ETSI GR CIM 051 V1.1.1 (2025-02)

Context Information Management (CIM); Using NGSI-LD in the context of Building Information Management (BIM)

DGR/CIM-0051

General Information

• Status: Not Published
• Technical Committee: CIM - cross-cutting Context Information Management
• Current Stage: 12 - Citation in the OJ (auto-insert)
• Due Date: 13-Feb-2025
• Completion Date: 11-Feb-2025

Standards Content (Sample)

GROUP REPORT
Context Information Management (CIM);
Using NGSI-LD in the context of
Building Information Management (BIM)
Disclaimer
The present document has been produced and approved by the cross-cutting Context Information Management (CIM) ETSI
Industry Specification Group (ISG) and represents the views of those members who participated in this ISG.
It does not necessarily represent the views of the entire ETSI membership.

2 ETSI GR CIM 051 V1.1.1 (2025-02)

Reference
DGR/CIM-0051
Keywords
API, IoT, NGSI-LD
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3 ETSI GR CIM 051 V1.1.1 (2025-02)
Contents
Intellectual Property Rights . 7
Foreword . 7
Modal verbs terminology . 7
1 Scope . 8
2 References . 8
2.1 Normative references . 8
2.2 Informative references . 8
3 Definition of terms, symbols and abbreviations . 10
3.1 Terms . 10
3.2 Symbols . 10
3.3 Abbreviations . 11
4 Background information. 12
5 FAIR requirements in BIM context . 14
5.1 Introduction . 14
5.2 Findable: Locating and Identifying instances . 14
5.3 Accessible: Publishing Models and Data . 15
5.4 Interoperable . 15
5.5 Reusable . 15
6 Open standards in BIM domain . 15
6.1 Introduction . 15
6.2 BOT - Building Topology Ontology . 16
6.3 Digital Construction Ontology (DiCon) . 16
6.4 TNO Interconnect Ontology series . 16
6.5 SSN / SOSA - Semantic Sensor Network . 16
6.6 SAREF4BLDG - Smart Applications for Building . 16
6.7 Smart Model Building . 16
6.8 CityGML . 17
6.9 IFC - Industry Foundation Classes . 17
6.10 OGC GeoSPARQL. 17
6.11 GLTF - GL Transmission Format . 17
6.12 The FOG ontology - File Ontology for Geometry formats . 17
7 Use cases for Buildings . 18
7.1 Introduction . 18
7.2 Air Quality. 19
7.2.1 Scenarios and assumptions . 19
7.2.2 Stakeholders . 19
7.2.3 Data types and data sources . 21
7.3 Energy efficiency . 21
7.3.1 Foreword . 21
7.3.2 Stakeholders . 23
7.3.3 Data sources . 23
7.3.4 Conceptual Model . 24
7.4 Water management . 24
7.4.1 Foreword . 24
7.4.2 Scenario description . 25
7.4.3 Stakeholders . 26
7.4.4 Data sources . 27
7.4.5 Conceptual model . 28
8 Building Model Requirements . 30
8.1 Introduction . 30
8.2 Identification . 31
8.3 Spatial data . 31
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8.3.1 Location . 31
8.3.2 Containment . 32
8.3.3 Graph-based location . 35
8.3.4 Geometry . 35
8.4 Time series . 38
8.5 Additional data . 39
8.6 Element classification . 40
8.7 Overall model . 41
9 BIM / NGSI-LD Integration strategies . 44
9.1 Introduction . 44
9.2 OPTION A - BIM as a Link: Linking the data . 44
9.3 OPTION B - Mapping main BIM concepts through an NGSI-LD domain model . 46
9.4 OPTION C - BIM Smart Model: Creating an NGSI-LD domain model equivalent to IFC concepts . 48
10 Recommendations . 49
10.1 Integrating BIM with NGSI-LD: Three options. . 49
10.2 Identifying BIM elements . 49
10.3 Converting data to NGSI-LD . 49
10.4 Synchronizing data . 49
10.5 Rendering geometry . 50
11 Conclusion . 52
Annex A: Selection of relevant IFC (OWL) relations and properties . 53
Annex B: BIM / NGSI-LD alignment . 57
Annex C: Schemas . 58
Annex D: BIM Standards . 59
History . 66

