The growing demand for Cultural Heritage fruition is making it a major economic driver, also in connection with its tourism-related aspects. The current solutions available on the Web are still unable to provide satisfactory support to the various kinds of stakeholders and to the diferent applications. The History of Computing, as a peculiar, relevant and currently underinvestigated branch of Cultural Heritage, raises additional challenges and provides new opportunities, also in connection with a significant economic flow it generates. The variety and complexity of issues connected to this domain call for even more advanced solutions. In this paper we introduce the CHIPS&BITS project, which tackles these problems using a knowledge-based approach and leverages a novel framework that can meet the needs associated to this specific domain in a better way compared to standard Semantic Web approaches. We describe the contributions of the project to overcome the limitations of the state of the art, and focus on some of its more peculiar and original features, among which the definition of suitable ontologies to describe the complexity of the domain and the use of Artificial Intelligence algorithms for giving the users an advanced and personalized experience.
The demand for Cultural Heritage (CH) fruition is becoming larger and larger in the last years, making it a major component in the economic balance of Nations and territories. Not only it generates direct revenues through the fees for accessing cultural items; it also produces wealth at large considering the economic value for associated tourism, thanks to the travel, accommodation and subsistence expenditure the interested people incur. It often has additional connections to the local art, traditions and folklore, that the tourists like to enjoy once they are in the destination places. All this business, each aspect of which poses its own issues and complexities, is nowadays supported by computer applications, but only at a very superficial level: diferent platforms must be used to book travel, accommodation, meals and tickets, and the various aspects and items are handled independently of each other, while from the tourist’s perspective there is one overall and overarching experience in which every piece is strictly connected to all the others, determining them and being in turn afected from them.
So, it is fundamental to tackle this landscape in a holistic way, in order to provide the final user with an approach that resembles more closely his perspective and can help him in a more efective way, also boosting the associated economic value as a side efect. This objective requires connecting, combining and integrating all the pieces of information from the diferent aspects, which is what we call ‘knowledge’. Hence, our knowledge-based approach to the task. Specifically, we propose a Knowledge
Graph for storing and handling all of the above information, based on semantic technologies that ensure interoperability between diferent systems and platforms, make the procedures and results personalized and understandable to the final users, and enable the application of advanced Artificial Intelligence (AI) solutions to overcome the limitations of the current solutions. In particular, we propose the use of a framework we are currently developing that can support the needs of this application better with respect to standard Semantic Web solutions. Some examples are the following: • the full LOD perspective is not very suitable for preserving privacy (e.g., in auction, rental or exchange platforms); in CHIPS&BITS, the items owned by the users, their selling price and other sensitive information is not to be disclosed unless the users give permission. • Suppose user owns device , then sells it and then buys it again. This requires to set two instances of relationship owned between and , having each the initial and final date of ownership as attributes. This is not immediately feasible using the RDF representation. • Storing the knowledge on one DB enables the use of analysis, mining, and reasoning algorithms that would not be applicable on the ‘unbounded’ data organization of the Semantic Web. Still, the knowledge in CHIPS&BITS can interoperate with the knowledge stored in the Semantic Web.
The specific branch of CH we approach in this paper is the history of computing, due to cultural, technological and economic motivations. Our eforts in this direction resulted in the starting of CHIPS&BITS (acronym of ‘Computing Heritage Intelligent Platform for Searching & Browsing Information Technology and Science’), a self-funded project aimed at developing a knowledge-based platform for the history of computing, and advanced/innovative Web-based tools to allow its users to efectively use and experience, search and discover its content, and generate value from it, dealing with the complexity of the subject matter. The objective is manyfold: spreading awareness of, and supporting education on, such important and still mostly ignored branch of CH; collecting and preserving knowledge and materials that are at stake of being lost forever, and supporting all the diferent stakeholders interested in this topic in their activities, including those generating economic value.
