# Strategic Intelligence Evidence Register This register stores the public evidence behind the Workstream 5 deck. It is organized by organization so each claim can be traced back to public material. ## Palantir Technologies - Organization type: Ontology-centric - Sector: Enterprise, defense, operations - Adoption wave: 2000s+ - Semantic role: Mission semantics, interoperability, and decision models - Why it matters: Probably the most visible commercial success story for ontology as operating architecture. - Public signal: Palantir describes the Ontology as the system at the heart of its architecture, integrating data, logic, action, and security so humans and AI agents can collaborate on operational decisions. - Operational use: Foundry, Gotham, and AIP use object types, properties, links, actions, functions, and governed workflows to turn enterprise data into decisions and system write-back. - Strategic implication: The market is moving from dashboards to decision-centric semantic systems. TotalEnergies should position TSF as the industrial meaning layer that makes this possible without surrendering semantic authority to a vendor platform. - Sources: - [Palantir Ontology system](https://palantir.com/docs/foundry/architecture-center/ontology-system/) - [Palantir Foundry core concepts](https://palantir.com/docs/foundry/ontology/core-concepts/) ## TotalEnergies - Organization type: Ontology-centric - Sector: Energy and industrial operations - Adoption wave: 2022+ public evidence - Semantic role: Industrial asset, platform, and operations semantics - Why it matters: One of the most visible industrial semantic-framework programs among large energy companies. - Public signal: Public papers describe the TotalEnergies Semantic Framework as a methodology and open-source toolset using semantic web technologies, formal ontologies, ISO 15926 principles, OWL, SKOS, and SousLeSensVocables. - Operational use: TSF targets lifecycle asset data, business-object standardization, source-data mapping, standards alignment, and reusable domain ontologies for enterprise interoperability. - Strategic implication: TotalEnergies can credibly speak as a practitioner, not an observer. The benchmark should highlight that TSF/SousLeSens already sits in the same family as mature semantic programs in pharma, defense, finance, and aerospace. - Sources: - [TSF CEUR paper](https://ceur-ws.org/Vol-3240/poster1.pdf) - [I-ESA TSF paper](https://www.logilab.fr/file/17009607/raw/20220419_IESA_TSF.pdf) ## OBO Foundry - Organization type: Ontology-centric - Sector: Life sciences ontology ecosystem - Adoption wave: 2001+ - Semantic role: Biomedical concepts, evidence, and discovery graph - Why it matters: The gold standard for open, interoperable biomedical ontologies. - Public signal: The OBO Foundry coordinates community development of interoperable biological and biomedical ontologies and publishes principles, tools, and reusable ontology resources. - Operational use: OBO ontologies such as ECO and OBI are used to encode evidence, investigations, assays, and biomedical concepts across research resources and data platforms. - Strategic implication: OBO proves that ontology maturity requires governance, design principles, reuse, versioning, and community discipline. TSF should borrow that operating model, not only the technology. - Sources: - [OBO Foundry](http://obofoundry.org/) - [OBO Evidence and Conclusion Ontology](https://obofoundry.org/ontology/eco) ## United States Department of War / DoD and Intelligence Community - Organization type: Government semantic standards - Sector: Defense and intelligence - Adoption wave: 2024 baseline standard - Semantic role: Formal ontology standards for mission interoperability - Why it matters: Defense is treating ontology as a baseline standard, not an optional data-modeling exercise. - Public signal: Public reporting states that DoD and Intelligence Community chief data officers defined Basic Formal Ontology and Common Core Ontologies as baseline standards for formal ontology work. - Operational use: BFO provides the top-level ontology foundation. CCO provides mid-level reusable classes for mission-specific ontologies. Together, they target data sharing, federated search, analytic efficiency, and interoperability across complex mission systems. - Strategic implication: The defense lesson is standards before scale. Energy will need the same discipline for assets, risks, maintenance, and AI if semantic work is to survive beyond pilots. - Sources: - [BFO and CCO baseline standards](https://www.buffalo.edu/cas/philosophy/news-events/news/smith-top-level-ontologies.html) - [Common Core Ontologies