Towards preservation of semantically enriched architectural knowledge

1 / 20 20 / 03 / 2013
Towards Preservation of semantically
enriched Architectural Knowledge
Stefan Dietze (L3S Research Center, Leibniz University Hanover, DE)
Stefan Dietze, Jakob Beetz, Ujwal Gadiraju, Georgios
Katsimpras, Raoul Wessel, René Berndt

2 / 20 20 / 03 / 201327/09/13
Challenges
 Diversity of data - interoperability: low-level
point clouds & legacy 3D models up to enriched
Building Information Models (BIM), higher-level
semantics and Web data / knowledge
 Diverse stakeholders: architects, building
operators, urban planners, archivists, …
 Building, model and data evolution: document
temporal evolution to prevent information loss
Goals and Challenges (1/2)
Goal
 Methods and tools for sustainable long-term
preservation of architectural knowledge
Stefan Dietze (L3S Research Center)

3 / 20 20 / 03 / 201327/09/13
Challenges
 “Semantic” enrichment of
architectural knowledge: exploiting
Web data and knowledge to enrich
low-level architectural data.
 Inconsistent vocabularies: adopting
state of the art (LD) vocabularies and
schemas towards sustainability
 Long-term readability / renderability
of architectural models: addressing
digital decay (eg due to deprecated
file formats) and model evolution
Architectural Archives Architectural Web Data
Goals and Challenges (2/2)

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UBO: Universität Bonn
- Technical Coordinator
- WP4/WP5: change management, shape
recognition
Fraunhofer Austria
- WP2: system specification
& integration
TUE, Department of the Built Environment,
Eindhoven University of Technology
- WP3:,semantics & metadata
CITA, Center for Information Technology
and Architecture Copenhagen
- WP7: data, evaluation, test
Luleå University of Technology
- WP8: dissemination/exploitation
Catenda, SME
- User perspective, market requirements, evaluation
LUH: German National Library of
Science and Technology (TIB) &
L3S Research Center Hannover
-Coordinator
- WP3 Semantic Enrichment
- WP6 leader, long-term preservation
Consortium

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Why interlinking & semantic enrichment?
policies
traffichistory
environment
infrastructure
1. research 2. design 3. monitoring (over time)
A very simplistic view on urban planning/architectural lifecycle today
DURAARK approach - exploiting Web data to help architects and urban planners to
answer questions like:
 What‘s the legal, social and environmental context of a structure (sustainability policies etc)?
 How did buildings and their contexts (traffic, surroundings, usage and functionality, popularity, etc)
evolve over time?
 How did an architectural change impact surrounding traffic/environment?
(examples: bridges, airports)
 How did an architectural change impact popularity and attractiveness of a building?
 ….

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Architectural Data Preservation
3D Models
Point Clouds
Building Information
Models (BIM)
= structured „Building
Model Metadata“

7 / 20 20 / 03 / 201327/09/13
Architectural Data Preservation
SDA Scope
 Semantic enrichment of low-level architectural models
(gradual process)
 Interlinking of related models/data
(across different abstraction levels, model types, datasets
and repositories…)
 Preservation & temporal analysis: tracking the evolution
of models, buildings and related data

8 / 20 20 / 03 / 2013
Example: GDR’s People’s Palace - static vs evolving data/links
Social & Semantic Web for enrichment

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Semantic enrichment – schema/knowledge types
Challenges
 Selection of suitable datasets from
wealth of diverse datasets
 Preservation: dealing with evolution
of distributed datasets (i.e. the
semantics & context of the
structure/models)

10 / 20 20 / 03 / 201327/09/13 Stefan Dietze (L3S Research Center)
Data selection: too few information about too many datasets
 Lack of reliable dataset metadata but wide diversity (eg, DBpedia vs traffic stats London vs … ) :
 Spatial and temporal coverage ?
 Dynamics ? (evolution, frequency of changes…)
 Resource types & topics ? (policy documents vs traffic statistics)
 Currentness, availability, provenance, ….
Enrichment & Preservation
http://datahub.io/dataset/transport-data-gov-uk
329.527.661 triples
metadata
LOD cloud: 300++ datasets
DataHub: 6000++ datasets

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<geoLatLong:52/13>
Data preservation: handling evolution of distributed data
 Preservation needs to address evolution of distributed datasets / semantics of links
 In RDF graphs (such as the LOD Cloud), „all“ nodes are connected:
 Which datasets to preserve (only direct links or also more distant neighbours)?
(semantic relatedness, see [ESWC2013])
 Propagation of changes in LOD graph => measuring relevance of changes for specific entities
<dbp:Berlin(east)>
<dura:GDR Peoples Palace>
<dbp:Berlin>