CHIPS&BITS proposes innovative solutions and tools for all the above problems. To the best of our knowledge, it would be the first platform on the history of computing: • where stakeholders can put, interrelate, exchange, search information on the history of computing both from a scholarly perspective and for fostering its fruition in a touristic perspective, possibly generating money exchange and job opportunities as a side efect; • featuring a unified and coherent knowledge base and repository collecting information and documents on the history of computing; • featuring a data schema/ontology that can describe this field in all of its complexity, seamlessly dealing with the diferent facets of computing: hardware, software, documentation, intangible aspects, economic exploitation; • exploiting advanced AI solutions to ensure more comprehensive, accurate, diverse and personalized results to its users.
Since the proposed approach is general, it may act as a pilot for other branches of CH.
This paper is organized as follows. After discussing the motivations and contributions of this work (Section 2), the ontology for the history of computing is presented in Section 3. Then, Section 4 briefly describes the features and interactive interface of GraphBRAIN, and its current content. Finally, after discussing some related work (Section 5), Section 6 concludes the paper and outlines future work issues.
The CHIPS&BITS project aims at contributing under several aspects, as described in the following sections. We summarized the motivations and the specific key points for each aspect in Table 1.
While Computer Science and Engineering are quite young areas in the landscape of human knowledge,
their incredibly rapid advances in the last decades, and the relevance computers gained in every aspect
of our lives, recently raised significant interest in the study of the history of computing and in the
preservation of knowledge and artifacts related to it. So, computing is now not just a means to support
and foster all activities related to CH (known as Digital Cultural Heritage) [
Leveraging the enthusiasm of practitioners in this field, CHIPS&BITS provides a (set of) tool(s) for collaborative building and enrichment of a knowledge base covering all aspects of the history of computing from personal, scientific, industrial sources. In line with the EU idea of Open/Citizen Science1, the idea is that putting together all the ‘partial’ pieces, the final picture will be more complete, more understandable and, ultimately, more valuable than the simple sum of the pieces. Every new piece of information opens new knowledge paths, supporting new ways for discovering information and completeness, accuracy and diversity of the search results. Also, comparing diferent sources it can spot controversial cases and support trustworthiness. 1https://www.fosteropenscience.eu/foster-taxonomy/open-science-definition
The production of computing devices is much more complex than other kinds of manufactures. First of all,
it inextricably connects to each other aspects that are usually in the realm of diferent communities, using
diferent platforms and standards: hardware (museums), software (Internet repositories), documentation
(libraries and archives), and intangible aspects (people). Indeed, hardware is just dead matter if software
does not give it life, and software is meaningless and useless if detached from the hardware platform it is
intended for; both require documentation to understand and properly run them; finally, why hardware,
software and documents are as they are can often be understood only in connection with events, customs,
anecdotes, etc. that (sometimes fortuitously) determined a course of history rather than another. The
development of these objects involves many contributors, at diferent levels and with diferent roles.
Even considering hardware and software only, complexity is taken to the extreme, and stands apart
from all other CH items (see [
The current cataloguing and description standards proposed for CH items, even those specifically developed for technological items, are totally unable to handle all this complexity. Hence, a pressing need for the definition of a specific new scheme, to be shared and reused by all the stakeholders involved in this area of interest. However, this is a challenging task, because there is no standard, nor precise categorization taxonomy for computing elements, which makes it dificult to define the classes, assign them stable attributes, and organizing them into a hierarchy.
In this landscape, CHIPS&BITS contributes by adopting a knowledge-based approach and defining a ‘holistic’ ontology, to be used as a data schema, bringing together all these aspects.
There is a lively market for vintage computing elements and memorabilia, with exchanges, sales and auctions ongoing all over the world. Some items were sold for values similar to those of works of art by famous artists (e.g., one of the surviving units of Apple I, the first computer developed by Apple, was sold for $ 375,000; one of the prototypes of Commodore 65 — a computer which was never released —, endowed with a — probably unique — expansion board, was sold for about $ 95,000; some of the first models of personal computers, produced in thousands units, are sold for several thousand dollars, and so on). This market relies on standard, non-domain-aware platforms, providing the users (sellers and buyers) absolutely no support to understand and put in perspective the items they are exchanging.