mission](https://commoncoreontology.github.io/cco-webpage/) ## U.S. Customs and Border Protection / CBP - Organization type: Government ontology development - Sector: Homeland security, border operations - Adoption wave: 2020s+ - Semantic role: RDF knowledge graph for real-time sensor fusion and mission response - Why it matters: CBP shows ontology driving live operational decisions under time pressure, not just exchange standards. - Public signal: CBP Border Patrol leadership is leading an ontology development effort across CBP and DHS to support enterprise-wide data integration and knowledge sharing for mission-critical border operations. - Operational use: CBP proof of concept used RDF triples linking sensors, acts of sensing, and unmanned aerial vehicles to give field agents mapped, real-time airspace information for drone interdiction. A 30-day test collected 17,000 records meeting time and space criteria for qualified interdiction scenarios at the border. - Strategic implication: The CBP case shows that semantic models pay off when response windows are measured in minutes and many sensor feeds must fuse into one operational picture. - Sources: - [How US Homeland Security Plans to Use Knowledge Graph](https://www.semanticarts.com/how-us-homeland-security-plans-to-use-knowledge-graph/) ## National Institutes of Health / NCBO BioPortal - Organization type: Government and health infrastructure - Sector: Biomedical infrastructure - Adoption wave: 2005+ - Semantic role: Biomedical concepts, evidence, and discovery graph - Why it matters: NIH-backed infrastructure has made biomedical ontologies searchable, reusable, and queryable at scale. - Public signal: BioPortal describes itself as the world's most comprehensive repository of biomedical ontologies and provides search, annotation, mappings, APIs, and SPARQL access. - Operational use: BioPortal exposes more than a thousand biomedical ontologies and millions of mappings, enabling cross-ontology lookup, annotation, and linked-data access for healthcare and life sciences. - Strategic implication: Ontology value grows when it is exposed as shared infrastructure. TSF should be designed as a consumable service for platforms, not a private modeling archive. - Sources: - [NCBO BioPortal](https://bioportal.bioontology.org/) - [BioPortal biomedical ontology repository](https://www.bioontology.org/) ## NATO research community - Organization type: Government and defense interoperability - Sector: Defense interoperability - Adoption wave: 2007+ - Semantic role: Mission semantics, interoperability, and decision models - Why it matters: NATO research shows ontology as an interoperability tool for coalition command and control. - Public signal: NATO research groups IST-075 and IST-094 developed semantic interoperability frameworks using ontologies, mediation, and model harmonization for heterogeneous C2 systems. - Operational use: The work targets command-and-control information exchange, semantic service discovery, tactical military networks, and translation across heterogeneous abstractions. - Strategic implication: Ontology becomes valuable where multiple organizations must act together without one canonical system. That mirrors TotalEnergies platforms, affiliates, contractors, and vendors. - Sources: - [Towards semantic interoperability](https://foi.se/download/18.7fd35d7f166c56ebe0b1008f/1542623794163/Towards-semantic-interoperability_FOI-S--3827--SE.pdf) - [Semantic service discovery in tactical military networks](http://dodccrp.org/files/IC2J_v4n1_02_Johnsen.pdf) ## Google - Organization type: Big tech and semantic infrastructure - Sector: Search and AI infrastructure - Adoption wave: 2012+ - Semantic role: Semantic infrastructure for search, data, graph, or AI - Why it matters: Google made knowledge graphs mainstream for entity-centric search. - Public signal: Google introduced the Knowledge Graph as an intelligent model that understands real-world entities and relationships: things, not strings. - Operational use: The Knowledge Graph supports entity disambiguation, knowledge panels, search enrichment, and relationship-based understanding across public and licensed sources. - Strategic implication: Google shows the shift from text matching to entity understanding. For TotalEnergies, the equivalent move is from document retrieval to governed operational entities and relationships. - Sources: - [Google Knowledge Graph announcement](https://blog.google/products-and-platforms/products/search/introducing-knowledge-graph-things-not/) - [Google Knowledge Graph overview](https://en.wikipedia.org/wiki/Knowledge_Graph_(Google) ## Microsoft - Organization