Safeguarding Information Technology (IT) heritage is a matter of general interest. This is the aim of the ITinHeritage project, which takes a heritage-based approach to IT museums in France and around the world. The unprecedented study of this heritage leads us to understand how to perpetuate and mediate contemporary scientific and technical knowledge, of which data is the new heritage. They also the new corpora, which means that terminology work needs to be rethought. The ITinHeritage project aims to develop an innovative approach to digital humanities by creating a corpus from the collections of IT museums, organised in the form of a knowledge graph, and by using methods and tools that combine traditional linguistic approaches and explorations in the computer sciences. Big data, AI and NLP are thus being used to explore, enhance and perpetuate their own heritage.
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Looking at this heritage is a crucial part of understanding today’s world and how society has evolved since the second half of the twentieth century. But how can we define it, represent it, promote it and preserve it in its rapid and proliferating evolution? Our approach, which is resolutely heritage-based and interdisciplinary, takes as its starting point the IT museum spaces that have initiated this heritage enhancement. The Musée des Arts et Métiers (MAM, France), ACONIT (Grenoble, France), the London Science Museum (London, UK), the NAM-IP (Namur, Belgium), the Museo degli strumenti per il calcolo (Pisa, Italy), the Home Computer Museum (Helmond, Netherlands) and the HNF (Paderborn, Germany) are actively participating in our expanding network. The ITinHeritage research project thus aims to conduct an epistemological reflection on the issue of IT by placing at the heart of the question of heritage the history of a societal mutation and the emergence of new knowledge in terms of representations of the world.
Our starting point is the as yet unexplored definition of this labile heritage, whose knowledge
is crystallised in various forms. Firstly, through the expression of its explicit knowledge, through
its physical objects and its digital objects: the digitised artefact and its documentation, the
software which represents 1/3 of certain collections and above all the data which forms the new
collections of science. Secondly, through the expression of the tacit knowledge of IT, which has
not yet been questioned or given a heritage status. This tacit knowledge is expressed at the
experiential level. Whereas ’pure knowledge’ [
We are therefore placing language at the heart of the ITinHeritage project by combining
complementary and innovative approaches aimed at the interaction between terminology and
ontology. This link is not a new theme. From this theoretical perspective, Sager (1990) adopts
an onomasiological approach, but based on corpora of lexical data collections, adapted to the
creation of special linguistic vocabularies. The descriptive/socio-terminological theory in France,
theorized by Gaudin [
Data are not only the new objects (collections) of heritage, they are also the new corpus. To build our corpus, we chose to model the metadata of museum collections in the form of a knowledge graph, in the Semantic Web paradigm, which promotes links between data and is easily augmented by the arrival of new data. A crucible of IT heritage, our Knowledge Graph was developed using the Wikibase environment, after collecting and harmonizing the museums’ metadata (native XML, PDF, etc. formats, free fields) and then converting it to CSV, RDF and FAIR standards so that it could be opened up and linked on the web (Linked Open Data, LOD). The Knowledge Graph is structured by a first-level ontology based on CIDOC-CRM (International Committee for Documentation of the International Council of Museums, Conceptual Reference Model) and the EDM (Europeana Data Model) on the Protégé environment.
The Knowledge Graph currently contains more than 25,500 artifacts and is set to grow. It perpetuates and opens up the IT heritage, allowing it to be exploited, and above all it ofers us a choice textual corpus, very representative of the new corpora that terminologists are facing today, resulting from Big Data. This multilingual corpus (French, English, German, and Italian) is the basis for a study of the discourses that represent IT knowledge, which we will describe in detail. With the help of experts in the field (Pr Marie Gevers from NAM-IP and Xavier Heron from ACONIT), we are embarking an onto-terminological work through the construction of a quadrilingual (FR, EN, IT, GE – by now, the corpus has been explored for French and English) and multimedia dictionary, and an ontology of IT field, based upon the founding models of terminological work (ISO standards 5078, 704 and 1087).