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List of figures
Figure 1: NGSI-LD Information Model .12
Figure 2: Key components of the NGSI-LD ontology .13
Figure 3: Conceptual model of air quality .20
Figure 4: Conceptual model for Energy Monitoring and Management .24
Figure 5: SAREF4WATR asset model .29
Figure 6: Water-related terms of SAREF4WATR .29
Figure 7: SAREF4CITY ontology with City Objects .33
Figure 8: Overview of SAREF4BLDG ontology .33
Figure 9: BOT overview [i.28] .34
Figure 10: Objects involved in Interfaces [i.28] .34
Figure 11: Mapping between IFC, BOT and SAREF .35
Figure 12: Progression of design across the LODs with an example of Elevator .36
Figure 13: Relations between non-geometric objects and their geometry descriptions .37
Figure 14: ASTM Uniformat Classification for Building Elements .41
Figure 15: Multi-scale model interoperability based on BOT and SAREF .42
Figure 16: Web query of the referenced URI .45
Figure 17: SPARQL query of the referenced URI .45
Figure 18: Railway scenario converted to NGSI-LD data model .47
Figure 19: Link between a building element and its 3D representation .51
Figure 20: Adapting geometry accuracy to scale .51
Figure C.1: Kinds of City Objects in CityJSON .58

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List of tables
Table 1: Example of IFC, NGSI-LD mapping for water .30
Table D.1: BOT description .59
Table D.2: DiCon description .60
Table D.3: Interconnect description .61
Table D.4: SSN/SOSA description.62
Table D.5: SAREF4BLDG.62
Table D.6: Smart data model description .63
Table D.7: CityGML description .63
Table D.8: IFC description .64
Table D.9: geoSPARQL description .64
Table D.10: GITF description .65
Table D.11: FOG description .65

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Foreword
This Group Report (GR) has been produced by ETSI Industry Specification Group (ISG) cross-cutting Context
Information Management (CIM).
Modal verbs terminology
In the present document "should", "should not", "may", "need not", "will", "will not", "can" and "cannot" are to be
interpreted as described in clause 3.2 of the ETSI Drafting Rules (Verbal forms for the expression of provisions).
"must" and "must not" are NOT allowed in ETSI deliverables except when used in direct citation.