type: Big tech and semantic infrastructure - Sector: Enterprise data and AI - Adoption wave: 2025+ preview - Semantic role: Semantic infrastructure for search, data, graph, or AI - Why it matters: Microsoft is now making ontology a native enterprise data product concept inside Fabric. - Public signal: Microsoft Fabric IQ documentation describes ontology as an enterprise vocabulary and semantic layer that unifies meaning across domains and OneLake sources. - Operational use: Fabric can generate ontology items from Power BI semantic models, define entity types, properties, relationships, data bindings, graph views, and agent-consumable concepts. - Strategic implication: Microsoft confirms that ontology is entering mainstream enterprise platforms. TSF must define what Fabric consumes, otherwise Fabric-specific semantics can become the de facto authority. - Sources: - [Microsoft Fabric ontology overview](https://learn.microsoft.com/en-us/fabric/iq/ontology/overview) - [Generate ontology from semantic model](https://learn.microsoft.com/en-us/fabric/iq/ontology/concepts-generate) ## Amazon Web Services - Organization type: Big tech and graph infrastructure - Sector: Cloud graph infrastructure - Adoption wave: 2018+ - Semantic role: Semantic infrastructure for search, data, graph, or AI - Why it matters: AWS provides the infrastructure layer for RDF, SPARQL, and enterprise knowledge graphs through Neptune. - Public signal: Amazon Neptune supports RDF and SPARQL; AWS guidance shows how OWL ontologies can be loaded and queried, and how reasoning can be federated with engines such as RDFox. - Operational use: Neptune is used as a managed graph database for RDF and property graph workloads, including knowledge graphs, drug discovery, fraud, recommendations, and network security. - Strategic implication: Cloud providers validate graph infrastructure, but not semantic governance. TotalEnergies still needs TSF to define concepts, rules, and ownership above storage services. - Sources: - [Amazon Neptune SPARQL documentation](https://docs.aws.amazon.com/neptune/latest/userguide/access-graph-sparql.html) - [Model-driven graphs using OWL in Neptune](https://aws.amazon.com/blogs/database/model-driven-graphs-using-owl-in-amazon-neptune/) ## IKEA - Organization type: Consumer goods and retail - Sector: Consumer goods, retail, and digital experience - Adoption wave: 2022+ public evidence - Semantic role: Product and experience knowledge graph - Why it matters: A rare public consumer-goods example where ontology and knowledge graph practice directly support product discovery, recommendations, and customer experience. - Public signal: IKEA public material describes a knowledge graph with three layers: concepts, categories, and data. The concept layer functions as an ontology, while category vocabularies and product instances power customer-facing digital experiences. - Operational use: IKEA uses the graph to represent home furnishing knowledge, product attributes, controlled vocabularies, business rules, recommendations, search, navigation, and APIs for internal teams. - Strategic implication: IKEA proves that ontology is not only for defense, pharma, or finance. When product and operational context matters at scale, semantic models make recommendations explainable and reusable across channels. - Sources: - [IKEA Knowledge Hub](https://ingress.prod.knowledge.ikea.net/) - [IKEA knowledge graph layers](https://medium.com/flat-pack-tech/ikeas-knowledge-graph-and-why-it-has-three-layers-a38fca436349) - [IKEA recommendations at KGC 2023](https://blog.metaphacts.com/building-explainable-trustworthy-recommendation-systems-ikea-at-kgc2023) ## EDM Council FIBO - Organization type: Financial services standard - Sector: Finance - Adoption wave: 2014+ - Semantic role: Financial meaning, lineage, risk, and entity resolution - Why it matters: The most important open financial ontology standard. - Public signal: FIBO defines financial business concepts and relationships in OWL and Description Logic, with OMG standardization and industry review. - Operational use: FIBO provides a common vocabulary for contracts, instruments, securities, derivatives, indices, and regulatory reporting concepts. - Strategic implication: Finance shows how formal meaning becomes risk control. Energy needs the same precision for assets, maintenance evidence, production, emissions, and economic impact. - Sources: - [EDM Council FIBO](https://edmcouncil.org/frameworks/industry-models/fibo/) - [FIBO specification](https://spec.edmcouncil.org/fibo/) ## Goldman Sachs / FINOS Legend - Organization type: Financial services implementation - Sector: Finance - Adoption wave: 2020+ - Semantic role: Financial meaning, lineage, risk, and entity resolution - Why it matters: A rare public example of a major bank open-sourcing its internal data modeling and governance platform. - Public signal: Goldman Sachs open-sourced Legend through FINOS as a modeling, governance, lineage, and collaborative data platform for financial services. - Operational use: Legend provides a visual modeling environment, execution engine, mappings, SDLC, and collaborative model development for regulated financial data. - Strategic implication: The regulated-data lesson is that business meaning must be modeled, governed, versioned, reviewed, and executable by engineering teams. - Sources: - [FINOS Legend case study](https://www.finos.org/hubfs/FINOS/assets/FINOS%20Legend%20Case%20Study%202021.pdf) - [Goldman Sachs open-sources Legend](https://www.finos.org/press/goldman-sachs-open-sources-its-data-modeling-platform-through-finos) ## JPMorgan Chase - Organization type: Financial services implementation - Sector: Finance - Adoption wave: 2021+ public evidence - Semantic role: Financial meaning, lineage, risk, and entity resolution - Why it matters: A public example of enterprise knowledge graphs used for mission-critical financial applications. - Public signal: JPMorgan Chase publications describe knowledge graphs used across the organization for risk assessment, fraud detection, investment advice, and financial-news entity linking. - Operational use: Entity linking connects company mentions in textual sources to entities in a company knowledge graph, enabling alerts and analysis over enterprise-specific entities. - Strategic implication: The lesson for TotalEnergies is that entity resolution is not peripheral. It is a core capability when operational decisions depend on noisy documents and inconsistent names. - Sources: - [JEL at JPMorgan Chase](https://arxiv.org/html/2411.02695v1) - [AAAI JEL publication](https://ojs.aaai.org/index.php/AAAI/article/view/17796) ## Gene Ontology Consortium - Organization type: Pharma and healthcare - Sector: Life sciences - Adoption wave: 1998-2000s - Semantic role: Biomedical concepts, evidence, and discovery graph - Why it matters: The early proof that scientific knowledge needs shared computable meaning. - Public signal: The Gene Ontology knowledgebase provides a computational structure for gene function, evidence-supported annotations, and GO-CAM models that link annotations into biological pathways. - Operational use: GO supports analysis of large-scale experiments, gene-product function annotation, biological process modeling, and cross-organism comparison. - Strategic implication: GO is the origin story for many modern knowledge graph programs: identifiers, evidence, relations, and curation before analytics. - Sources: - [Gene Ontology Resource](http://geneontology.org/) - [Gene Ontology knowledgebase in 2023](https://par.nsf.gov/servlets/purl/10496319) ## Open PHACTS - Organization type: Pharma and healthcare - Sector: Life sciences / pharma - Adoption wave: 2011-2014 - Semantic role: Biomedical concepts, evidence, and discovery graph - Why it matters: A landmark public-private semantic web initiative for drug discovery. - Public signal: Open PHACTS used semantic web technology to integrate compounds, targets, diseases, tissues, and pathways for complex drug discovery questions. - Operational use: The platform linked public drug-discovery databases into a semantic infrastructure with APIs and query mechanisms for researchers. - Strategic implication: Open PHACTS shows why domain specialists need answerable cross-domain questions, not isolated datasets. - Sources: - [IHI Open PHACTS factsheet](https://www.ihi.europa.eu/projects-results/project-factsheets/open-phacts) - [Open PHACTS triple store paper](https://pmc.ncbi.nlm.nih.gov/articles/PMC4270790/) ## AstraZeneca - Organization type: Pharma and healthcare - Sector: Life sciences / pharma - Adoption wave: 2021+ - Semantic role: Biomedical concepts, evidence, and discovery graph - Why it matters: A strong public example of an internal pharma knowledge graph for drug development. - Public signal: AstraZeneca published BIKG, an internal Biological Insights Knowledge Graph combining public, licensed, proprietary, and literature-extracted data for machine learning. - Operational use: BIKG integrates entities such as genes, proteins, diseases, and compounds, with NLP pipelines and ontology alignment for analytics and machine learning. - Strategic implication: AstraZeneca shows that competitive advantage comes from combining public knowledge, internal evidence, literature extraction, and governed