Dozens crowdfunding campaigns have been successfully run aimed at reproducing old computers (or building new ones based on obsolete technology), developing new software for vintage machines, writing books on the history of computing, producing films and documentaries, establishing museums, etc. The number of supporters is in the order of thousand people, and the money collected for each of these campaigns is typically in the order of tens of thousands of euros, often ending up in the hundreds of thousands. Also, many kinds of events (conferences, seminars, shows) on ‘vintage computing’ have been organized, attracting scholars, researchers, amateurs, collectors or other kinds of enthusiasts. This generates tourism, and associated economic flow, as a side efect. Finally, additional economic flow is associated to job opportunities in the form of rental of items, service or restoration, consultancies, and education.
CHIPS&BITS aims at supporting all these activities by providing the stored knowledge to the various kinds of stakeholders involved, making them more aware, boosting the connections and exchanges, and helping to match requests and ofers, all in a personalized way.
Supporting the previous needs also poses technological challenges that require new solutions to overcome the limitations of the current state-of-the-art.
A collaborative approach in which many people, with diferent expertise, culture, background and perspective contribute small pieces that together make up the big picture, requires powerful and shared representation and management solutions to organize the knowledge.
CHIPS&BITS adopts a knowledge-based approach based on ontologies as schemas for representing and interconnecting all the diferent kinds of data. The integration of diferent ontologies describing the diferent aspects allows to deal with the complexity and peculiarities of the field. A valuable result by itself, it enforces qualitative approaches, more focused on concepts than on statistics. The ontology itself will be searchable knowledge, useful for understanding the domain. Since CH data may have Intellectual Property-related restrictions, and their economic exploitation may involve private or sensitive information, CHIPS&BITS guarantees data protection, overcoming some limitations of both the collaborative approach and the LOD perspective, while still supporting systems’ interoperability. This is obtained by the use of GraphBRAIN, a framework that is based on a proprietary DB to store the data, so that the data owners may decide what, when, how and to whom can be disclosed. The portion meant to be public can be published as LOD, and transparently be perceived as such by standard Semantic Web technologies. In particular, GraphBRAIN was extended to add flexibility to the data visibility settings, by which the data owner may decide at several levels of granularity who is guaranteed access to each kind of information stored in the knowledge base. Finally, the fruition of such a varied and complex knowledge to serve the needs of diferent kinds of stakeholders and applications, including research, hobby, trade, and education, requires the system to ensure user-friendliness, personalization, diversity, accuracy, and comprehensiveness in knowledge delivery.
CHIPS&BITS uses advanced AI solutions based on Natural Language Processing (e.g., for expressing in natural language the formal knowledge extracted from the graph), Document Image Analysis (to auatomatically extract knowledge from existing documents), Knowledge Representation and Reasoning (to obtain implicit information in an explainable way), Network Analysis (to understand the relevance and possible connection of knowledge items), and Machine Learning/Data Mining (to learn new knowledge from the knowledge base or profiles from users’ behaviors) to extract and deliver relevant knowledge by fulfilling all of these requirements. The use of qualitative approaches (not afected by frequency and explainable), in addition to quantitative ones, enforces diversity in the outcomes and trustworthiness of the system and of its outcomes.
This section provides details about some of the technical solutions embedded in CHIPS&BITS. A graphical overview of the framework is shown in Figure 1.
GraphBRAIN is a general-purpose knowledge base management system aimed at covering all stages
and tasks in the lifecycle of a knowledge base, including knowledge acquisition, organization, and
(personalized) fruition2. It adopts the Labeled Property Graph (LPG) data model, where nodes (representing
individuals) and arcs (representing relationships) may have labels (usually expressing their type) and
associated attribute-value pairs, and uses the Neo4j [
GraphBRAIN provides a top-level ontology defining very general and highly reusable concepts and relationships (e.g., Person, Place; Person.wasIn.Place; etc.). This top-level ontology plays a crucial role to interconnect the domain-specific ontologies, ensuring an overall connected knowledge graph. Using a suitable tool, GraphBRAIN administrators may create, build and maintain additional ontologies.
After setting up the ontologies, information can be fed into the knowledge base. A form-based interface allows users to manually insert/update/remove instances or to query the knowledge base for instances of entities and relationships: they must select one of the available domains/schemas, and the forms are automatically generated by the system starting from the corresponding ontologies. The knowledge base can also be fed by automatic knowledge extraction from documents and other kinds of resources (e.g., books or the Internet). Instances may also have attachments, making GraphBRAIN a digital library, whose content is organized according to formal ontologies, fostering interoperability with other systems. Users may add, show, or delete attachments.