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1 Scope
The present document encompasses BIM from the city scale down to building components but is mainly focused on the
buildings scale and related data. This approach remains inside the domain of application of BIM: Building close
environment, Building envelop and indoor components. Infrastructures are not considered here, as they are not covered
by actual BIM standards and overlap with geospatial standards at upper scales. The approach is illustrated through a few
use cases that cover the main multi-scale aspects of BIM. It also gives some guidelines and recommendations to use
NGSI-LD in Buildings context.
2 References
2.1 Normative references
Normative references are not applicable in the present document.
2.2 Informative references
References are either specific (identified by date of publication and/or edition number or version number) or
non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the
referenced document (including any amendments) applies.
NOTE: While any hyperlinks included in this clause were valid at the time of publication, ETSI cannot guarantee
their long term validity.
The following referenced documents are not necessary for the application of the present document but they assist the
user with regard to a particular subject area.
[i.1] ETSI White Paper No. 42: "Guidelines for Modelling with NGSI-LD", March 2021.
[i.2] Marco Pritoni, Drew Paine: "Metadata Schemas and Ontologies for Building Energy Applications:
A Critical Review and Use Case Analysis".
[i.3] Martin Bauer, Sonia Bilbao et al.: "Semantic IoT Solutions - A Developer Perspective".
[i.4] ETSI GS CIM 006 (V1.2.1): "Context Information Management (CIM); NGSI-LD Information
Model".
[i.5] Jimmy Abualdenien, M.Sc. et al.: "Levels of Detail, Development, Definition, and Information
Need: A Critical Literature Review", 2022.
[i.6] Chi Zhang, Jakob Beetz: "BimSPARQL: Domain-specific functional SPARQL extensions for
querying RDF building data".
[i.7] Pieter Pauwels, Davy Van Deursen: "IFC-to-RDF: Adaptation, Aggregation and Enrichment".
[i.8] Andrew Malcolm, Jeroen Werbrouck, Pieter Pauwels: "LBD server: Visualising Building Graphs
in web-based environments using semantic graphs and glTF-models". ®
[i.9] W3C Recommendation: "SPARQL 1.1 Protocol", 21 March 2013.
[i.10] Iker Esnaola-Gonzalez, Jesús Bermúdez, Izaskun Fernandez, and Aitor Arnaiz: "Ontologies for
Observations and Actuations in Buildings: A Survey".
[i.11] Diego Vinasco-Alvarez, John Samuel Samuel, Sylvie Servigne, Gilles Gesquière: "From
CityGML to OWL". [Technical Report] LIRIS UMR 5205. 2020. hal-02948955.
[i.12] Alper Tunga Akın, Ç. Cömert: "'CITYJSON2RDF' A converter for producing 3D city knowledge
graphs".
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[i.13] Anna Wagner, Pieter Pauwels et al.:"Representing construction-related geometry in a semantic
web context: A review of approaches".
[i.14] Robert Amor and Johannes Dimyadi: "An open repository of IFC data models and analyses to
support interoperability deployment", pp. 16-18, 2010.
[i.15] Andre Borrmann, Jakob Beetz, Christian Koch, T. Liebich and Sergej Muhic: "Industry
Foundation Classes: A Standardized Data Model for the Vendor-Neutral Exchange of Digital
Building Models", pp. 81-126, 2018.
[i.16] Georgios Bouloukakis, Chrysostomos Zeginis, Nikolaos Papadakis, Panagiotis Zervakis, Dimitris
Plexousakis, and Kostas Magoutis: "Enabling IoT enhanced Transportation Systems using the
th
NGSI Protocol", In Proceedings of the 12 ACM International Conference on the Internet of
Things (Delft, Netherlands), 2022.
[i.17] Manu Sporny, Gregg Kellogg, and Markus Lanthaler. 2014. JSON-LD 1.0 - A JSON-based ®
Serialization for Linked Data. W3C Recommendation (01/2014).
[i.18] Roberto Yus, Georgios Bouloukakis, Sharad Mehrotra and Nalini Venkatasubramanian:
"Abstracting Interactions with IoT Devices Towards a Semantic Vision of Smart Spaces". In
th
Proceedings of the 6 ACM International Conference on Systems for Energy-Efficient Buildings,
Cities, and Transportation (New York, NY, USA) (BuildSys '19). Association for Computing
Machinery, New York, NY, USA, pp. 91-100, 2019.
[i.19] Roberto Yus, Georgios Bouloukakis, Sharad Mehrotra and Nalini Venkatasubramanian: "The
SemIoTic Ecosystem: A Semantic Bridge between IoT Devices and Smart Spaces", ACM
Transactions on Internet Technology - TOIT (2022).
[i.20] FAIR in Practice Task Force of the European Open Science Cloud FAIR Working Group: "Six
Recommendations for Implementation of FAIR Practice".
[i.21] buildingSMART Australia: "User Guide for Geo-referencing in IFC 'How to Setup
Geo-referencing in a Building or Linear Infrastructure Model'", January 2020.
[i.22] ETSI: "SAREF extension for building", 2021.
[i.23] buildingSMART: "Industry Foundation Classes", 2013.
[i.24] OGC: "GeoSPARQL", 2012.
[i.25] Pieter Pauwels: "Building Element Ontology (BEO)", 2018.
[i.26] Maxime Lefrançois: "Props", 2019.
[i.27] Digital Construction Ontology Group: "Digital Construction Ontologies", 2020. ®
