Ontology and Knowledge Graph Development

We are adept at the development of ontologies at different levels of sophistication, from RDF Schema through to OWL with restrictions and inference. Either from scratch, through reuse of existing enterprise or industry ontologies, or by transforming existing data models, UML Models, or XML Schemas. And in supplementing a core ontology with Shapes (SHACL) or Datalog rules.

A key tenet is the focus on business use cases to ensure the ontologies provide business value, and are not over-engineered or attempt to boil the ocean and model everything conceivable.

Ontologies are just part of knowledge graph development, and we can help with all aspects including choice of technology, configuration, automation, integration and SPARQL query development.

Glossary/Vocabulary Management

We believe that natural language terms and definitions are just as important as definitions in logic to ensure business comprehension. We have pioneered an approach, now standardized as OMG’s Multiple Vocabulary Facility, and the subject of a Knowledge Graph Conference presentation, which allows loose coupling between the business language and the logic. So that different business communities can stick with the terms they’re comfortable with, and the enterprise as a whole can avoid long and expensive sessions attempting to get the entire business to agree on universal terms.

Taxonomy and Classification

We believe that taxonomies, typically modeled using SKOS, have a place alongside ontologies for specific use cases, such as support for enterprise search. We have an approach to help identify these cases and provide context-based linkage of different classification schemes with ontologies, and mapping of different schemes with each other.

Semantic Website Markup, using Schema.org

We can supplement knowledge graph-based web content with generated markup using the schema.org set of tags, used by Google and other search engines to enable them to better understand your content and improve your site's visibility in search results. This service can significantly boost your SEO efforts, leading to increased web traffic and a higher engagement rate.

Data Wrangling

Taking pragmatic ontology development further, our data wrangling services support the inflow and outflow of knowledge graph data, through the construction of repeatable pipelines with cleaning, structuring, and enriching raw data (in whatever form), cataloging of datasets, and maintenance of change and provenance information for traceability.

Applying FIBO (Financial Industry Business Ontology)

FIBO is certainly large, complex and hard to work out how to apply for specific use cases. Pete Rivett has been actively involved in FIBO from the start, and combines a deep understanding of FIBO, warts and all, with a pragmatic approach to achieving scalable business value and integration.

Model Conversion and Interchange, using XMI

Pete Rivett is probably the world’s leading expert on XMI, having co-authored the specification and facilitated many conversions involving a slew of different metamodels. He can provide a review capability, develop specific transformations to or from XMI and diagnose interchange issues. 

XSLT Development

Our mentoring service aims to supplement your team's skills and knowledge, improving it over time, and provide a second opinion, objective review, or analysis of risks. This could include formal training, coaching, establishing third party contacts, or embedding with the team.

We can support your selection of technology which can involve analyzing requirements, contacting vendors, benchmarking, and conducting proofs-of-concept (PoC).

Standards Review and Development

Pete Rivett’s 20+ years on OMG’s Architecture Board has given him unmatched expertise in both reviewing and crafting specifications and standards, whether for internal or industry-wide usage.

Data Governance and Metadata Management

Our metadata management service ensures your metadata is accurate, complete, and consistent. We also provide lineage services, tracking the life cycle of your data from its source to its final form. Lineage, different tools, traceability. This enables you to understand how data transforms through systems and processes, allowing for improved data traceability, integrity, and trustworthiness.