=Paper=
{{Paper
|id=Vol-3741/paper16
|storemode=property
|title=The ARIADNEplus Knowledge Base: a Linked Open Data set for archaeological research
|pdfUrl=https://ceur-ws.org/Vol-3741/paper16.pdf
|volume=Vol-3741
|authors=Alessia Bardi,Miriam Baglioni,Michele Artini,Andrea Mannocci,Gina Pavone
|dblpUrl=https://dblp.org/rec/conf/sebd/BardiBAMP24
}}
==The ARIADNEplus Knowledge Base: a Linked Open Data set for archaeological research==
https://ceur-ws.org/Vol-3741/paper16.pdf
The ARIADNEplus Knowledge Base: a Linked Open
Data set for archaeological research
Alessia Bardi1,* , Miriam Baglioni1,† , Michele Artini1,‡ , Andrea Mannocci1,‡ and
Gina Pavone1,‡
1
Istituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche, Italy
Abstract
The ARIADNE infrastructure provides tools and services for researchers to address archaeological grand
challenges that require discovery and analysis of information scattered across different thematic and
geographically distributed sources. The ARIADNEplus Knowledge Base (KB) is an archaeological Linked
Open Data set modelled according to the ARIADNE ontology, based on CIDOC-CRM, and provided by
an international network of organisations leaders in different domains of archaeological sciences. In
February 2024, the ARIADNEplus KB features about 4 million archaeological resources. Thanks to the
ARIADNE infrastructure, data providers increased the level of fairness of their resources and contributed
to a unique asset for the archaeology research community, the European Open Science Cloud and society
at large.
Keywords
Knowledge graph, semantic web, e-infrastructure, interoperability
1. Introduction
ARIADNE is the Archaeological Research Infrastructure for Archaeological Data Networking in
Europe. Since 2013, ARIADNE establishes a community in archaeological research, gathering
together more than 40 organisations in the domain and about 11,000 archaeologists, correspond-
ing to one third of all European archaeologists and probably more than 50% of those using some
computer support in their research activities [1]. The ARIADNE infrastructure facilitates open
access to Europe’s archaeological heritage and proposes technical solutions to overcome the
fragmentation of digital repositories, placed in different countries and compiled in different
languages [1].
ARIADNE offers resources for digital data analysis, exploration, and research collaboration
in line with the Open Science principles. One of those resources is the ARIADNEplus Knowl-
edge Base (KB), an archaeological Linked Open Data set modelled according to the ARIADNE
ontology and provided by an international network of organisations in the field. In February
SEBD 2024: 32nd Symposium on Advanced Database Systems, June 23-26, 2024, Villasimius, Sardinia, Italy
*
Corresponding and main author.
†
Second author.
‡
These authors contributed equally.
$ alessia.bardi@isti.cnr.it (A. Bardi); miriam.baglioni@isti.cnr.it (M. Baglioni); michele.artini@isti.cnr.it
(M. Artini); andrea.mannocci@isti.cnr.it (A. Mannocci); gina.pavone@isti.cnr.it (G. Pavone)
0000-0002-1112-1292 (A. Bardi); 0000-0002-2273-9004 (M. Baglioni); 0000-0002-5193-7851 (A. Mannocci);
0000-0003-0087-2151 (G. Pavone)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
2024, the ARIADNEplus KB integrates about 4 million archaeological resources including ar-
chaeological reports, findings, inscriptions, archaeological sites and monuments from archives
and repositories in Europe and beyond (e.g. Argentina, Japan).
The integration of archaeological datasets is realised by a metadata aggregation system
based on the D-NET Framework Toolkit [2], which is a framework developed by CNR-ISTI for
the realization, maintenance, and operation of (meta)data aggregative infrastructures. D-NET
has been successfully applied to various thematic domains such as social history (HOPE [3]),
the preservation of film archives (EFG [4]), and ancient epigraphy (EAGLE [5]). It has also
been used to build scholarly communication infrastructures (DRIVER [6] for the aggregation
of metadata about research publications from Open Access repositories) and it is currently
used by OpenAIRE 1 for the construction of a scientific knowledge graph about scholarly
communication. The D-NET framework provides developers with data management services
capable of providing access to different kinds of external data sources, storing and processing
information objects of any data models, converting them into common formats, and exposing
information objects to third-party applications through a number of standard access APIs.
