standOff. When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote shown on the same row, so the borrowing can be checked against the thing borrowed from without leaving this page. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.| second reading | alternatives | an independent re-reading of a facet, recorded whether or not it agrees |
| relation to other work | claim_relations | how this paper stands to another: agrees, contradicts, extends, qualifies |
| claim type | claim_type | the shape of the claim: existence, mechanism, method, critique, prediction, null |
| concept edge | concept_edges | an asserted relation between two concepts, with the sentence asserting it |
| concept | concepts | an idea the paper works with, quoted where the paper uses it |
| content tag | content_tags | a finding, definition, quantity, statistic or variable named in the paper |
| contribution | contribution | what the paper claims to add |
| limitation | limitations | what the paper says its evidence cannot support |
| mechanism | mechanisms | how the reported effect is said to come about |
| method | method | how the paper produced its evidence |
| object of study | object_of_study | what the paper is about — the thing it investigates |
| phenomenon | phenomenon | the effect or regularity under study |
| research question | research_question | the question the paper sets itself |
| sense | senses | a distinct use of a term, quoted where the paper fixes it |
| transfer | transfers | a method or concept carried in from another field |
The reference list is not part of the text we hold for this paper. Our copy was extracted from the publisher's page and the bibliography did not survive that extraction, so the article below ends before its references. Nothing has been removed by us.
Introduction
The scientific discourse in object-focused and historical disciplines follows certain methods and builds a narration with objects, events, evidence and conclusions.
transfersIn Digital Humanities (DH) research this process and its specific structure has to be represented on a data level. Linked Open Data, Semantic Web ontologies and, in particular, content_tags↗reference models like CIDOC CRM (2022) offer a precise structural vocabulary to describe cultural heritage objects and their relations. These frameworks can support GLAM institutions to make the data on their objects findable, accessible and interoperable.
However, in research projects the course of developing arguments and describing findings is crucial. method↗alternativesWe use Art History research, in particular, the project “Restaging Fashion - Digital contextualization of vestimentary sources” (Refa), as one example to show how emerging discourses within the research process can be mapped in a knowledge organization system and which aspects may not be formally represented. object_of_studyphenomenonconcepts↗concept_edgescontent_tagsA recurring question is: What should be modeled as linked open data (LOD) and what should be entered as free-form text? research_questioncontent_tagsDespite the proven utility of data standards, vocabularies, and authority files providing for accuracy, context, and unambiguity, there is also a need for an open space to formulate and present tentative observations. contributionconcepts↗With this contribution we present how we bridge LOD and full text descriptions through information visualization.
Semantic data modeling in Museums
alternativescontent_tagsSemantic data modeling of a collection during an early stage of the research prompts a trade-off to be made between data standardization and semantic accuracy.concepts↗concept_edgescontent_tags↗Sophisticated ontologies like CIDOC CRM encourage the detailed description of an object through an event-driven structure, which makes it a very powerful tool for the cultural heritage sector by providing for the possibility of adding meaning to the original data.
claim_relationsSince 2006, when the term Linked Data was coined (see Berners-Lee 2006), according to Daquinoet al."only a few pioneers have abandoned legacy cataloguing and archiving systems to fully embrace the Semantic Web paradigm and manage their catalogues through LOD-native management systems" (2022).
When considering the LOD datasets published by The British Museum and the Rijksmuseum, who also create tools such as ResearchSpace (Oldman / Tanase 2018) and dedicated platforms to engage with their collections, content_tagsit appears that these museums only share relatively shallow data models for their artworks on the web. The Rijksmuseum provides basic metadata on objects structured in DC , EDM and LIDO schemas. Only one example is modeled deeply: Linked Art van Gogh object metadata download . But the creators clearly state that: “both the implementation of the model as well as the use of identifiers will be subject to change” (RijksData 2022). mechanisms↗content_tagsThe British Museum, on the other hand, structured its entire collection using CIDOC CRM, but was unable to preserve its database in a consistent and accessible manner (Lincoln, 2015). Data that should be linked together is no longer accessible, making the research of scholars no longer available ( see Programminghistorian 2017).
concepts↗claim_relationscontent_tags↗The potential of these applications is obvious: LOD allows data to be reused and redistributed, revealing relationships with other data on the Web (Berners-Lee 2006). The resources involved, however, are somewhat excessive both from a technological perspective (see De Decker 2015)and regarding the cataloging of information. content_tagsManual tagging can be performed by domain experts in a very precise manner, but it cannot be performed manually to large volumes of already existing content due to the lack of resources (Simou et al. 2017) or may not be conducted automatically due to inconsistencies and/or the lack of standardization in the datasets. mechanisms↗concepts↗This leads institutions to publish only a subset of the available information, exposing minimal metadata (Doerr et al. 2010), which might prevent researchers from using the data and constrains them to conventional research methods.