If we focus on language, we must take it in all its complexity. It is true that, as Bertrand Russell
notes, in an ideal language there should be only one word for a single object, and any complex
object would be expressed by a combination of words, each for each characteristic of the object
[
A fortiori, when aiming to build a science dictionary, the dissemination of scientific and technical knowledge requires explaining the significance of the names of concepts in a field, i.e. specialist terms. The outreach and didactic purpose, places greater emphasis on the notional dimension, its accuracy, and its deciphering to cover all the knowledge in a field. In essence, they are aimed at a more restricted audience than encyclopedic dictionaries, even if the primary intention is to disseminate knowledge as widely as possible and reach everyone. Their construction involves, in our case, the participation of historians of science and especially of computing. The transmission of scientific and technical concepts to as many people as possible, necessarily requires the integration of a meta-linguistic discourse and textual and semantic strategies, to translate the accumulation of specialized terms into an understandable language.
In terminology, a descriptive strategy will focus on diferentiating notions from linguistic
links, to make the names of concepts, that are opaque to the uninitiated, or even unknown,
readable, and appropriable. The aim here is to bring scientific concepts into the cultural
mainstream [
Particularly well-suited to popularizing science, semantic-syntactic relations lead the work of textual analysis toward the modeling of knowledge. A fortiori, they make it possible to express the practices surrounding the object. Putting its application into context can only be conducive to understanding, even if this description is not suficient for the acquisition of knowledge. The path towards the concept to be understood is thus supported by a description of the object’s properties, i.e. a ”making sense” and a delimitation, with these properties echoing the characteristics of the concept.
But the scale of complexity of a specialist language for the layman varies enormously from
one field to another. In the example cited above, the semantic-syntactic relationships presented
are not enough to provide access to meaning. Specialized terms need to be translated, otherwise,
there is a risk that too many obscure terms will be brought together and accumulate, creating a
barrier for the uninitiated. For example, the typical application ”electromechanical calculation”
can be presented as a ”precursor of computer programming”, which provides the reader with a
reference point. Thus, not all scientific languages are equal in dificulty in understanding their
concepts, and they do not have the same inclusions in everyday language. Specialized language
is not a whole. So many obstacles stand in the way of understanding by the layman: complex
terms (mechanographic workshops with punched cards), eponyms (Turing machine, Moore’s
law), words’ morphologies such as acronyms, complicated by version numbers (IBM 1130),
special characters, etc. However, these signifiers can also serve as echoes of fuzzy knowledge, a
visual and semantic anchor of recognition for the reader. Contemporary science is characterized
not only by its highly technical nature but also by its social roots. Technical objects are at the
heart of our daily lives, all the more so for science such as Information Technologies. Many
terms are an integral part of our knowledge, such as a keyboard or computer mouse, and it is
through their typical application that meaning can be built up towards more complex notions
and names attached to these objects. Terminological complexity is thus found between the
common language and the specialist language. But language uses can also difer within the
same specialty, geographically (diatopy), over time according to technological developments
(diachrony), and between communities of practice (diastracy). For example, a ”first generation
calculator” for the computer amateur (source: Wikipedia), will be referred to as a ”mechanical
calculator” by a computing history expert, or as an ”ancient calculator” by a general history
expert. For the initiated, one of the objects embodying this concept is an ”abacus” (“abaque” in
French), or ”first digital tablet”. For the general public of the 21st century, however, the concept
has a completely diferent connotation, and the term is associated with a modern digital pad
(portable computer), whereas the object, which dates back to ancient times (it has been found
as far as 500 BC), is actually made up of clay balls and tokens, the ”calculi” (lat.). ”These were
used for arithmetic until 7000 BC, and evolved into a device with rows of moving parts, better