12 / 20 20 / 03 / 201327/09/13
<geoLatLong:52/13>
Data preservation: handling evolution of distributed data
 Preservation needs to address evolution of distributed datasets / semantics of links
 In RDF graphs (such as the LOD Cloud), „all“ nodes are connected:
 Which datasets to preserve (only direct links or also more distant neighbours)?
(semantic relatedness, see [ESWC2013])
 Propagation of changes in LOD graph => measuring relevance of changes for specific entities
 Preservation strategies dependent on dataset dynamics
 Simple linking (archiving) for static datasets (eg statistics over past periods in data.gov.uk)
 Recurring link computation and graph archival for dynamic datasets (frequency?)
<dbp:Berlin(east)>
<dura:GDR Peoples Palace>
<dbp:Berlin>
Traffic statistics
(1986-1989)
Traffic statistics
(2013-…)
Energy efficiency policies

13 / 20 20 / 03 / 201327/09/13
Approach: dataset profiling
 Enrichment & preservation = intertwined process!
 Dataset selection & cataloging: via DataHub.io
(similar to LOD cloud)
 Dataset profiling: metadata about dataset dynamics, size,
types, topics, evolution, temporal/spatial coverage etc
=> Data observatory (see also [ESWC2013], [ISWC2013])
 Vocabulary curation (expert-based)
Web Data Curation for Building-related Data
DURAARK
Data Observatory
Automated processing to generate:
 Descriptive Dataset Profiles
 Data Interlinking & Correlation
describes
Endpoint
Retrieval & Graph
Extraction
Schema
Extraction and
Mapping
Sample Graph
Extraction
(per dataset)
NER & NED
(per resource)
Interlinking & Co-
Resolution
(cross-dataset)
Profiling
(topics, coverage,
dynamics,…)http://datahub.io/group/linked-building-data

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Endpoint
Retrieval & Graph
Extraction
Schema
Extraction and
Mapping
Sample Graph
Extraction
(per dataset)
NER & NED
(per resource)
Interlinking & Co-
Resolution
(cross-dataset)
Profiling
(topics, coverage,
dynamics,…)
Dataset
Catalog/Index
Links/
Cross-references
rdfs:label:„…ECB….“ ?
Dataset metadata (RDF/VoID):
 Schema mappings
(types, properties)
 Entities & categories
 Topic relevance scores
 Availability, currentness
data (tbc)
dbpedia:Finance
dbpedia:Sports
dbpedia:England-Wales-Cricket-Board
dbpedia:European_Central_Bank
Goals:
 RDF catalog of datasets
 Tracking the evolution
of datasets according
to, eg, topics,
dynamics, spatial
coverage, accessability
 Links and coreferences
=> unified view on data
=> Linked Building Data
Graph
 Infrastructure & APIs for
federated queries
Dataset profiling: processing workflow
Towards a Web Data ”Observatory”
dbpedia:Frankfurt

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Pipeline
 Demo categories!
Web Data Observatory – ongoing work
http://data.linkededucation.org/linkedup/categories-explorer

16 / 20 20 / 03 / 201327/09/13
Vocabulary Curation & Data Interlinking
 Using dataset profiles for semi-
automated data interlinking:
 Manual alignment of schemas &
vocabularies into unified RDF
graph
 Automated interlinking (and
preservation) techniques
 Preservation metadata (PREMIS RDF?)
 Expert-based curation of building-
related vocabularies
 BuildingSmartDD
(http://www.buildingsmart.org
/standards/ifd)
 OMNIClass, UNIClass
 SFB-NL (http://nl-
sfb.bk.tudelft.nl)
 CROW Library for
infrastructural objects
(http://www.gww-ob.nl/)
 …

17 / 20 20 / 03 / 201327/09/13
Conclusions
Summary
 “Data Observatory” as generic platform and domain-specific instantiation
(profiling building-related dataset aspects in DURAARK)
 Preservation/linking strategies for SDA based on dataset profiles (eg dynamics, relevance)
SDA Scope
Outlook
 Dataset selection: populating DataHub-
group
 Schema and vocabulary curation and
alignment
 Dataset profiling: establishing LDO,
considering range of metadata aspects
 Building SDA: data interlinking & dataset
preservation
DURAARK
Data Observatory
ongoing work
future work

18 / 20 20 / 03 / 201327/09/13
Thank you!
http://purl.org/dietze | @stefandietze
http://www.duraark.eu

Towards preservation of semantically enriched architectural knowledge

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