graph structure. - Sources: - [BIKG paper](https://www.biorxiv.org/content/10.1101/2021.10.28.466262v1) - [Kazu documentation](https://astrazeneca.github.io/KAZU/introduction.html) ## Roche - Organization type: Pharma and healthcare - Sector: Life sciences / pharma - Adoption wave: 2021+ public evidence - Semantic role: Biomedical concepts, evidence, and discovery graph - Why it matters: A mature pharma example of FAIR data, semantic hubs, and ontology-backed interoperability. - Public signal: Public sources describe Roche using RDF, RDFS, OWL, community vocabularies, BFO-aligned reference models, and semantically harmonized data for FAIR knowledge graphs. - Operational use: Roche's FAIR data approach uses metadata, terminologies, domain ontologies, and application models to connect productive applications and support interoperable knowledge graphs. - Strategic implication: Roche shows that ontology is not a research toy; it is data-governance plumbing for large pharmaceutical operations. - Sources: - [Roche FAIR data by design](https://fairtoolkit.pistoiaalliance.org/use-cases/fair-data-by-design/) - [Roche FAIR in vivo platform](https://ceur-ws.org/Vol-3235/paper25.pdf) ## Novartis - Organization type: Pharma and healthcare - Sector: Life sciences / pharma - Adoption wave: 2018+ public evidence - Semantic role: Biomedical concepts, evidence, and discovery graph - Why it matters: A visible drug-discovery knowledge graph case using biomedical entities and literature evidence. - Public signal: Novartis public case studies describe a knowledge graph linking genes, diseases, compounds, text-mined literature, historical data, and image-derived data. - Operational use: Researchers use the graph to navigate gene-disease-compound relationships, evaluate association strength, and identify promising compounds or disease hypotheses. - Strategic implication: Novartis is a clean analogy for industrial operations: experts need to traverse evidence relationships, not search isolated repositories. - Sources: - [Neo4j Novartis case study](https://neo4j.com/customer-stories/novartis/) - [Novartis OntoBrowser](https://dev.arctic.novartis.com/research-and-development/open-source-science) ## Pfizer - Organization type: Pharma and healthcare - Sector: Life sciences / pharma - Adoption wave: 2019+ public evidence - Semantic role: Biomedical concepts, evidence, and discovery graph - Why it matters: A visible pharma example of semantic integration, ontologies, and knowledge graph AI. - Public signal: Public sources describe Pfizer using data standards, vocabularies, ontologies, linked data, and more recently a continuously updated biomedical knowledge graph with Data4Cure. - Operational use: Pfizer's semantic approaches connect public and internal datasets, literature evidence, identifiers, compound data, and disease areas for drug discovery and development. - Strategic implication: Pfizer reinforces the core point: the higher the cost of scientific ambiguity, the more valuable governed semantic integration becomes. - Sources: - [Pfizer Intelligent Data Framework](https://www.bio-itworld.com/news/2019/04/25/pfizers-model-for-the-intelligent-data-framework) - [Pfizer and Data4Cure knowledge graphs](https://www.drugdiscoveryonline.com/doc/data-cure-partners-with-pfizer-to-deliver-new-insights-with-ai-and-data-driven-knowledge-graphs-0001) ## DARPA - Organization type: Historical defense catalyst - Sector: Defense research - Adoption wave: 1999-2006 - Semantic role: Mission semantics, interoperability, and decision models - Why it matters: The historical bridge between early semantic web research and defense needs. - Public signal: DAML aimed to make web information machine-readable using semantic annotations and ontologies, with transition paths to military command-and-control and intelligence activities. - Operational use: DAML and DAML+OIL influenced OWL and the semantic web standards that later became foundational to many ontology and knowledge graph systems. - Strategic implication: DARPA explains why military and intelligence communities saw semantic interoperability early: autonomous systems need explicit meaning. - Sources: - [DAML.org](https://www.daml.org/) - [DAML BAA 00-07](https://xml.coverpages.org/daml-pipBAA0007.html) ## Boeing - Organization type: Aerospace and engineering systems - Sector: Aerospace - Adoption wave: 2000s-2020s - Semantic role: Lifecycle engineering and operational asset semantics - Why it matters: A public aerospace example where semantics support model-based systems engineering and lifecycle data. - Public signal: Public sources describe Boeing using semantic capabilities and