known from 500 BC under the name of ”abacus” (“boulier” in French, which distinguishes the
two types of objects in their diachronic evolution, contrary to English). Diferences in language,
particularly among experts, stem from diferent representations of the world and diferent
strategies: ”... we only manipulate reality through the representations we have of it” [12]. In
the field of Information Technologies, we can therefore find very divergent denominations:
those of the general public, the amateur, the expert in the world of technology or heritage,
etc. Museum inventories, for example, use highly controlled language, demonstrating, just as
much as the accumulation of knowledge around artifacts with documentation, a mastery of the
subject, setting up curators as credible guardians of an almost sacralized heritage. The ”Baby”
for the amateur is called by museums ”Manchester Baby” because of where it was built, or
”Manchester Small-Scale Experimental Machine” (London Science Museum) in reference to its
history: the ”Small-Scale Experimental Machine” (SSEM) was the world’s first von Neumann
architecture machine. Built at Manchester’s Victoria University by Frederic Calland Williams,
Tom Kilburn, and Geof Tootill in 1948, it is what is known to the general public as the first
”computer”. According to an expert in the history of computing, mainstream ”computers” are
”stored-program computers, in the strict sense of the term, i.e. von Neumann-type computers,
which have a very precise meaning: they are electronic calculating machines with a central
memory large enough to hold the program being executed, as well as the data”. We are therefore
faced with highly diversified linguistic uses, which can only lead to a blurring of concepts
and names. On the one hand, there is the everyday language, and on the other, there are the
specialist languages, which do not have the same status and are based on extralinguistic realities
shared by sub-communities in the same field. In these languages, words can have a diferent
linguistic weight, for example, when they are themselves the name of a concept [13]. In this
inclusive relationship between everyday language and its specialized subsets, words are the
only tangible elements available to us to capture the representations to which they refer. We
naturally find them in texts that can serve as a basis for terminology work. Linguistic analysis of
corpora enables us to extract syntagms that can be linked together by lexical relations. Lexical
gender and partitive relationships are essential to the acquisition of new knowledge, and ISO 704
identifies them as the foundation of terminology work. Hyperonymy, hyponymy, meronymy,
antonymy, or isonymy, efectively situate the new term in a notional context. Hyperonymy in a
genus/species relationship, hyponymy in class descriptions, antonymy, and especially isonymy,
in notional diferentiation. ”To determine the meaning of a unit, the reader needs contrasts, and
the canonical definition provides only one mode of category construction, that of specialization
with the genus, defining it” [
The study of computer terminology is undoubtedly one of the fields that has most interested terminologists, who have delved into its morphological and semantic aspects (among others, see [15]), its uses in specialized discourse and its textual dimension, with particular reference to corpus linguistics (for an initial fondamental study, see Condamines, 2005), and issues related to interlingual comparison (among others, [16]). Particularly interesting in our perspective is L’Homme, 2008[17], comparing terminological, ontological and general resources in describing computing terminology1. The ITinHeritage project is heritage-based, deeply interdisciplinary in nature, and the dialogue between disciplines presupposes an interrogation of fundamental notions such as term and concept from diferent points of view - namely, from a knowledgebased approach and from a linguistic-based one. As L’Homme states, ”Given the diferences between the assumption of lexical-based and knowledge-based approaches and the principle on which they rely, the question is whether they can be used simultaneously in terminology work” [18]. In this perspective, by its focus on heritage-based, interdisciplinary vision, the ITinHeritage project can be considered as an attempt to find an answer to this question. The main issue that interests us at this stage of the project is the identification of terminological units and lexical relations, necessary to structure domain knowledge ; thus, our analysis stands in continuity with many studies in the field of terminology, which question the interface between terminology and knowledge-based approaches [19][20][21][22].