In a collaborative spirit, users may add comments on (to provide suggestions or add information), or approve/disapprove, each entity or relationship instance, each single attribute value thereof, and even the ontological items. This feedback is used to assign a trust value to the users.
Another interface allows users to display a portion of the graph, browse it interactively and display detailed information about entity and relationship instances. This allows the user to continue his search in a less structured way, by exploring the graph (expanding or compressing node neighbors) with a predefined goal in mind, or letting the data themselves drive the search, possibly finding relevant information in a serendipitous way.
The users can also run on the available knowledge several algorithms for graph mining, network analysis, information extraction, automated reasoning, natural language processing, etc. Some of these algorithms are reused from the literature; others are original. Currently included functions are: • assess relevance of nodes and arcs in the graph, and extract the most relevant ones; • extract a portion of the graph that is relevant to some specified starting nodes; • extract frequent patterns and associated sub-graphs; • predict possible links between nodes; • recommend relevant knowledge items; • translate into natural language the information content of a portion of the graph. If available, a user profile can be used to personalize the results of all these algorithms. This would ensure that each user obtains tailored information, which is another novelty introduced by GraphBRAIN.
While a proprietary technology, all the functions of the GraphBRAIN framework are exposed as services of an API whose code will be released for free use, so that any third-party application and organization will be able to exploit it.
Currently the CHIPS&BITS knowledge graph is organized according to a merger of the following
ontologies:
general dealing with very general concepts and relationships that are expected to be present in almost
all domains; of interest to CHIPS&BITS are, e.g., entities Agent (including persons, organizations
and users), Place (geographical, administrative, locations, etc.), Event (both historical ones
and conferences, fairs, shows, etc.), Collection (any kind of grouping), Document (in its most
general definition as “something that serves as evidence or proof”, and thus not limited to printed
(or printable) documents), IntellectualWork (the original result of an intellectual efort, such
as inventions, subjects, works of art, etc.), Award (any kind of recognition that can be awarded
to, or record that can be marked by, persons, companies, devices, documents, or components: e.g.,
Education achievements, Prizes, Records), Item (a specific, identifiable specimen of a serially
produced object).
lam concerning libraries, archives and museums; it includes and is aligned to the standard data models
from IFLA (the International Federation of Library Associations) [
A sample of domain-specific classes from the computing ontology is the following: • ElectronicComponent: a part, useful or needed to build a Device but not operable directly by the final user (e.g., Printed Circuit Boards, Integrated Circuits). • Device: a manufact that can be operated directly from the final user (it includes Calculators, several kinds of Computers, Input and Output Devices, StorageDevices, etc.). • Software (of various kinds: Development, Educational, Embedded, Ofice Automation, Operating
Systems, Videogames, . . . ). • Package: a specific packaging of a Device or of a set of devices sold together (often very relevant for collectors); • System: a group of Devices that is functional only as a whole; it difers from a Configuration in that, in a Configuration , at least one of the Devices would be functional if taken alone. • Configuration: a relevant group of Devices, relevant because typical or determined in order to satisfy specific needs (e.g., a configuration of devices for desktop publishing); Also useful are the concepts of Award (that may be useful to note the firsts in the history of computing), IntellectualWork (extended to account for algorithms, approaches, inventions, programming languages, technologies, theorems, theoretical models), Item (extended from just documents to components, devices, software, etc.), borrowed from the other ontologies.
A sample of domain-specific relationships are Agent.acquired.Item (including donations and purchases), clones compatibleWith and mayReplace (between pairs of components, devices or software), owned (for agents who owned organizations, but also to record notable previous owners of some items) Component.partOf.{Item,Device} (some rare parts may dramatically change the value of an item or device), evolves (for hardware and software versions), requires (software may require another software or a device to work properly), repaired (to record who is able to repair what). Some of these are relevant for the economic aspects, others are relevant for repairing and restoration, which is extremely important because computing devices has peculiarities that cannot be expressed in existing ontologies designed for other kinds of CH.