[i.28] W3C Linked Building Data Community Group: "Building Topology Ontology (BOT)", 2019.
[i.29] Khronos Group: "glTF™ 2.0 Specification", 2017.
[i.30] FOG Ontology Group: "FOG: File Ontology for Geometry formats", 2020.
[i.31] FIWARE: "Revolutionary AI Air Pollution System powered by FIWARE", November 25, 2021.
[i.32] Inspection générale des affaires sociales (IGAS): "L'Observatoire de la qualité de l'air intérieur :
bilan et perspectives".
[i.33] Atmo Nouvelle-Aquitaine: "Qualité de L"air en Nouvelle Aquitaine", 2023.
[i.34] Chi Zhang: "Requirement checking in the building industry: enabling modularized and extensible
requirement checking systems based on semantic web technologies", March 11, 2019.
[i.35] Chi Zhang: "IFC-to-WKT_Converter", 2021.
[i.36] Matthias Wagner, Pieter Pauwels: "Representing construction-related geometry in a semantic web
context", 2019.
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[i.37] Pieter Pauwels, Anna Wagner, Maxime Kriss McGlinn: "Interlinking geospatial and building
geometry with existing and developing standards on the web", 2019.
[i.38] Nikolaos Papadakis, Georgios Bouloukakis, Kostas Magoutis: "Enabling dynamic smart spaces
using IoT-enhanced NGSI-LD data models", November 7, 2022.
[i.39] Interoperable Europe: "Rolling Plan for ICT standardisation: Water Management Digitalisation
(RP2024)", 2024.
[i.40] Geonovum: "Use cases for 3D GeoSPARQL", Geonovum General Working Version,
September 25, 2024.
[i.41] Fiware4Water: "Demo case #1: Water supply system real time operational management (Greece)".
[i.42] Cinzia Slongo, Giada Malacarne, Dominik T. Matt: "The IFC File Format as a Means of
Integrating BIM and GIS: The Case of the Management and Maintenance of Underground
Networks", 2022.
[i.43] C. Ellul, J. Stoter, L. Harrie: "Investigating the state of play of GEOBIM across Europe", 2018.
[i.44] BuildingSmart, IFC 4.3.2.20241204 (IFC4X3_ADD2): "IfcBuildingControlsDomain".
[i.45] BuildingSmart, IFC 4.3.2.20241204 (IFC4X3_ADD2): "IfcHvacDomain".
[i.46] BuildingSmart, IFC 4.3.2.20241204 (IFC4X3_ADD2): "IfcPlumbingFireProtectionDomain".
[i.47] ETSI TR 103 547 (V1.1.1): "SmartM2M; SAREF extension investigation; Requirements for the
Water domain".
[i.48] ETSI: "SAREF4WATR ontology".
[i.49] FIWARE: "Smart data models repository".
[i.50] ISO 16739-1:2024: "Industry Foundation Classes (IFC) for data sharing in the construction and
facility management industries- Part 1: Data schema".
NOTE: ISO 16739-1:2024 supersedes ISO/PAS 16739:2005 and ISO 16739-1:2018.
[i.51] IETF RFC 7946: "The GeoJSON Format".
[i.52] CEN/TC 442: "Building Information Modelling (BIM)".
[i.53] ISO/TC 59/SC 13: "Organisation et numérisation des informations relatives aux bâtiments et
ouvrages de génie civil, y compris modélisation des informations de la construction (BIM)".
[i.54] ISO/IEC 12113:2022: "Information technology — Runtime 3D asset delivery format — Khronos
glTF™ 2.0".
3 Definition of terms, symbols and abbreviations
3.1 Terms
Void.
3.2 Symbols
Void.
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3.3 Abbreviations
For the purposes of the present document, the following abbreviations apply:
AECO Architecture, Engineering, Construction, and Operations
API Application Programming Interface
BBS Building Block System
BIM Building Information Modelling
BMS Building Management System
BOT Building Operation and Technology
CIM Context Information Management
CRS Coordinate Reference System
ECM Energy Concervation Measures
EPA Environmental Protection Agency
FAIR Findable, Accessible, Interoperable, and Reusable
FOG File Ontology for Geometry formats
GLTF GL Transmission Format (a file format for 3D models and scenes)
GML Geography Markup Language
GUID Global Unique Identifier
HVAC Heating, Ventilation, and Air Conditioning
ICT Information Technology
IFC Industry Foundation Classes
IoT Internet of Things
JSON JavaScript Object Notation
JSON-LD JavaScript Object Notation for Linked Data
LBD Linked Building Data
LD Linked Data
LOD Level Of Details
NGSI Next Generation Service Interfaces
NGSI-LD Next Generation Service Interface - Linked Data
NIBS National Institute of Building Sciences
OGC Open Geospatial Consortium
OMG Object Management Group
OWL Web Ontology Language
RDF Resource Description Framework
RDFS Resource Description Framework Schema
SAREF Smart Applications REFerence ontology
SOSA Sensor, Observation, Sample, and Actuator (ontology)
SPARQL SPARQL Protocol and RDF Query Language
SRS Spatial Reference System
SSN Semantic Sensor Network
SVG Scalable Vector Graphics
TIN Triangulated Irregular Network
UID Universal Identifier
URI Uniform Resource Identifier
UUID Universal Unique Identifier ®
W3C World Wide Web Consortium
WNS Web Notification Standard
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4 Background information
Next Generation Service Interface - Linked Data (NGSI-LD) is an open standard for context information management,
which defines a common data model and interface for exchanging context information between different systems and