ontologies for MBSE, aircraft data hierarchy, impact analysis, and lifecycle data consistency. - Operational use: Boeing work connects aircraft parts, requirements, engineering data, system models, and analysis workflows across complex vehicle design and safety contexts. - Strategic implication: Aerospace is the closest analogy to energy assets: safety, versions, parts, maintenance, evidence, and lifecycle decisions must share one model of reality. - Sources: - [MarkLogic MBSE at Boeing](https://www.progress.com/blogs/how-marklogic-enabled-mbse-at-boeing) - [Boeing Aircraft Data Hierarchy](https://github.com/Boeing/aircraft-data-hierarchy) ## Airbus Skywise - Organization type: Aerospace and operational platform - Sector: Aerospace - Adoption wave: 2017+ - Semantic role: Lifecycle engineering and operational asset semantics - Why it matters: The clearest public aviation platform example of ontology-backed operational data integration. - Public signal: Airbus launched Skywise with Palantir to integrate aviation data; public documentation exposes Skywise Ontology APIs for aircraft, parts, events, maintenance, and work packs. - Operational use: Skywise supports predictive maintenance, fleet operations, disruption reduction, maintenance cost reduction, and operational analytics over aviation data. - Strategic implication: Airbus shows how ontology becomes operational when it is embedded into maintenance, fleet, and equipment workflows, not parked as a modeling exercise. - Sources: - [Airbus Skywise launch](https://www.airbus.com/en/newsroom/press-releases/2017-06-airbus-launches-skywise-aviations-open-data-platform) - [Skywise developer documentation](https://developer.services.skywise.com/) ## The Open Group OSDU - Organization type: Energy data standardization - Sector: Energy - Adoption wave: 2018-2021+ - Semantic role: Industrial asset, platform, and operations semantics - Why it matters: The energy sector's clearest open data-standardization move. - Public signal: OSDU is an open-source, standards-based, technology-agnostic data platform for energy. Public OSDU ontology work converts OSDU schemas into OWL/RDF. - Operational use: OSDU provides common APIs, data schemas, and platform foundations for subsurface and energy data applications across operators and vendors. - Strategic implication: OSDU is necessary but not sufficient. TSF should sit above platform schemas to govern concepts, relationships, and decision meaning across energy domains. - Sources: - [OSDU Forum](https://osduforum.org/) - [OSDU Ontology](https://accenture.github.io/OSDU-Ontology/) ## Equinor - Organization type: Energy operator evidence - Sector: Energy - Adoption wave: 2018+ - Semantic role: Industrial asset, platform, and operations semantics - Why it matters: A public operator example of ontology-based access and contextualized industrial data. - Public signal: Public research describes ontology-based data access at Equinor for exploration data. Equinor also publishes Omnia Plant principles for contextualized industrial data through open APIs. - Operational use: The work targets exploration geologists, plant data, timeseries metadata, contextualized APIs, and industrial applications over operational data. - Strategic implication: Energy operators already know the bottleneck: finding trusted data is often harder than analyzing it. Ontology turns discovery into governed access. - Sources: - [Finding Data Should be Easier than Finding Oil](https://www.sintef.no/en/publications/publication/0198cc53e1b2-ab91570e-8b40-4c10-81a1-884cfbaeb2b0/) - [Equinor OmniaPlant](https://github.com/equinor/OmniaPlant) ## Cognite - Organization type: Energy industrial platform - Sector: Energy / industrial - Adoption wave: 2020s+ - Semantic role: Industrial asset, platform, and operations semantics - Why it matters: A market-facing industrial knowledge graph platform used in energy and process industries. - Public signal: Cognite markets an industrial knowledge graph in CDF and publishes core/process industry data models for assets, equipment, timeseries, maintenance orders, and notifications. - Operational use: CDF contextualizes asset, time-series, document, 3D, maintenance, and process data using standardized core and industry data models. - Strategic implication: Cognite validates the industrial knowledge graph category, but TotalEnergies still needs TSF to control formal meaning across Cognite and non-Cognite systems. - Sources: - [Cognite industrial knowledge graph](https://www.cognite.com/en/industrial-knowledge-graph) - [Cognite process industries data model](https://docs.cognite.com/cdf/dm/dm_reference/dm_process_industry_data_model)