=Paper=
{{Paper
|id=Vol-1764/4
|storemode=property
|title=CIDOC CRM-based modeling of archaeological catalogue data
|pdfUrl=https://ceur-ws.org/Vol-1764/4.pdf
|volume=Vol-1764
|authors=Aline Julia Elisabeth Deicke
|dblpUrl=https://dblp.org/rec/conf/mtsr/Deicke16
}}
==CIDOC CRM-based modeling of archaeological catalogue data==
https://ceur-ws.org/Vol-1764/4.pdf
CIDOC CRM-based modeling of archaeological
catalogue data
Aline Deicke1
1
Academy of Sciences and Literature | Mainz, Digital Academy, Mainz, Germany
Aline.Deicke@adwmainz.de
Over the last decades, the CIDOC Conceptual Reference Model has
become the internationally recognized standard for modeling data from the field
of cultural heritage. With the growing digitalization of the humanities, the scope
of use cases for the CRM is being steadily extended to topics beyond museum
knowledge. Among those is the digital publication of archaeological catalogues
that focus on the description of sites, associated finds, objects, and the relations
between them. This paper presents an exemplary modeling of a standard
archaeological catalogue object and offers an outlook on the benefits such a
model provides for researchers.
Keywords: CIDOC CRM · data modeling · digital humanities · digital
archaeology
1 Introduction
Since its inception in 1996 by the International Committee for Documentation
(CIDOC) of the International Council of Museums (ICOM), the CIDOC Conceptual
Rreference Model (CRM) has become the internationally recognized standard for the
description of concepts from the field of cultural heritage. The implementation as an
ISO-Standard (ISO 21127) in 2006 confirmed the status of the CRM as the essential
ontology for all disciplines dealing with material culture. Through its strong
semantics and its object-oriented approach it is independent of specific technologies
and facilitates sharing and exchange of data [1], [3].
The overall scope of the CRM is defined as the »curated knowledge of
museums«, but it is intended to include all forms of academic research and field work
[3]. Implementations such as the Centre for Archaeology’s CRM-EH have made use
of its possibilities to create extensive models of their own domains [1,2].
Project-specific modelings such as these are especially important in cases
where data is intended to be subsequently re-used by other projects and researchers,
so that there can be a clear understanding of the modeled entities themselves and of
any biases implicit in the database structure. One important use case in this context
concerns the growing importance of Linked Open Data and the Semantic Web,
especially in archaeology. Projects like ARIADNE1, Pelagios Commons2, Perseus3 or
Arachne4 have all contributed to the growing number of resources linked in the part of
the Linked Data Cloud sometimes referred to as the Graph of Ancient World Data [1],
[4], [6,7]. This potential for comprehensive analysis beyond the context of one
specific project can only be unlocked if the underlying data is well structured
according to a commonly used ontology.
1.1 Archaeological catalogues
Over the last decades, more and more of archaeological research and its
publication have moved into the digital realm. While most publications still take the
form of text, some projects have begun to make raw data and databases available
online.
One of the most common forms of archaeological publication is the catalogue.
It lists, describes and classifies finds from a certain context that can range from
depositions like a single hoard to whole regions and countries. Depending on the
objective of the author, the catalogue can be a detailed listing of objects and their
properties, for example an excavation report, or it can give further typological,
chronological or bibliographic information on its subjects, either as a stand-alone
effort or as the basis of an independent research project. This data is especially
interesting for subsequent use in projects utilizing digital research methods, e.g.
network analysis, or as an addition to the Linked Data Cloud as described above.
The model presented in this article has been developed for a graph database. It
serves as the foundation of the author’s dissertation about the genesis of an elite
identity in the last period of the Late Bronze Age as reflected by rich burials in the
area of the so-called Urnfield Cultures. At the same time, it has been structured in a
way that also takes into account a more generic understanding of catalogue editions as
collections of archaeological finds, subsumed by context, of a certain period over an
associated geographical area.
The benefits of a time-costly and complex modeling process [1], [6] such as
this might not be evident at first, especially to independent researchers or small
project teams with no ambition to further disseminate their raw research data. Yet the
use of a well-known ontology and the subsequent publication of well-structured data
facilitate communication not only about the data itself but also about research results,
and opens the latter up to well-informed discussions. Also, modeling the data basis on
which a study rests can lead to a deeper structural understanding of the study subject.
Depending on the complexity of the source material, it can take on characteristics of
an exploratory process that can result in new ideas and perspectives [5]. Furthermore,
while data modeling – as all processes of categorization and interpretation – implies a
1
http://ariadne-infrastructure.eu/ (Accessed 2016-12-01).
2
http://commons.pelagios.org/ (Accessed 2016-11-07).
2
http://commons.pelagios.org/ (Accessed 2016-11-07).
3
http://www.perseus.tufts.edu/hopper/ (Accessed 2016-11-07).
4
http://arachne.uni-koeln.de/drupal/ (Accessed 2016-11-07).
certain degree of generalization, ideally it does so in a systematic way that exposes
these biases and makes them explicit and verifiable. This last point is especially
important in a field like archaeology whose source material is inherently characterized
by gaps that might not always be easily recognizable. Careful ontological modeling
can help to identify these missing pieces and as such strengthens inferences drawn
from the data [1].
