Professional services

Each step in an organisation’s Digital Transformation process will bring with it both new opportunities and new challenges that will require specific services tailed to your needs in terms of change, adaptation and growth.

The GNOSS team’s cumulative knowledge and experience spans more than 15 years of building Knowledge Graphs and intelligent platforms for widely-ranging, highly-visible, high-impact and complex projects. This has enabled us to configure a complete service package deployed as a digital semantic ecosystem on the GNOSS platform that caters to organisations’ evolving needs and encompasses the construction and deployment life cycle from start to finish.

With the aim of continuing to develop our partner ecosystem and expand through it the number of companies capable of developing new projects in these areas, so that they can continue to provide differential value to their end customers, we offer our partners preferential conditions in case they need to hire our professional services. If you would like to know more about our partner programme, please contact us.

Business and Strategic Consulting services: knowledge engineering

  • Structured project methodology to help you take advantage of semantic technologies in an effective and agile way.
  • User eXperience design
  • Linked Data strategy development

See the Prado Museum Digital Model

Business and Strategic Consulting services: knowledge engineering -- Business and Strategic Consulting

Ontological Engineering

  • Analysis and benchmarking of ontologies and reference vocabularies
  • Ontology modeling
  • Applied ontology engineering
  • Ontology network construction (ontology programming)
Ontological Engineering -- Ontological Engineering
  1. Formal markup languages

Ontological engineering aims to make the knowledge contained in digital resources created by people, contained in software applications or on the internet, explicit and interpretable for machines. To do this, it uses formal markup languages ​​such as OWL, an acronym for Ontology Web Language, and RDF, an acronym for Resource Description Framework. Ontological engineering thus makes it possible to compute the meaning of the document content and not only its format, which in turn enables users to integrate and handle more and more complex information, with greater efficiency and security.

OWL is a markup language designed to publish and share data on the World Wide Web or global computer network using ontologies. OWL aims to provide a markup model built on RDF and encoded in XML. For its part, RDF or Resource Description Framework is a set of specifications from the World Wide Web Consortium (W3C) designed as a data model for metadata. The RDF data model is based on the idea of ​​making declarations about resources (in particular, web resources) in the form of subject-predicate-object expressions. These expressions are known as triples and can be computed with First Order Logic, which has an expressive power far superior to that of propositional logic, which is used in transactional computer systems. The subject indicates the resource and the predicate denotes attributes of the resource and expresses a relationship between the subject and the object

  1. Ontological Engineering and Knowledge Graphs

An ontological project is the condition to represent the data of a set of distributed systems, often heterogeneous, and to consolidate it in a Knowledge Graph. The GNOSS Ontological Engineering methodology is aimed not only at representing the data in order to generate a dataset reusable by third-party machines, but also at solving the set of operations and questions that the different interest groups may want to carry out on that knowledge thus represented.

  1. Ontological engineering and semantic web

OWL and RDF, as well as other components, make possible the Semantic Web project, a set of activities developed within the World Wide Web Consortium aimed at creating technologies for publishing machine-readable data or computer applications. It is based on the idea of ​​adding semantic and ontological metadata, which describes the content, meaning and relationship of the data. This metadata must be provided in a formal way, so that it can be automatically evaluated by machines. The goal is to improve the Internet by expanding interoperability between computer systems using "smart agents". Intelligent agents are programs that search for information that is meaningful to people, but that do not require human operators.

  1. Ontological Engineering and Artificial Intelligence

Automated processing of non-interpretable information by software agents can be enhanced by adding rich semantics to corresponding resources, such as video files.

  1. Ontological engineering and interoperability

Ontological engineering is the most efficient means of solving interoperability problems derived from the fact that information related to a given set of entities is often distributed in diverse heterogeneous systems. Ontological engineering is the means to overcome this class of semantic obstacles caused by the fact that the information is stored in silos not connected between them. In short, it is the means to "unblock" and link the information contained in our systems.

One of the approaches to the formal conceptualization of represented knowledge domains is the use of machine-interpretable ontologies, which provide data structured in, or based on, RDF, RDFS and OWL.

Data integration and development of semantic AI solutions

  • Data integration
  • Integration with systems
  • Customized data portals for better analytics and visualization
Data integration and development of semantic AI solutions -- Development of semantic AI solutions

AI Services and Natural Language Processing (NLP)

Consultancy specialized in NLP-NLU services for information enrichment and automatic categorization

  • PLN
  • Machine Learning
  • Deep Learning
  • Model for integration of AI results in a knowledge graph
AI Services and Natural Language Processing (NLP) -- AI Services