In a first phase, we postulate that the study of the linguistic uses [ 23] in the IT heritage field, will enable us to identify names of concepts and their significations, socially stabilized within the various communities that found this domain, by identifying the relationships mentioned above. This detailed analysis will ultimately enable us to construct a dialectic between experts, science, and the public, and to observe the joint evolution of language and technology [24]. As the methodology in textual terminology is based on the triptych specialized corpus - experts - digital tools [25], our terminological work begins with a semasiological approach, through lexical extraction from museum metadata and linguistic analysis of our corpus. The corpus, composed by the collection of metadata from museums involved in the project, is built following the text selection criteria ISO/DIS 5078:2023. It is precisely from these metadata that a first terminological extraction, always based on the regulatory criteria of ISO 5078, is currently carried out. Our extraction approach, which will be applied to all languages included in the project, can be considered semi-automatic, comprising an initial automatic selection by extraction tools (Termostat and Sketch Engine) and a subsequent manual intervention for lexical cleaning. We have relied on a predominantly hybrid approach, combining statistical techniques initially and then linguistic ones. We will have the opportunity to analyze these techniques in detail with respective examples. Starting with the French corpus, whose data come from the ACONIT museum, the following fields were taken into consideration: description, name, model, and use. In this corpus, Description and Use are the only two fields composed of multiple textual 1L’Homme (2008), made a lexical extraction work regarding the computing field. She extracted the 75 most frequent candidate terms from a corpus of specialized resources in the field of computer science using TermoStat. She demonstrated how, in terms of formal coverage, the resource that exhibited a greater presence of these terms, and therefore more completeness, is WordNet, a general-purpose lexical database, compared to domain-specific online dictionaries and ontologies. These latter resources even yielded difering results among themselves. sentences, while name and model can be defined as made up by a sort of controlled vocabulary.
For the first two fields, it was necessary to rely on a strategy of automatic terminological extraction. Various tools were tested and compared, including Sketch Engine [26], TermoStat [27], and we also relied on the NLTK package of the Python programming language [28] as far as programming languages are concerned, they are now considered relevant alternatives to traditional extraction tools when facing corpora derived from big data [29]. However, after a ifrst attempt, in the initial extraction phase done by Termostat as well as with the NLKT library, we encountered a high level of noise, which would have required an excessively demanding manual intervention. Finally, Sketch Engine, a tool for textual management and analysis for extraction, proved to be the best option. The comparison corpus is the French Web Corpus 2023 (frTenTen23), composed of texts collected from the internet, totaling approximately 24 billion words in the French language. The corpus annotates words with POS tags using the FreeLing tool. This comparison corpus belongs to the TenTen corpus family, extending across 40 diferent languages (Jakubíček, M., Kilgarrif, A., Kovář, V., Rychlý, P., Suchomel, V., 2013). Thanks to the TenTen corpora, we can use the same procedure applied to the French corpus for other languages as well. For the terminological extraction by the Sketch Engine function keywords, the selection filter was set to a maximum of 5000 items, considering with particular attention the MULTI-WORD TERMS results. The first SINGLE-WORD terms obtained, such as micro-ordinateur, disquette, macintosh, microprocesseur, azerty, hewlett-packard, powerbook, olivetti, alphanumérique can in many cases be considered quite generic. We decided to focus on multi-word terms which seemed to be the most frequent terminological pattern in IT. Regarding linguistic analysis of the extracted terms, the TermoStat software was initially preferred: simple and complex terms were extracted by selecting lexical categories such as nouns, adjectives, and verbs. TermoStat uses a method of contrast between specialized and non-specialized corpora to identify terms. Those of our greater interest are terms with the highest specificity score, with a lower frequency in the general reference corpus, but more representative in the specialized domain. TermoStat has some limitations: it is not possible, for example, to extract acronyms or terms beginning with an uppercase letter followed by a series of numbers. Extraction tools tend to overlook these patterns, while they represent a major terminological source in the IT industry. In most cases, these are names of machines and their models or manufacturers - e.g., Gamma 30, IBM 1130, …) and in this case, regular expressions and the Corpus Query Language (CQL) of the Concordance section of Sketch Engine can be used, allowing the analysis of words, sentences, documents in various possible contexts, extracting and analyzing more complex grammatical patterns. However, a first analysis provide some useful insight about the morphological patterns of candidate terms, as shown below:
Some preliminary observations can be made, on the basis of this first linguistic analysis. The description and the use fields present diferent characteristics as for the identified candidate terms. The description field focuses on machines and their components, as shown by the 10 most frequent candidate terms (classified on the basis of their specificity score):