The proposed conceptualization may be a starting point for the definition of cataloguing standards
for cultural heritage material related to the history of computing. Indeed, existing standards for cultural
heritage, even those developed for technological and scientific elements (e.g., [
Personalization and Privacy are handled in CHIPS&BITS with a system of registered users and with the help of a (relational) database, which was inherited and extended from the GraphBRAIN framework.
The user profile takes the form of a set of weights, associated to the ontological elements (entity, relationship or attribute) or to specific nodes or arcs (i.e., entity or relationship instances). The weight is formed and continuously updated by taking into account both explicit preference indications by the users and implicit preferences computed on usage. Many kinds of user interaction contribute to the latter part: selection of an entity or relationship, access to a specific node, arc or attribute (to display its value or to add, modify or delete it), approval/disapproval or comment on a node, arc or attribute value. These parameters are stored in 3 tables of the relational DB: Preferences for explicit preferences, Interactions for visualization and CRUD operations, Evaluations for approvals/disapprovals and comments. The profile is used to highlight the user changes in the knowledge associated to his preferred elements, or to drive knowledge extraction and graph mining algorithms so that they can focus more on his preferred elements and less on the others.
While designed according to the linked data perspective, the GraphBRAIN knowledge graph is not available in its entirety as Linked Open Data. Indeed, it is not directly accessible to the public. Access is available only through the query, graph browsing and knowledge extraction facilities provided by an API, that any interested application must use. Each function in the API, exposed as service, returns relevant portions of the graph based on the input parameters, on the user’s profile (if available), and on the privacy specifications for the various knowledge items. Privacy is obtained by adding to each ontological element a privacy attribute, which can be set to True or False. When True, the owner of a knowledge item (an entity or relationship instance, i.e., a node or arc in the graph) can set its privacy value to one of the values Private (visible only to its owner), Restricted (only selected users can access it), or Public (visible to all users and publishable as open data). This information is stored in a Privacy table in the relational DB. When Restricted, another table Permission in the DB reports, for each node or arc as a whole, or for its labels and attributes, which users may access it, specifying for each of them if they can see, add, modify or delete it (in case of a node or of an arc) or its value (in the case of an attribute).
CHIPS&BITS currently involves 11 ontologies, describing various contextual or application domains related to the history of computing (general, libraries/archives/museums, education, tourism). The dependencies among such ontologies are graphically shown in Figure 2, where lower-level ones import the concepts from the higher-level ones. The figure shows that ontologies purposely defined in GraphBRAIN all import the ‘general’ ontology, while ontologies obtained by translating existing schemes are stand-alone. However, a connection still exists between these classes and the other ones, because in the translation an alignment was carried out, and the same names were used for the classes referring to the same concept.
Table 2 reports statistics on the number of ontological elements (classes, relationships, attributes) in these ontologies. The number of top classes is reported separately from the number of their subclasses, in order to distinguish the specializations from the core concepts. Since the model underlying GraphBRAIN is an LPG, also relationships may have attributes. Note that in GraphBRAIN one relationship name may represent many specific relationships, distinguished by diferent subject-object pairs (which would require diferent names in OWL). So, the ‘Relationships’ column reports the number of diferent relationship names followed, in parentheses, by the number of actual relationships. Considering both its own elements and those inherited by the imported ontologies, the core ontology for the history of computing currently includes 267 classes and 540 relationships.
Population of the CHIPS&BITS knowledge graph has already started, based on the current version of the ontology. Concerning the specific domain, the catalogues of a private collection and of the Museum of Computer Science at UniBA are being used as sources of knowledge, entered partly manually and partly using an upload procedure from their catalogues. These two collections were chosen because their owners and maintainers share our holistic view, according to which a seamless integration among hardware, software, documentation and intangible aspects. The private collection includes about 500 units between devices and rare components, plus hundreds of books, magazine issues, videos, software packages and other memorabilia. The Museum of Computer Science has on show a mainframe, several minicomputers and terminals, many personal computers and workstations, plus many items in the deposits and a huge collection of several thousand historical books, magazines and software, a large part of which is still to be catalogued.
Other instances uploaded in the knowledge base concern the contextual part, expressed by the general
ontology. For example, instances of class Place are being uploaded from an atlas. Classes Category
and Word were populated from WordNet [
An automated tool is also under development that can fill entity, relationship and attribute instances by extracting them from the text of books, leveraging Large Language Models technology.
