Automated Ontology Matching in the Architecture, Engineering and Construction Domain - A Case Study

Abstract

The ontology-based modelling of the built environment is deemed promising to successfully integrate disparate knowledge silos and has gained signi cant attraction in industry and academia. This interest has led to a proliferating number of ontologies and the manual de nition of schema level alignments among them is a tedious task. Hence, this paper explores the possibilities of automated ontology matching methods in this regard. This work compares manually created and automatically generated alignments of six domain ontologies to the building topology ontology. The initial ndings of this case study indicate that current state of the art ontology matching tools are in principle capable of detecting automatically correct alignments and that their is a strong need to de ne domain speci c benchmarks.

Keywords

• Automated Ontology Matching Architecture Engineering Construction Facility Management Heterogeneity Building Topology Ontology

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Ontology-based modelling and associated implementations based on Semantic Web Technologies (SWT) [ 10 ] have gained attention by academia and industry in the Architecture, Engineering, Construction and Facility Management (AEC/FM) domain [ 23 ]. A main motivation to use the technology is its ability to successfully address the problem of integrating heterogeneous information silos distributed across the AEC/FM domain [ 2 ].

The spread of the technology has lead to a proliferating development of domain ontologies (cf. reviews in [ 7,23,25 ]). This development poses the risk of putting the bene ts of the technology at stake as the de ned ontologies overlap and found ontological design patterns are reimplemented again and again making a reuse di cult. 'Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it raises heterogeneity problems to a higher level.' [ 12 ]. The principle of ontology reuse stipulated in well-known ontology engineering methodologies [ 19,14 ] is frequently not followed, when designing domain ontologies in the building domain. This makes it cumbersome for potential developers to implement applications as again a heterogeneous landscape of domain models appears.

The Building Topology Ontology (BOT), initially de ned in [ 25,26 ] and further developed by the members of the W3C Linked Building Data Community Group (W3C LBD CG) [ 32 ], has been proposed to de ne commonly reoccurring design patterns in domain ontologies of the AEC/FM domain. These design patterns then can be reused by developers in their respective domains through extending from BOT [ 29 ]. Following this approach, intrinsically a domain wide interoperability can be ensured.

The successful manual alignment of ve domain ontologies (SAREF4Building [ 24 ], BRICK [ 3,4 ], DogOnt [ 6 ], ThinkHome [ 27 ], ifcOWL [ 22 ]) to BOT is presented in an initial e ort in Schneider [ 29 ]. The schema level alignment of ontologies is a tedious task, which potentially is as challenging as ontology engineering itself [ 12 ]. There exists a strong need to use automated ontology matching methods to automatically nd alignments [ 7 ]. Also ontologies tend to evolve over time and alignments need to be updated and checked accordingly. The analysis of the current state of the art presented in section 2 indicates that the use of automated ontology matching methods has not been studied in depth in the AEC/FM domain so far.

The contributions of this paper are two fold. First, manual alignments originally de ned in an earlier contribution [ 29 ] are revised and updated to re ect the latest version of BOT (v0.3.0). Second, a study is conducted, where an automated ontology matching method [ 13 ] is utilised to align the respective domain ontologies to BOT. The generated alignments are compared to the manually found ones.

The remainder of this paper includes a review of existing work (Section 2) related to the automated matching of ontologies in the AEC/FM domain. Then in Section 3 a methodology is presented to compare manually de ned and automated generated alignments of domain ontologies. Finally, in Section 4 revised manual alignments are presented and the results of the comparison to automatically generated alignments are presented in Section 5. 2

Related Work

The eld of ontology matching has been around for a number of years and the fundamentals of the technology are presented in Euzenat & Shvaiko [ 12,30 ]. Speci c matching algorithms are developed actively and their performance is evaluated yearly in benchmark tests under the supervision of the Ontology Alignment Evaluation Initiative (OAEI), where the results of the most recent event are presented in Algergawy et al. [ 1 ].

A, yet limited, number of contributions, which investigate the topic of ontology matching in the context of the AEC/FM domain exist.

In their demand for interoperability in the smart cities domain, Costin & Eastman [ 7 ] conduct a thorough review of existing contributions in this regard. They conclude the ontology-based modelling of the domain based on SWT provide means to address the prevalent heterogeneity. However, as the manual alignment of domain ontologies is a tedious task, the demand for automated ontology matching methods is made.