Verbs, which make up an interesting percentage of this sub-corpus, are very often markers of meronymic relations [30], as in the case of posséder (132 occurrences), contenir (101 occurrences), comprendre (90 occurrences), comporter (30 occurrences). The markers often indicate references to the composition of the machines or their technical functioning. In the case of adjectives, they mostly refer to the shape or functions of the machines. For example, ’clavier,’ the most frequent noun among the candidates provided by TermoStat (Frequency 509, Score (Specificity) 269.89), is associated with adjectives such as alphanumérique (153.97), numérique (44.83). Whereas the Description field involves descriptive museum mediation centered on the components of the object, the Use field involves narrative mediation strategy. It reveals a more complex structure, where the terminological extraction focuses not only on artifacts, but also on practices and actors involved in IT history, as suggested by the presence of the verb utiliser and of the nouns utilisateur et utilization. Verbs in this section are more often focused on relating specific actions or processes (permettre 471 occurrences, pouvoir 308 occurrences, servir 123 occurrences, agir 72 occurrences, fonctionner 60 occurrences), but we can also find verbal conceptual relation markers such as appartenir (9 occurrences), whose related terms highlight meronymic relationships: appartenir-lignée (73.17); appartenir-famille (56.93); appartenir-microordinateur (48.81). Moving to the English language corpus, made up of metadata provided by the London Science Museum, the fields for lexical extraction taken into consideration are the same as for the French corpus in order to obtain a reliable comparative study. Now we can make an initial comparison between corpora in diferent languages. For example, the results of the Description field extraction show that this field most focuses on artifacts and concrete objects contextualized within a history or practice, and in this respect is similar to the mediation strategy observed in the Use field of the French corpus. Moving to another museum and another culture changes the relationship with the object and the representations of the world to which it belongs. Here, the morphological pattern Adj+Noun is more productive in this field, with examples such as personal computer (110 occurrences), video game (84 occurrences), electronic calculator (53 occurrences), electronic component (41 occurrences). The most frequent verbs focus on concrete actions: manufacture (195 occurrences), build (120 occurrences), work (101 occurrences), even if we can also find an important number of occurrences for verbs such as include (51 occurrences), which can be considered as markers of meronymic relations.
This semasiological approach results in lexicons that reflect the initial corpus and highlight rather than resolve the linguistic variety of a domain. It is not yet a consensual conceptualization. But if we are interested in the way experts name their domain, we are interested in the way they conceptualize it. However, our experience has shown us how dificult it is to draw up a semantic network solely on the basis of this semasiological work [31]. It poses the problem of finding only the variety of names of domain concepts in the texts, and not the concepts themselves. This type of study, although essential for laying the foundations of terminology work, stops at the meaning of words observed in discourse, defined according to their uses. To compensate for linguistic variations and bring out a common meaning, it is advisable to take an interest in the extralinguistic part of terminology work, centered on the relationship of the concept to the object. The meaning of a word is intended to be independent of its uses and is defined as a meaning that is consensually standardized within a community, referring to a conceptualization of the world. The meaning of a word is ”signification actualized in discourse” [12]. Thus, ”it should be remembered that all terminological work should be based on concepts and not on terms” (Felber, 1984). Terms are of interest to the terminologist because they denote a concept, a kind of bijective form, a bridge, reflecting the continuous dynamic between the linguistic part of the real world and the extralinguistic part of the symbolic one. The notional (or conceptual) world is the translation of how we apprehend objects in the real world. The standardization required by terminology work makes it possible to fix this consensual notional system within a community of practice. In order to take into account the specificities and variations of the linguistic system, the notional system is more easily constructed using an onomasiological approach. In this formalization based on the convention in the Latin sense of foedus, the names of concepts must be situated within the notional system as a whole, even if this means artificializing them to provide a signification that goes beyond usage. Structuring concepts according to subsumption and diference relations enables us to build a standardized vocabulary that blurs the varieties and ambiguities of natural language. The linguistic relations mentioned above and observed in our corpus, above all, the genus and the merological relation, enable a detailed description of objects in that they provide the notional environment (specific features or constituents of an object). In this way, ““ontological relations” are defined as “indirect relationships between the notions”, the most important of which is the merological (partitive) relationship” [12]. So, objects are defined in the sensible or intellectual reality, according to the properties that the experience of empirical practice forms for them in the real world, to which the linguistic stratum corresponds. By abstraction, these notions (concepts) reflect and are organized according to these properties in the meta-linguistic stratum (the symbolic world), which are translated into a set of characteristics derived from the reason. The organization of concepts to each other within the notional system therefore requires us to question the very essence of things, their eidetic characteristics.