Data-driven art historical discourse
Art historical research on and with collections is deeply concerned with the history of objects and their discourse such as provenance history, history of interpretation, dating or attribution based on comparable works. This can be expressed with the event structure of CIDOC CRM including descriptive information on the objects like dimensions, inscriptions, and alternative titles also linked to relevant vocabularies like ICONCLASS and AAT . But a crucial part of humanities research consists of annotating that basic formal information, by elaborating and contextualizing the history of the artworks and their interpretation, often concluding in the form of a paper or monograph. Undertaking this part of research in the form of LOD is very intricate and laborious. mechanisms↗concepts↗Complex ontologies will never be completely exhaustive since no cultural artifact or experience could be fully and finally contextualized and vocabularies are not equally accurate and detailed for every subtopic. mechanisms↗In addition, contexts and meanings shift over time, even during the timeframe of a research project. sensesclaim_relationscontent_tags↗According to Galloway "there are some things that are unrepresentable. And the computer is our guide into that realm" (2011: 91). claim_typesensesalternativesclaim_relationsIn terms of representing the research process it is not worth making this part machine -readable as a basis for formal queries. sensesIt is more important to present it in a way that is accessible and understandable to other researchers, which does not require this high level of formalization, because it would end up in overcomplicated queries.
Visualizing context
One way to equally accommodate formal LOD structures as well as full text descriptions is through custom interfaces. In particular, information visualization can be used for the contextualization of images, sources, texts and their relations.
Fig. 1: Draft of Refa interface
limitationsAs part of the Restaging Fashion project, we devised different experimental prototypes, linked specific parts of the textual description to artworks, to classification entries, to archival sources as well as to other cultural heritage objects (see Fig. 1.) with the aim to establish an overview of the context.
content_tagsInstead of hiding the full text in unstructured note fields, we approach this as a starting point for the semantic contextualization of the research process.
In conclusion, certain aspects of humanities research can be properly expressed by semantic web structures. limitationsconcepts↗In particular quantitative information, for instance, on objects and their relations, which can be queried and expressed statistically, are worth to be modeled in triple structures. content_tagsOther, more elaborated, reflexive, or contextualized information may require different forms of representations like visual interfaces.
CIDOC Conceptual Reference Model
Galleries, Libraries, Archives and Museums
Dublin Core
Europeana Data Model
Lightweight Information Describing Objects
https://iconclass.org/
https://www.getty.edu/research/tools/vocabularies/aat/
Relations on this page carry a quotation for the claim. Where a relation was separately evidenced — and so tested against the vocabulary for its type — the row says so; none of 2 relations here carry that mark. The rest are the tagger’s reading of the sentence quoted beside them.
This record holds no controls. A layer is empty when the tagger recorded nothing in it — which is not the same as the paper having nothing to record.
| facet | tag | field | conf. | whose words |
|---|---|---|---|---|
| object_of_study | Where the boundary falls between what an art-historical research project can express as Linked Open Data and what it must leave as prose. | object_of_study | the paper's words | |
| research_question | Structured vocabularies buy accuracy and queryability - so what is lost when the reflexive, contextual part of humanities research is forced into them? | research_question | the paper's words | |
| contribution | Argues that only the quantitative, queryable part of art-historical research is worth modelling as triples, and bridges the rest to it through visual interfaces rather than forcing it into ontologies. | contribution | the paper's words | |
| claim_type | critique | claim_type | the paper's words | |
| phenomenon | linked open data modelling in art history | phenomenon | the paper's words | |
| method | practice-report also in: practice-report | method | the paper's words | |
| mechanisms | mechanisms | mechanisms[0] | 1.0 | the tagger's words |
| mechanisms | mechanisms | mechanisms[1] | 1.0 | the tagger's words |
| mechanisms | mechanisms | mechanisms[2] | 1.0 | the tagger's words |
| mechanisms | mechanisms | mechanisms[3] | 1.0 | the tagger's words |
| limitations | One project, one discipline: the argument is illustrated by Restaging Fashion rather than tested across cases. what this limits: the generality of the argument: it is illustrated by the Restaging Fashion project rather than tested across cases | limitations[0] | 0.9 | the tagger's words |
| limitations | The boundary it proposes - quantitative to triples, reflexive to interfaces - is asserted rather than operationalised; nothing says where a given sentence falls. what this limits: the proposed boundary itself: QUANTITATIVE INFORMATION ... WHICH CAN BE QUERIED AND EXPRESSED STATISTICALLY, ARE WORTH TO BE MODELED IN TRIPLE STRUCTURES - asserted, not operationalised | limitations[1] | 0.8 | the tagger's words |