D-NET features infrastructure-enabling services that facilitate the construction of domain-
specific aggregative infrastructures by selecting and configuring the needed services and easily
combining them to form autonomic data processing workflows.
For the ARIADNE research infrastructure, D-NET was configured to harmonise metadata
records according to the ARIADNE Ontology (AO) [7], an extension of the CIDOC-CRM standard.
Data providers define the mapping from their model to the AO with the 3M Editor developed
by FORTH-ICS [8]. The resulting records are stored in a triple store implemented with a
GraphDB2 server and enriched with information from the Linked Open Datasets of the Getty
Art & Architecture Thesaurus (AAT)3 and PeriodO4 , a public gazetteer of scholarly definitions of
historical, art-historical, and archaeological periods. Enriched records form the ARIADNEplus
KB, accessible via a SPARQL endpoint, the GraphDB Workbench and the ARIADNE portal 5 .
This paper describes how the different tools and services for semantic interoperability have
been integrated to realise the ARIADNE aggregator and produce the ARIADNEplus Knowledge
Base. Section 2 introduces the ARIADNE Ontology (AO) and its main classes. Section 3 describes
how archaeological resources are aggregated and enriched. Section 4 provides insights about
the ARIADNEplus KB and how the data is organised to support incremental aggregation of
resources and to keep provenance information at different levels. Section 5 concludes the paper
and outlines future work panned to be carried out in the context of the EC Horizion Europe
project ATRIUM and the ARIADNE Research Infrastructure AISBL, the no-profit organisation
founded to ensure long-term sustainability of the ARIADNE infrastructure.
1
OpenAIRE, www.openaire.eu
2
GraphDB, https://graphdb.ontotext.com/
3
Getty AAT, https://www.getty.edu/research/tools/vocabularies/aat/about.html
4
PeriodO Gazetteer, https://perio.do/en/
5
ARIADNE Portal, https://portal.ariadne-infrastructure.eu/
2. The ARIADNE Ontology
The ARIADNE Ontology (AO) [7] was developed to integrate archaeological data of different
type, granularity and geographical scope into a common information space. The CIDOC-CRM,
the standard ontology in the cultural heritage domain, is the conceptual backbone of AO. AO
specialises CIDOC-CRM to address specific modelling needs of archaeological sub-domains:
• AO-Cat for the representation of cataloguing information. It captures the basic ’What’,
’When’ and ’Where’ information and provides an adequate representation for the discov-
ery of resources relating to archaeological sites, monuments, artefacts, and data from the
palaeo-anthropology, environmental, maritime and underwater archeology, and public
archaeological finds;
• CRMhs for the representation of scientific data;
• aDNA for the representation of bio-archaeology and ancient DNA.
For other sub-domain we used AO-Cat in combination with existing extensions of CIDOC-CRM:
CRMarchaeo for field survey information, CRMba for standing structures, and CRMtex for
inscriptions [9, 10].
Figure 1 shows the AO-Cat class taxonomy:
• AO_Entity: the most general class of AO-Cat, all classes being sub-classes of AO_Entity.
• AO_Resource: any digital resource in the ARIADNE research infrastructure. The class
hierarchy of AO_Resource is shown in Figure 2. Instances of AO_Resource can be:
– AO_Service: a digital representation of a service, intended as an offer by some actor
of their willingness and ability to execute an activity or series of activities upon
request;
– AO_Data_Resource: represents an archeological data resource at different granu-
larity levels via the subclasses AO_Individual_Data_Resource and AO_Collection.
Documents and digital images are sub-classes of AO_Individual_Data_Resource.
• AO_Object: digital representation of a physical object (e.g. an item found during an
excavation).
• AO_Concept: terms used to classify entities in terms of type and subject.
• AO_Spatial_Region: represent spatial location identified as points, polygons, bounding
boxes or simple place names.
• AO_Temporal_Region: represent time as absolute dates, time intervals expressed as
absolute dates or period names. Period names are harmonised with the PeriodO gazetteer
service.
• AO_Event and AO_Activity: correspond to CIDOC CRM events and activities.
• AO_Agent: persons or organisations that hold responsibilities for resources or that carry
out activities (e.g. publisher, contributor).
