and the most well-rounded domain knowledge.

Semantic Objects and the Semantic Objects Modeling Language (SOML) is a simple way to describe business objects or domain objects. The Platform is able to generate the initial Semantic Objects model, which can be modified and extended by the business user without having to work directly with the underlying knowledge graphs. The business user can focus on describing the attributes and associations with other semantic objects in a way that they are comfortable with that will enrich the underlying knowledge graph and leverage the power of the semantic technology.

Ontotext Platform can be employed for a number of applications within an enterprise.

Content Enrichment and Metadata Management

The value of metadata for content providers is well-established. When that metadata is connected within a knowledge graph, a powerful mechanism for content enrichment is unlocked. Through collaboration between knowledge engineers, who tune the models and rules, and subject matter experts, who modify the taxonomies and ontologies, concept extraction is improved and important or missing concepts are added.

The resulting detailed and structured description of content serves as a basis for semantic indexing, search and exploration as well as the ability to create dynamic and automated content publishing.

Semantic Search and Insight Engines

The vast majority of that information is unstructured and unstructured means undiscoverable. Ontotext’s unique offering of NLP technology and semantic-enrichment coupled with the Platform’s knowledge graphs solves this problem. Making the data machine-readable and meaningful opens it up to the growing sophistication of Artificial Intelligence (AI). AI is augmenting the tools and interfaces that make it easier for human users to find and integrate information quickly and sufficiently so that informed business decisions can be made faster and with better results.

Machine learning coupled with knowledge graphs is already collecting, categorizing, tagging and adding the needed structure to the endless (and useless) swathes of unstructured data. Enterprises in the financial services sector have been using the Platform to seamlessly integrate and organize 3rd party content and create predictive analytic tools at a fraction of the cost it took before.

Continuous Data Operations and Data Management for Analytics and Master Data Management

Data silos are as costly as they are inevitable. Ontotext Platform brings business value by not trying to solve the inevitable data silo problem but making it irrelevant. The use of knowledge graphs doesn’t try to enforce yet another format on the data but instead overlays a semantic fabric, which virtualizes the data at a level of abstraction more closely to how the users want to make use of the data.

Multiple and varying ‘views’ of the data are now possible without modifying the data at its source and or the host system. The users are freed from having to negotiate the particularities of where the data is, how to get it and the effect of changes on that data for others. Users can create on-the-fly views of the data without duplication and without being beholden to the idiosyncrasies of the data’s origins and tailored to the user’s security privileges, technical ability and needs.

With its cloud-agnostic infrastructure, Ontotext Platform can operate in cloud and on-premise environments. It is an enterprise ready platform supporting LDAP integration, multi factor authentication and performance monitoring dashboards.

And now the business can get on with baking pies because the Platform is doing everything else from scratch.

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Article's content

Technical Author at Freelancer

Jarred McGinnis is a managing consultant in Semantic Technologies. Previously he was the Head of Research, Semantic Technologies, at the Press Association, investigating the role of technologies such as natural language processing and Linked Data in the news industry. Dr. McGinnis received his PhD in Informatics from the University of Edinburgh in 2006.

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