| transfers | Linked Data, reference ontologies and authority files, applied to art-historical research and found to fit only part of it | transfers[0] | 0.9 | the tagger's words |
| concepts | linked open data also in: linked open data | concepts[0] | the paper's words | |
| concepts | CIDOC CRM also in: CIDOC CRM | concepts[1] | the paper's words | |
| concepts | free-form text also in: free-form text | concepts[5] | 1.0 | the paper's words |
| concepts | information visualization also in: information visualization | concepts[6] | 1.0 | the paper's words |
| concepts | ontologies also in: ontologies | concepts[7] | 1.0 | the paper's words |
| concepts | metadata also in: metadata | concepts[8] | 1.0 | the paper's words |
| concepts | triple structures also in: triple structures | concepts[9] | 1.0 | the paper's words |
| senses | representation - as machine-readable structure, whose point is that a formal query can be run over it | senses[0] | 1.0 | the tagger's words |
| senses | representation - as something another researcher can read and understand, which the paper argues needs no formalisation at all | senses[1] | 1.0 | the tagger's words |
| senses | the unrepresentable - material the paper holds cannot be captured by either, quoting Galloway | senses[2] | 1.0 | the tagger's words |
| alternatives | claim_type: critique a second reading reaching the same value. Recorded: critique — note: a second reading reaching the same value from a different sentence. Not applied - a person decides. | alternatives.claim_type | the tagger's words | |
| alternatives | method: conceptual-analysis a second reading DIFFERING from the recorded value. Recorded: practice-report — note: a second reading, verbatim-verified, disagreeing with the value on the record. Not applied - a person decides. | alternatives.method | the tagger's words | |
| alternatives | phenomenon: the trade-off between semantic formalisation and the expressiveness of humanities research writing a second reading DIFFERING from the recorded value. Recorded: linked open data modelling in art history — note: a second reading, verbatim-verified, disagreeing with the value on the record. Not applied - a person decides. | alternatives.phenomenon | the tagger's words | |
| claim_relations | builds-on → Galloway (2011: 91) on the unrepresentable | claim_relations[0] | the tagger's words | |
| claim_relations | builds-on → Daquino et al. (2022) on institutional uptake of LOD-native systems | claim_relations[1] | the tagger's words | |
| claim_relations | builds-on → Berners-Lee (2006) on Linked Data | claim_relations[2] | 1.0 | the tagger's words |
| claim_relations | contradicts → the programme of modelling cultural heritage research exhaustively in formal ontologies differs in: THE AXIS IS WHICH PART OF THE WORK IS BEING MODELLED. 'IN TERMS OF REPRESENTING THE RESEARCH PROCESS IT IS NOT WORTH MAKING THIS PART MACHINE-READABLE as a basis for formal queries.' The exhaustive-formalisation programme is contradicted for ONE COMPONENT and left standing for the rest - which is exactly the qualification a bare 'contradicts' deletes. | claim_relations[3] | 0.9 | the tagger's words |
| concept_edges | Linked Open Data —contrasted-with→ free-form text strength of the relation: 1
Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span poses the choice between LOD and free-form text as a question; contrasted-with is loose. also in: Linked Open Data · free-form text | concept_edges[0] | the tagger's words | |
| concept_edges | CIDOC CRM —is-a→ ontologies strength of the relation: 1 also in: CIDOC CRM · ontologies | concept_edges[1] | the tagger's words | |
| content_tags | The central question of the paper is a boundary question: what becomes structured data and what stays prose. | content_tags.findings[0] | the tagger's words | |
| content_tags | Standards buy accuracy and unambiguity, but research also needs room for observations that are not yet settled. | content_tags.findings[1] | the tagger's words | |
| content_tags | Modelling a collection early forces a trade-off between standardisation and semantic accuracy. | content_tags.findings[2] | the tagger's words | |
| content_tags | Even major museums publishing LOD share only shallow models of their objects. | content_tags.findings[3] | the tagger's words | |
| content_tags | The British Museum structured its whole collection in CIDOC CRM but could not keep the database consistent and accessible. | content_tags.findings[4] | 1.0 | the tagger's words |
| content_tags | Manual tagging is precise but does not scale, and automatic tagging fails on inconsistent data - so neither route reaches existing collections. | content_tags.findings[5] | 1.0 | the tagger's words |
| content_tags | More elaborated, reflexive or contextualized information may need other forms of representation, such as visual interfaces. | content_tags.findings[6] | 1.0 | the tagger's words |
| content_tags | Free text is treated as the starting point for semantic contextualisation rather than as a residue to be tidied away. | content_tags.findings[7] | 1.0 | the tagger's words |
| content_tags | reference models also in: reference models | content_tags.definitions[0] | the tagger's words | |
| content_tags | CIDOC CRM also in: CIDOC CRM | content_tags.definitions[2] | 1.0 | the tagger's words |
| content_tags | LOD also in: LOD | content_tags.definitions[3] | 1.0 | the tagger's words |
| content_tags | unrepresentable also in: unrepresentable | content_tags.definitions[4] | 1.0 | the tagger's words |
Tags that could not be placed in the text:
What was deliberately not tagged, and why, is recorded per paper by the tagger. Where a paper has no such record, this page says so rather than implying that nothing was declined.