For the full description of the model, refer to [7].
Figure 1: AO-Cat class taxonomy
Figure 2: AO_Resource class hierarchy
3. Methodology
The ARIADNEplus Knowledge Base (KB) is populated via the ARIADNE aggregator, a D-NET
instance customised for ARIADNE. The ARIADNE aggregator is capable of collecting detailed
descriptions of archaeological resources (metadata records) in different formats, transforming
the records according to the ARIADNE Ontology, enriching them with subject terms via Getty
AAT and dating information via PeriodO and making them available via a SPARQL endpoint
and the ARIADNE portal. The ARIADNE portal 6 , developed by the Swedish National Data
Service, where the resources can be searched and filtered by different criteria (e.g. by location,
by historical period, by subject, by contributor).
Figure 3 shows the workflows defined for the processing of each dataset:
1. Ingestion of XML records of the provider. The workflow applies a 3M mapping [8] to
each of the input records of a data source and generates RDF/XML records compliant
with the ARIADNE Ontology. Transformed records are suitable for ingestion into the
ARIADNEplus KB, an instance of GraphDB.
2. Enrichment with Getty AAT subjects. To better qualify the archaeological resources,
providers are asked to map their local subjects and concepts into the Getty AAT. Getty
AAT is a thesaurus of concepts describing different aspects of cultural heritage, such
as materials, techniques, cultures (e.g., amphora, oil paint, Buddhism). The mapping
between terms used by data providers and Getty AAT terms is done using the Vocabulary
Matching Tool developed by University of South-Wales 7 [11]. The subject mapping to
Getty AAT is transformed into RDF and fed into the KB. As a result, the KB contains the
correspondences between native subjects and terms within the Getty AAT vocabulary.
3. Enrichment with PeriodO. For the enrichment with dating information, providers curate
an authority file on PeriodO. The authority file specifies the time spans in absolute
dates of historical periods that are referred in the metadata records [12]. The authority
file is ingested into the knowledge base and used to generate explicit aocat:has_period
properties.
6
ARIADNE Portal, https://portal.ariadne-infrastructure.eu/
7
Vocabulary Matching Tool, https://vmt.ariadne.d4science.org/vmt/vmt-app.html
Figure 3: Workflows for the aggregation and enrichment of archaeological resources
4. Feed the staging knowledge base. In order to support the providers with checking the
content before it is made publicly available, the push on the knowledge base initially
targets a “staging” instance. Specific SPARQL INSERT statements are executed (4a) in
order to enrich records with AO-Cat properties that are not explicitly available in the
mapped records, but that are statically known or can be inferred from other properties or
related records (e.g. inheritance of properties of a dataset from its collection). Step 4a also
allows the aggregation manager to address possible peculiarities in the data that could
complicate the automatic feeding to the portal.
5. Feed the public knowledge base. This flow is executed if the provider successfully completed
the content checking on the staging knowledge base and portal.
6. Feed the public portal. The flow applies the same procedure as step 5, using the public
instances of the knowledge base and portal instead of the staging instances.
Steps 4 and 5 support the quality checks of content and can be bypassed once the input
format of the records and the 3M mappings of a provider are stable, so that it will be possible to
automatically update the knowledge graph and the ARIADNE portal with updated and new
records without human intervention. If necessary, steps 4 and 5 might be reactivated for a
given source (e.g. because the provider upgraded the information system and wants to perform
extensive checks before the data supplied by the new system goes public).
4. The ARIADNEplus Knowledge Base for Archaeological
Sciences
As of February 2024, the ARIADNEplus KB integrates about 4 million archaeological resources
including archaeological reports, findings, inscriptions, archaeological sites and monuments
from archives and repositories in Europe and beyond.
On GraphDB, we count about 490M of triples (subject - predicate - object) describing 13K
instances of AO_Collections and 3.9M instances AO_Individual_Data_Resource provided by 59
publishers. The ARIADNEplus KB is stored on a GraphDB server (version 9.8, free edition).
Data on GraphDB is organised in named graphs so that the ARIADNE aggregator can incre-
mentally update the KB. GraphDB features one named graph for each data source. All records
aggregated from the same data source are stored as triples in the same name graph.