Otero-Cerdeira et al. [ 20 ] investigate the use of automated ontology matching methods in the context of smart cities. The present OntoPhil a ontology matching technique speci cally designed for the matching of disparate knowledge sources in the context of smart cities. Beside the cited works no further documentation or download possibility of the OntoPhil tool has been found.

Bellini et al. [ 5 ] present a system for the integration of disparate data sources in the context of smart cities. The system is designed to handle large data volumes and integrates them by mapping the data to the Knowledge Model for City (KM4City) ontology. The actual mapping is undertaken manually using the Karma Data Integration tool [ 15 ].

Gyrard et al. [ 16 ] present an approach to enrich ontology catalogues with domain ontologies of smart cities. Their approach aims for interoperability among applications by providing an interface, the catalogue, to developers to easily nd and reuse existing domain ontologies. An automated update is discussed 'but a manual checking is preferred to handle synonyms' [ 16 ].

Espinoza-Arias et al. [ 11 ] review existing ontological representations of smart city data. No explicit mappings are de ned among the ontologies but after a characterisation a number of reoccurring ontology design patterns are de ned.

The presented contributions indicate that automated ontology matching methods seem to be promising to address heterogeneity of formats and formal models. Most work reviewed focuses on ontology matching and alignment in the domain of smart cities. The AEC/FM domain can be seen as a sub-domain of smart cities but it has not been discussed in detail to the best of the authors knowledge. 3

Methodology

A methodology is established to evaluate and compare the manual and automated matching of ontologies in the AEC/FM domain. In the study the de nition of alignments between BOT [ 25,26 ] and six domain ontologies is studied (SAREF4Building [ 24 ], BRICK [ 3,4 ], DogOnt [ 6 ], ThinkHome [ 27 ], ifcOWL [ 22 ], DERI Room [ 8 ]). The namespaces used in the work are reported in Table 1. In particular the following steps are conducted: 1. Manual de nition of alignments on class and object property level; 2. Use of the AgreementMakerLight [ 13 ] tool to automatically generate alignments; 3. Comparison of the obtained manual and automatically created alignments.

The manual de nition of alignments is an extension and revision of the work documented in an earlier contribution [ 29 ]. The step involves the retrieval and local storage of the most recent version of all involved ontologies. The de nition of an alignment ontology which performs a full import (owl:import) of BOT and the respective domain ontology. Finally, alignments are de ned manually through the use of the Protege ontology editor [ 18 ]. The de ned alignments mainly use subsumption for alignment. This has been found bene cial in discussion within the W3C LBD CG as the semantic implied by subsumption are less rigid as compared the de nition of equivalences. Equivalence (e.g. owl:equivalenceClass) implies that all statements on one class also are true for the other, which might not always be the case. An OWL DL reasoner is invoked on the resulting alignment ontology (Pellet [ 31 ]) to ensure the consistency. The de ned ontologies are published in an online repository3.

A large number of tools and associated algorithms exist to perform automated ontology matching [ 12,21,1 ]. An an initial attempt here the AgreementMakerLight tool [ 13 ] is chosen from an extensive list of available tools4. The usage of the AgreementMakerLight tool has been found intuitive and the tool is available open-source in as a compiled java library from a web repository. The tool is used with default settings and BOT is always used as the source ontology. 4.1

SAREF Extension for Building Devices

The SAREF Extension for Building Devices (SAREF4Building) [ 24 ] ontology is an ontology to extend the SAREF ontology [ 9 ] into the buildings domain. A description of the ontology can be found in its documentation and reviews [ 24,23,29 ].

The de ned alignments are reported in Table 2 and the rationale behind is described in the following paragraph.

SAREF4Building describes the concepts of s4bldg:Buildings and s4bldg:Spaces, which can be de ned as specialisations of bot:Building and bot:Space. As the focus of SAREF and its extension to the buildings domain focus on the description of tangible devices, s4bldg:PhysicalObjects, s4bldg:Sensors and s4bldg:Actuators qualify as bot:Elements, which again has been formalised through subsumption.

The, compared to the last iteration of alignments [ 29 ], newly introduced high level relationships bot:containsZone and bot:containsElement re ect on a high level the semantics of s4bldg:hasSpace and s4bldg:contains object properties of SAREF4Building and are aligned through de ning them as subproperties of the respective BOT object properties (see Table 2).