applications [i.1]. It is an extension of the NGSI standard, which was developed by the FIWARE Foundation, and is
designed to support the integration of Internet of Things (IoT) devices and data sources. NGSI-LD uses Linked Data
principles to represent context information as a graph of interconnected entities, where each entity has a unique
Uniform Resource Identifier (URI) and can be described using a set of attributes and relationships. This allows for
greater flexibility and interoperability in the exchange of context information, as it enables data to be shared and reused
across different domains and applications. NGSI-LD is used in a variety of domains, including smart cities,
industry 4.0, and transportation. It provides a common framework for managing context information in these domains,
enabling the development of new services and applications that can leverage data from multiple sources. By using
NGSI-LD, organizations can improve data sharing and collaboration, reduce integration costs, and accelerate the
development of innovative solutions.
The NGSI-LD information model (Figure 1) is derived from PGs. Entities, relationships, and properties are the key
components of the NGSI-LD information model, as shown in Figure 2. A real-world item, such as a building or a
person, is represented by an entity. A relationship connects two or more entities, such as a person who works in a
building. A property connects values to elements, such that it can identify that an entity corresponds to a real person.
Due to its extensive data structure, it may be utilized for practically any data exchange situation throughout the life
cycle of a building.
Figure 1: NGSI-LD Information Model
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Figure 2: Key components of the NGSI-LD ontology
BIM stands for Building Information Modelling. It is a digital representation of the physical and functional
characteristics of a building, including both geometric and non-geometric information. BIM is a process that involves
creating and managing digital models of a project, which can be used for planning, design, construction, and
management of buildings and infrastructure. The BIM process allows architects, engineers, and construction
professionals to collaborate more effectively, as it provides a shared view of the project and enables better
communication and coordination. It can also help to improve the accuracy of cost estimates and construction schedules,
reduce waste, and enhance the overall quality and sustainability of buildings. BIM is typically used throughout the
entire lifecycle of a building, from the initial design phase through to construction, operation, and maintenance. By
using BIM, stakeholders can make more informed decisions, improve the efficiency of their workflows, and ultimately
deliver better outcomes for their clients and end-users.
Data spaces are a concept used in data management and integration to describe a virtual environment where data from
different sources can be stored, managed, and shared in a secure and controlled manner. Data spaces are typically
designed to support specific use cases or domains, such as healthcare, finance, or manufacturing. Data spaces provide a
common framework for data integration and sharing, enabling organizations to break down data silos and leverage data
from multiple sources to gain new insights and create new value. Data spaces typically include a set of tools and
services for data discovery, access control, data quality management, and data governance. Data spaces can be
implemented using a variety of technologies and architectures, such as data lakes, data warehouses, or federated data
platforms. They can also be implemented using decentralized architectures, such as blockchain or peer-to-peer
networks, to enable secure and distributed data sharing. Data spaces are becoming increasingly important in the era of
big data and digital transformation, as organizations seek to leverage data to gain a competitive advantage and create
new business models. By providing a secure and controlled environment for data sharing and collaboration, data spaces
can help organizations to unlock the value of their data, while ensuring compliance with privacy and security
regulations. By using NGSI-LD, data spaces can provide a common data model and interface for representing and
sharing data, enabling greater interoperability and integration between different data sources and applications. For
example, in a smart city context, different data spaces may be created for different domains, such as transportation,
energy, and building management. NGSI-LD can be used as a common data model and API specification for
exchanging data between these different data spaces, enabling greater integration and collaboration between different
domains and stakeholders.
Domain models are models that represent the concepts, entities, and relationships within a specific domain or area of