The aggregator is thus able to request the deletion of that specific data source without affecting
triples of other data sources. This feature of isolation was a requirement to support continuous
aggregation and automatic update of the KB. Every time a dataset is updated (because the input
metadata records changed or the 3M mapping changed), the aggregator requests the deletion
of the named graph that corresponds to the data source at hand and then proceeds with the
feeding of the updated records.
Enhancements to the provided records are added to dedicated named graphs, following a
similar logic. As a result, for each data provider, GraphDB features several named graphs:
• One named graph with the triples of the records aggregated from the same data source.
If the provider manages different data sources (e.g. two databases), then GraphDB will
feature one named graph per data source.
• One named graph with the matches between local subjects and Getty AAT terms as
defined by the provider with the Vocabulary Matching Tool.
• One named graph with the PeriodO authority files of the provider.
• One named graph with the triples inferred by intersecting the aggregated data and Getty
AAT based on the provided matching.
• One named graph with the triples inferred by intersecting the aggregated data and the
PeriodO authority files of the provider.
In addition, the aggregator adds provenance information to a special graph. The provenance
graph contains information about when and which endpoints and which data sources have been
added to the ARIADNEplus KB. Its triples are compliant with the PAV (Provenance, Authoring
and Versioning) ontology [13].
The benefits of such a partition of content on GraphDB target data curators, aggregation
managers, and end-users in different ways:
• Machine discoverability of new content and new providers in the KB thanks to the
provenance graph;
• Easy identification of what has been aggregated and what has been inferred;
• Easy update of each subset of triples. If there are mistakes in the inference rules, only the
graphs with inferred triples can be deleted, the inference rules updated and the relative
graphs regenerated;
• Continuous updates of PeriodO terms, Getty AAT matching, and input data do not affect
each other and can be run in isolation.
The main drawback is that SPARQL queries have to explicitly target different named graphs
to get complete information about a resource. This may be not very convenient, especially
for end-users who might not be fully aware of how the data is organised. We addressed the
problem by engaging with the users and providing clear and public documentation.
Table 1
Top 5 results of query in Listing 1
Publisher Subject Count
Archaeology Data Service Site/monument 776,056
British Museum Artefact 476,224
Historic Environment Scotland Site/monument 334,636
Museum of Cultural History, University of Oslo Artefact 296,165
Archaeology Data Service Fieldwork 189,803
In December 2022 we organised a hackathon at the LinkedPasts conference in York, where
we engaged with users of the ARIADNE infrastructure and IT people with a knowledge of the
archaeological field. User support and feedback is continuously gathered via the ARIADNEplus
Lab Virtual Research Environment (VRE)8 . The VRE offers a social feed where users can post
and reply to comments and questions.
Finally, we prepared documentation with examples in the form of a Jupyter Notebook that
can be run on the JupyterHub available via the ARIADNEplus Lab VRE. The Jupyter Notebook
uses Python and the SPARQL Wrapper library9 to execute queries that are useful to understand
the organisation of the data, its coverage and richness. Listing 1, for example, gets the number
of resources grouped by publisher and typology (aocat:has_ARIADNE_subject). The first five
results are in Table 1.
Listing 1: Get the number of resources grouped by publisher and typology
PREFIX a o c a t : < h t t p s : / / www. a r i a d n e − i n f r a s t r u c t u r e . eu / r e s o u r c e /
ao / c a t / 1 . 1 / >
PREFIX r d f : < h t t p : / / www. w3 . o r g / 1 9 9 9 / 0 2 / 2 2 − r d f − s y n t a x − ns # >
PREFIX r d f s : < h t t p : / / www. w3 . o r g / 2 0 0 0 / 0 1 / r d f − schema # >
SELECT ( c o u n t ( ? r e s o u r c e ) AS ? c n t ) ? p u b l i s h e r N a m e ? a s l WHERE {
? resource aocat : has_publisher ? publisher .
? p u b l i s h e r a o c a t : has_name ? p u b l i s h e r N a m e .
? r e s o u r c e a o c a t : has_ARIADNE_subject ? a s .
? as r d f s : l a b e l ? a s l
}
GROUP BY ? p u b l i s h e r N a m e ? a s l
ORDER BY DESC ( ? c n t )
5. Conclusion and future work
To our knowledge, the ARIADNEplus KB provides access to the largest international archaeo-
logical dataset available online, with about 4 millions resources maintained by 59 publishers in
Europe and beyond.