BRICK Uniform Schema for Representing Metadata in

Buildings

The BRICK ontology [ 3,4 ] is an ontology, which focuses on the description of building management systems and their domain concepts such as data points, HVAC equipment and the topology of the building. The de ned and revised alignments are reported in Table 3 and explained in the following.

The alignments are de ned by using subsumption on class and object property level. A brick:Location is considered as a specialisation of bot:Zone as it is used as a super-concept from other concepts describing topological aspects of a building. Hence, the alignment of brick:Building, brick:Floor, brick:Basement, brick:Outside, brick:Room, brick:Space and brick:Wing is straightforward. A brick:Zone is considered to be a specialisation of a bot:Space as BRICK uses this concepts to refer to HVAC zones often used in the context of the control of a building. The brick:Equipment of a building and brick:Points are considered as specialisations of bot:Element. In particular this holds for the concept brick:Point as BRICK describes for instance sensors or meters as tangible objects, which are located in some zone or space.

The semantics of brick:contains can be directly mapped to bot:containsElement and, hence, a specialisation is de ned. Interesting is the brick:hasPart object property de ned in BRICK. The property can be used to relate brick:Equipment to brick:Sensors, brick:Equipment to brick:Equipment or brick:Locations to brick:Locations [ 3 ], hence, it quali es for an extension of bot:containsElement, bot:containsZone and bot:hasSubElement as de ned. Similar semantics apply for the brick:hasPoint object property, which can be specialised from bot:containsZone and bot:containsElement as it allows to relate brick:Equipment to brick:Sensors and brick:Locations to brick:Sensors. 4.3

DogOnt - Ontology Modeling for Intelligent Domotic

Environments

DogOnt ontology is an ontology to formally describe the domain of domotic devices in home appliances. It is initially described in Bonino & Corno [ 6 ] but has since undergone many revision and extensions. The most recent version as of writing (4.0.1) can be obtained from a remote repository5.

A number of concepts of the ontology can be specialised from BOT. The de ned alignments are reported in Table 4. In particular the ontology describes the concepts dogont:Building, dogont:Storey, dogont:Room, which can be mapped to respective BOT concepts. The general concept of dogont:Environment can be seen of a generalisation of the bot:Zone concept as de ned. Interesting is the de nition of the concepts dogont:Ceiling and dogont:Floor as areas bounding a room. This complies to the de nition of bot:Interface and can be aligned by specialisation. To re ect the di erent semantics the di erent sub-concepts of dogont:UnControllable need to be separately specialised (e.g. dogont:Furniture).

In terms of aligning object properties a number of specialisation can be found. The object property dogont:contains is used in DogOnt to describe that some tangible object is fully contained in a dogont:BuildingEnviroment. Essentially this is the semantics of bot:containsElement. The object properties dogont:belongsTo and dogont:hasWallOpening allow to describe composition of classes which specialised from bot:Element. Hence, they are specialised from bot:hasSubElement, potentially its inverse where needed. Interesting are also the dogont:floorOf and dogont:ceilingOf object properties, which qualify as specialisation of bot:interfaceOf together with the specialisation of dogont:Ceiling and dogont:Floor as bot:Interface as de ned above. 4.4

ThinkHome Ontology

The ThinkHome ontologies [ 27 ] are a family of ontologies to describe smart home systems formally and link this with adjacent domains. A detailed description of the ontologies can be found in its documentation and reviews [ 27,22,28 ]. Alignments are de ned to the BuildingOntology of the family of ontologies, which has been derived from the gbXML format6.

The common concepts of th:Campus, th:Building, th:BuildingStorey, th:Opening, th:Space, th:Zone and th:Equipment can be specialised directly from BOT. Interesting are the concepts th:Construction, th:Layer, th:Material, which refer to di erent layers of a wall needed for instance in building performance simulation. The semantics comply to bot:Interface and hence a specialisation is de ned.

A number of object properties are de ned in ThinkHome, which describe the containment of an element in a zone, a zone in a zone or composition of 5 https://github.com/iot-ontologies/dogont, Last accessed: 20 May 2019 6 http://www.gbxml.org/, Last accessed: 20 May 2019

Subject bot:Building bot:Storey bot:Space dog:Environment bot:Zone bot:Space bot:Zone bot:Zone bot:Element bot:Element bot:Element bot:Element bot:Element bot:Interface bot:Interface rdfs:subPropertyOf bot:containsElement rdfs:subPropertyOf owl:inverseOf bot:hasSubElement rdfs:subPropertyOf bot:interfaceOf rdfs:subPropertyOf bot:interfaceOf rdfs:subPropertyOf bot:adjacentElement rdfs:subPropertyOf bot:hasSubElement elements. Hence, the object properties are specialised from the respective object properties in BOT as reported in Table 5.