At this point, the help of experts in the field is mandatory. They have a socio-linguistic
responsibility in the transfer of knowledge because they are the only ones to master the discourse
of the field, its conceptual representation, and its consensus [ 11]. Our work therefore is now to
model this knowledge using an ontology that reflects the conceptualization of the IT world. The
construction of the IT domain’s onto-terminology is facilitated by the use of both the Protégé
environment for the structuration of the knowledge graph, and the TEDI AI tool [
In this way, our terminological work is supported by the Computer Science Ontology Classifier (CSO) [33], a tool originally developed to automatically categorize Computer Sciences research papers, based on abstracts, into a comprehensive semantical network, especially the gender and partitive relationship and which we are founding our terminological work. We decided to adapt this advanced AI-driven tool to our specific research needs, employing it to analyze particular descriptive fields, first within our dataset, secondly on web datasets, such as Wiktionary and BDPedia. It will aim us to detect neo-terms relating to new objects in the IT field and allow us to fit these neo-terms into our ontology. This adaptation involves a dive into the classifier’s operational mechanics, necessitating modifications to accommodate our unique ontology, which diverges from the broad Computer Sciences focus of the CSO. A key advantage in this adaptation process is the close alignment between our research domain and the original CSO corpus, allowing us to utilize the existing word to vec embedding model [34] which was trained on 4.6 millions English papers in the field of Computer Science and align to the corpus we are using. We remain open to exploring the potential benefits of retraining these embeddings in the future, to see if such refinements could enhance the classifier’s accuracy and sensitivity to the nuances of our domain.
Finally, because our work would be useless if inaccessible to the public, we are building a web portal to provide access to the Knowledge Graph. Other platforms federate museum collections [35][36][37] or safeguard software [ 38], but these projects enhance objects, whereas we aim to enhance knowledge. The platform, designed as a place for transferring knowledge, will ofer: navigation via a time map to represent the technological evolution across history; a graph based upon the domain ontology to provide input to the knowledge graph via concepts representing domain knowledge; knowledge completion via the multilingual dictionary; and natural language querying. Querying the Knowledge Graph (RDF) in natural language requires SPARQL (SPARQL Protocol and RDF Query Language) queries. This approach significantly lowers the barrier to entry for users unfamiliar with technical query languages, making the data within our Knowledge Graph accessible to a broader audience. So, here again, we must mobilize NLP, through question-answering (QA) technologies, with IA/IHM tools. SparNatural is a well-known open-source tool available for querying by non-experts [39], which can be used to navigate our knowledge graph based on the domain ontology. Comparatively, the QAnswer tool [40] is end-user-oriented and automatically translates natural language queries into SPARQL. It is intuitive and also learns from user feedback. We are currently assessing the suitability of these two tools for our needs.
The Information Technologies heritage is defined by the intrinsic tension between ”objects languages - representations” [41]. Technologies, terminologies, and conceptualizations in the ifeld are constantly evolving and interrelated. Our work aims to highlight and analyze these developments. It is the starting point for a better understanding of this recent and proliferating ifeld, and of its heritage and dissemination, which are of prime importance, as it bears witness to our contemporary era and its digital and societal transformation. In this sense, the ITinHeritage research project is conducting this onto-terminological work as an anchor point for a wider epistemological reflection to come, on the question of IT, placing at the heart of the question of this heritage, the history of a societal mutation and the emergence of new knowledge in terms of representations of the world, through the definition of this heritage and the new places of knowledge and practices. For this purpose, our multi-disciplinary team is committed to harnessing IT for its own benefit and, in its own image, combining approaches from the social sciences and humanities and the computer sciences. The new heritages and corpuses formed by data are leading us towards new approaches to terminology work. In doing so, we hope to provide an epistemological reflection not only on the subject of our study but also on the tools and methods related to language (linguistics and computer science).
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