8
ARIADNE Lab Virtual Research Environment by D4Science.org, https://ariadne.d4science.org/group/ariadneplus_lab
9
SPARQL Wrapper Library, https://github.com/RDFLib/sparqlwrapper
Thanks to the ARIADNE Ontology, based on the CIDOC-CRM standard, heterogeneous data
can be harmonised and offered via a single-entry point.
The harmonisation process is managed by the ARIADNE aggregator, a system based on the
D-NET framework toolkit, the 3M Editor and the Vocabulary Matching Tool. The combination
of the three services proved to be effective to deal with very different use cases and to manage
interoperability challenges due to idiosyncratic exchange protocols, metadata models and
formats.
Organizations providing content to the KB improved the level of FAIRness of their data.
Each resource in the KB is described according to the ARIADNE Ontology, which was defined
together with the research community to address the needs of researchers in archaeology ad
its many sub-domains. Each resource is assigned a unique and persistent URL that resolves
either on its landing page on the ARIADNE portal or to its RDF/XML representation (based on
content negotiation). Descriptions of the resources are enriched with properties and links to
standard vocabularies and gazetters (e.g. PeriodO and Getty AAT). The KB is a Linked Open
Dataset, compliant with the Resource Description Framework and queriable via the standard
SPARQL protocol.
The re-usability of the KB and of the software of the portal10 is demonstrated by the Unpath’d
Waters Portal11 launched in April 2023. The Archaeology Data Service adapted the ARIADNE
portal to provide a discovery portal for resources about maritime heritage of UK coastal waters
available in the ARIADNEplus KB. As highlighted also in [10], the same approach could be
easily adopted by other projects or initiatives willing to provide thematic or national portal
without the burden and costs of maintaining a dedicated aggregation system.
In November 2022, ARIADNE has become a not-for-profit association registered under
Belgian law, but operating internationally, named ARIADNE Research Infrastructure AISBL.
As of February 2024, ARIADNE RI AISBL has 29 organisational members from 20 countries,
including Italy, United Kingdom, and Japan.12 The setup of the association was fundamental to
ensure the long-term sustainability of the ARIADNE infrastructure.
Thanks to the participation in the project ATRIUM (Advancing FronTier Research In the
Arts and hUManities), the ARIADNE infrastructure will further grow its community and the
coverage of its knowledge base. The project is funded by the European Commission under the
Horizon Europe Framework programme. It started in January 2024 and will last for 4 years.
During the project the ARIADNEplus KB will be enriched with additional content like reports
on primary fieldwork, standing building surveys, images, and an improved management of
geo-spatial data. ARIADNE services will also be registered in the SSH Open Marketplace13 ,
contributing to the European Open Science Cloud for Social Sciences and Humanities.
10
ARIADNE portal software, https://github.com/ariadne-infrastructure
11
Unpath’d Waters Portal, https://unpathd.ads.ac.uk/
12
ARIADNE Research Infrastructure AISBL members, https://www.ariadne-research-infrastructure.eu/partners/
13
SSH Open Marketplace, https://sshopencloud.eu/ssh-open-marketplace
6. Data availability statement
The ARIADNEplus KB is accessible via the ARIADNEplus Lab Virtual
Research Environment (VRE) hosted by the D4Science infrastructure at
https://ariadne.d4science.org/group/ariadneplus_lab/.
The Jupyter Notebook with instructions and sample queries is available at
https://data.d4science.net/YuUq and can be run on the JupyterHub available in the
VRE linked above.
The ARIADNE portal is accessible at https://portal.ariadne-infrastructure.eu/. Its source code
is at https://github.com/ariadne-infrastructure.
Acknowledgments
This work has been supported by ARIADNEplus EC H2020 Grant 823914 and ATRIUM EC
HE Grant 101132163. The ARIADNE aggregator is operated by CNR-ISTI on the D4Science
infrastructure (https://www.d4science.org/). We thank the members of the ARIADNEplus
project, during which the aggregation system was designed and developed, especially the
members of the aggregation task force, with their strong commitment and passion: Ceri Binding,
Achille Felicetti, Carlo Meghini, Enrico Ottonello, Julian Richards, and Maria Theodoridou.
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