Industry Foundation Classes 4 Addendum 2

No new or revised alignments to the OWL version of the Industry Foundation Classes (IFC) [ 17,22 ] from BOT are found in this work in comparison to the initial mapping [ 29 ]. However, the alignments are changed to subsumption as mentioned above. 4.6

DERI Room Ontology

The DERI Room ontology [ 8 ] has been added to this study as is represents a light weight vocabulary, compared to the other ontologies, dedicated to the description of buildings. The found alignments are documented in Table 7 and almost all classes and object properties could be specialised from BOT. The results of the manually de ned and revised alignments are summarised in Table 8. The respective domain ontologies are denoted and the considered version of the ontology, if applicable. All de ned mappings are de ned in a separated ontology le and the respective ontology is checked for consistency by invoking a OWL DL reasoner (Pellet [ 31 ]) and looking for inconsistencies. The total number of alignments between concepts and object properties are reported. It should be noted that the total number does not qualify as a metric to determine if BOT can be extended very well to the respective domain. In comparison to the last study [ 29 ] it is interesting that the number of ontologies, where a mapping on the object property level is possible has been signi cantly increased. 5

Automated Alignment

As described in Section 3 a study is conducted in this work using the tool AgreementMakerLight [ 13 ] to automatically derive ontology alignments. Figure 1 shows as an example the result reported by the tool for matching BOT and the ThinkHome ontology.

The tool reports the found alignments in the graphical user interface as well as exports them in RDF format. All automatically found alignments are reported in Table 9. The automated ontology matching tool has found from zero up to three alignments between concepts of the respective ontologies. No alignments between object properties are found. The reported suggested alignment is always equivalence. One false alignment is reported mapping a bot:Space to ifc:IfcSpaceType.

AgreementMakerLight implements three types of primary matching algorithms: a lexical matcher, mediating matcher and word matcher and one secondary type matching algorithm: a parametric string matcher [ 13 ]. An in depth treatment of the matching methods is beyond the scope of this paper. All algorithms take as an input the two to-be-aligned ontologies. The primary matching algorithms compare obtained terms and assert alignments if a similarity measure exceeds a threshold with di erent complexities. Hence, it should be noted that the chosen parameterisation of the thresholds, etc. has an impact on the results and should be studied in more detail on a elaborated benchmark de ned for the AEC/FM domain. In the initial experiments conducted in this study the default suggested values are utilised. Automated ontology matching is a well-known discipline and the topic is researched since decades. A plethora of tools is available, see e.g. [ 1 ]. In this study only one tool has been used. A detailed study of methods and tools should be conducted to further clarify the abilities of automated ontology matching methods for their application in the AEC/FM domain.

The ontologies considered in this study mainly reside from the building automation domain. This is mainly motivated by the authors expertise and research interest. However, other AEC/FM domains should be included in future studies on automated ontology matching.

Conclusion

Within this paper manually de ned and automatically obtained alignments between domain ontologies from the Architecture, Engineering, Construction/ Facility Management (AEC/FM) domain to the Building Topology Ontology (BOT) [ 25 ] are compared. The manual de nition of alignments between ontologies is a tedious task and almost as di cult as developing ontologies from scratch. Initial experiments show that automated matching methods can support nding alignments. The results are promising to also support not only the alignment of domain ontologies but the revision of alignments, e.g. because of schema level updates.

The presented study can only be seen as a starting point and the following open questions for future research remain: { There is a strong need for the de nition of a well-de ned benchmark from AEC/FM domain, potentially including building product data, to establish the attention of ontology matching experts; { Addittional sub-domains of the AEC/FM domain should be added in future studies; { As ontologies evolve over time a future question is, if existing alignments can be reused as a starting point ("hot-start") for matching methods.

Acknowledgements

This paper documents work conducted in a collaborative e ort by the W3C LBD CG. The author gratefully acknowledges nancial support from MOEEBIUS project, a Horizon 2020 research and innovation program under grant agreement No. 680517 and the initiative Mittelstand 4.0 by the German Federal Ministry for Economic A airs and Energy.

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