2 Exemplary data model for archaeological catalogue data
The provision and presentation of catalogue data has a different focus than the
documentation of museum information or the processing of excavation results. The
emphasis on events as evident in the CRM [3] shifts towards contexts as established
by the discovery and the academic analysis of finds. The presented model shows an
ongoing effort to capture these contexts and the semantic structure of an
archaeological catalogue listing detailed information about elite graves of the Late
Urnfield Culture. As such, figure 1 concentrates on the core aspect of a closed find
and its associations and presents several related concepts such as the detailed
description of specific timespans or the activities surrounding an excavation in an
abridged version.
The central object of the model is the closed context of the find itself, be it a
grave, a hoard or a single find. It is assigned the class E19 Physical Object, which
includes «all aggregates of objects made for functional purposes of whatever kind»
[3]. While it itself can be part of a larger context (E27 Site), it can also contain several
types of objects, namely E20 Biological Object such as animal bones or remains of
organic materials, E21 Person comprised of E20 such as a skeleton or cremated
bones, E25 Man-Made Feature detailing e.g. grave architecture, and E22 Man-Made
Object which stands for the single objects composing the find.
Several of the classes connected to E22 can also be used to describe properties
of E20, E21 or E25, like E57 Material or E3 Condition State. Specific mostly to E22
is the construct of typological classification built from several instances of E55 Type
connected through P127 has broader type, and E83 Type Creation.
This last class is one of the components of the model that refers to the
scientific analysis of the material and its embedment into the broader archaeological
literature. Another example of this is presented in the structure around E4 period that
aims less to connect the object to a specific timespan, than to set it in relation to the
intricacies that can make up archaeological relative dating systems. Some cases that
are included in figure 1 are periods that are specific to regional groups (E4 > P7 took
place at > E53 Place) or periods that are known under different names in different
regions (E4 > P114 is equal in time to > E4). Each E4 can be identified by its relative
term E49 Time Appellation (e.g. Ha B3) as well as absolute dates E50 Date. Given the
equal standing of absolute and relative chronology in archaeology, the at times
tenuous connection between the two and the problems absolute dating methods such
as radiocarbon dating can have in determining the exact age of objects, E50 as a
subclass of E49 Time Appellation is used instead of E52 Time-Span5 to emphasize the
subjectivity inherent in assigning both relative as well as absolute dates.
Both appellations, E49 as well as E50, should be used only in conjunction with
the pattern P70 documents < E31 Document, which places the appellations in the
context of the corresponding literature. Indeed, due to the nature of the database as a
tool for and documentation of a research process, this construct can and should be
used for almost every class represented in the model. This ensures that concepts
which have been defined by more than one author, for example typo-chronological
assignations, can be recognized in the intended meaning, and as such guarantees the
study’s scientific viability. Other generally applicable classes that give further details
and context are P3 has note > E62 String, and E42 Identifier.
Several other areas of the model deal with the construction of geographic
information or the context of discorvery, e.g. excavation, as can be grasped from
figure 1.
3 Conclusions and Outlook
The increasing digitalization of archaeology, especially the growing
importance of linked data and semantic web technologies, demands data that is well
structured, documented and easy to share. At the same time, data modeling as a
process can lead to a deeper structural understanding of the study subject, expose gaps
and biases in the data and results in new ideas and perspectives.
The CIDOC CRM provides the common ontology that enables research
projects as well as independent researchers to engage in such a process to meet the
mentioned criteria. Many efforts have already been undertaken [2], [4], yet further
discussions about modeling processes are necessary to cover the diverse and varied
areas of archaeological research.
The paper presents an ongoing approach at modeling one of these areas,
namely objects described in a standard archaeological catalogue, to facilitate
discussion about and sharing of research data. It proposes a way of modeling several
key concepts common to archaeological research, chief among them the
bibliographical documentation of implicit assumptions, and is intended to serve as a
starting point for researchers dealing with archaeological catalogue data to create
models for their respective use cases.
Further efforts to refine the model will include the positioning of objects (E19,
E21, E20, E22, E25) in relation to each other, a closer look at more complicated
patterns of deposition, the possibility of assigning cultural affiliations to entities and
that of documenting the reasoning behind decisions about chronological or
typological classifications.
5
This class is used elsewhere in the model in conjunction with E7 activity, which describes a
recent and therefore dated event, e.g. an excavation.
Fig. 1: CIDOC CRM-based data model of a catalogue object
4 References
1. Cripps, P., Greenhalgh, A., Fellows, D., May, K., Robinson, D.: Ontological Modelling of
the Work of the Centre for Archaeology. Centre for Archaeology, English Heritage (2004)
2. crmeh. For people interested in Conceptual Reference Modelling and Ontologies for the
broader Cultural Heritage domain, https://crmeh.wordpress.com/
3. Crofts, N., Doerr, M., Gill, T., Stead, S., Stiff, M. (eds.): Definition of the CIDOC
Conceptual Reference Model. CIDOC (2011)
4. Erlangen CRM / OWL, http://erlangen-crm.org/
5. Flanders, J., Jannidis, F., Knowledge Organization and Data Modeling in the Humanities
(2015)
6. Isaksen, L., Simon, R., Barker, E. T. E., de Soto Canamares, P.: Pelagios and the emerging
graph of ancient world data. In: WebSci ’14. Proceedings of the 2014 ACM conference on
Web science, pp. 197–201. ACM, New York (2014)
7. Robineau, R.: Graph of Ancient World Data (2012)