interest. A domain model is typically developed by domain experts who have a deep understanding of the business or
technical requirements of the domain. The purpose of a domain model is to provide a common language and a shared
understanding of the domain among stakeholders, such as business analysts, developers, and users. A domain model
typically includes entities, attributes, relationships, and constraints that are relevant to the domain.
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A cross-domain model refers to a common data model that can be used to represent and exchange context information
across different domains or applications. A cross-domain model provides a standardized way to describe entities,
attributes, and relationships in a way that is interoperable and reusable across different domains. NGSI-LD is designed
to support cross-domain data integration and interoperability, and the development of cross-domain models is an
important part of this effort. Cross-domain models can be created by mapping and aligning domain-specific data models
to a common data model, using Linked Data principles and vocabularies such as RDF, RDFS, and OWL. For example,
in a smart city context, a cross-domain model may be developed to represent and exchange data related to buildings,
transportation, energy, and environmental conditions. This cross-domain model would provide a common data model
and vocabulary for representing entities such as buildings, vehicles, sensors, and weather conditions, as well as their
attributes and relationships. By using a cross-domain model, stakeholders can improve data integration and
interoperability, enabling greater collaboration and innovation across different domains and applications. Cross-domain
models can also help to reduce data silos and enable the development of new services and applications that leverage
data from multiple domains. Developing cross-domain models requires collaboration and consensus-building between
different stakeholders, including domain experts, data modelers, and software developers. It also requires the use of ®
and other standards organizations.
common vocabularies and standards, such as those provided by the W3C
An ontology is a formal representation of a set of concepts and their relationships within a specific domain of
knowledge. An ontology provides a shared vocabulary and a set of rules for describing and reasoning about the
concepts and relationships within that domain. Ontologies are used to enable interoperability and knowledge sharing
between different systems, applications, and stakeholders. By providing a common understanding of the concepts and
relationships within a domain, ontologies can help to overcome semantic heterogeneity, which is the difference in
meaning and interpretation of data between different systems and stakeholders. An ontology typically includes a set of
classes or concepts, which represent the entities or objects within a domain, and a set of properties or attributes, which
describe the characteristics of those entities. Ontologies can also include relationships between classes, such as
hierarchical or associative relationships, and constraints or rules that govern the use of the ontology. Ontologies are
used in a variety of domains, including healthcare, finance, biology, and engineering. They are often used in the
development of semantic web applications, which aim to provide a more meaningful and contextual representation of
data on the web. Ontologies can also be used in artificial intelligence and machine learning applications, to provide a
structured representation of knowledge and enable more sophisticated reasoning and decision-making.
5 FAIR requirements in BIM context
5.1 Introduction
This clause lists Findable, Accessible, Interoperable, Reusable (FAIR) [i.20] requirements specific to BIM context, and ®
interoperability
how they are currently addressed by architecture and construction communities [i.14] and [i.15]. W3C
recommendations and OASC Minimal Interoperability Mechanisms are also listed and taken into account.
5.2 Findable: Locating and Identifying instances
The first step in using data is to be able to find them. Metadata and data should be easy to find for both humans and
computers. Machine-readable metadata are essential for automatic discovery of datasets and services.
F1. (Meta)data are assigned a globally unique and persistent identifier
F2. Data are described with rich metadata
F3. Metadata clearly and explicitly include the identifier of the data they describe
F4. (Meta)data are registered or indexed in a searchable resource
Unique identifiers, by means of namespaces and registration identifiers, provided by National Registration authorities
(e.g. Nation Building Referential in France) or Building Owners, are to be used.
Identifying properties and geospatial references (as, for instance, described in the user guide for geo-referencing in IFC)
is extremely important.
Finding a glue between differe
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