The current content of the CHIPS&BITS knowledge base consists of 347295 entity instances, of which 1858 related to the computing domain and 4822 from the libraries/archives/museum domain, and 641293 relationship instances. The education and tourism sections were started recently, so they include just a few hundred instances. Conversely, the general ontology contributes with a few hundred thousand instances, thanks to the batch ingestion of various resources: the atlas for places, WordNet for words and concepts, and some subject taxonomies.
Let us now report some use cases we ran on the knowledge base. Figure 3 shows a subgraph extracted from the overall knowledge based on starting nodes that are relevant to the user’s information need (shown with thicker borders in the graph and listed on the left-hand side of the interface). The subgraph extraction algorithm is aware of the user’s profile and uses it to drive the extraction. Most of the functions used in the following use cases rely on a preliminary subgraph extraction like this, which is the starting point for carrying out the diferent tasks described.
Use Case 1 User is a collector, with broad interests but a special focus on personal computing and specifically the Commodore brand. He misses a few models from Commodore, and the system knows that another user, , owns two units of one of these models. The system prompts in case he would like to sell one of his units to , or exchange it with one of ’s units. He accepts, and the system establishes a contact between and , still hiding their real identities for privacy reasons, until they decide they can disclose their identities to each other.
Use Case 2 Institution owns a unit of a computer model, but knows almost nothing about it. The system extracts relevant knowledge in the form of a subgraph centered in that model, and returns a natural language description of the model, its history and relevance, and its connections to other models, hardware and software. The system also retrieves other users who own(ed) or use(d) that model, so that they may be contacted for more information. Since the unit is not working, the system also provides information on how to fix it, and points to users who can repair or restore it. It also determines the value of the unit based on previous transactions, and retrieves users potentially interested in purchasing the unit in case the institution is not interested in it.
Use Case 3 Researcher , interested in peripherals, comes to know about a company he was not aware of, that had a role in the history of computing and is relevant to his research. Actually, the system itself pointed to that company, based on ’s interests. The system delivers a report on that company, describing its history, products and achievements, the people who worked for it and their roles and tasks, all from the specific perspective of peripherals (albeit the company may also have produced computers and software).
Use Case 4 Association is organizing an exhibition about a pioneer of computer science and engineering, . The system finds the set of devices and software he developed, the companies he worked for, his history and some anecdotes that might make the exhibition more interesting. It also points to users who own and might rent units of the devices and software developed by , to documents, audios and videos involving (with information on where they deal with ), to other relevant memorabilia, and to previous events about .
Use Case 5 User is interested in the Olivetti brand, and specifically in the firsts that it marked in the computing history. The system identifies the models developed by Olivetti which are landmarks, provides an explanation for the choice, identifies museums or exhibitions where units of these models are on show, and provides a description of touristic points of interests, typical food and folklore of the regions where such units are hosted, taking into account ’s interests and preferences.
Work on Cultural Heritage knowledge representation is more oriented toward data schemas and vocabularies rather than ontologies. However, there are some noteworthy initiatives also about the development of ontologies, and associated knowledge graphs, for CH. A relevant initiative in this direction is supported by the Italian Ministry of Culture: ArCo (Architecture of Knowledge)4, is an ontology for, and a knowledge graph of, Italian CH. It currently reuses, and is aligned to standard CH-related schemes and ontologies:, CIDOC-CRM, EDM, Cultural-ON, and OntoPiA. The resulting knowledge graph includes, and provides as LOD, data from records of the General Catalogue of Cultural Heritage, a database of Italian cultural heritage entities. Like ArCo, CHIPS&BITS organizes several ontologies in a network for modeling diferent kinds of cultural properties and their corresponding catalog records. Some branches of knowledge described by ArCo overlap with those of CHIPS&BITS (e.g., Archive, Catalogue & catalogue records, Cultural events & exhibitions). In principle, any part of ArCo (and of the ontologies it includes) can be reused in CHIPS&BITS, as well as content might be included from many resources such as Geonames, Wikidata, VIAF, etc. Describing in detail what is or might be reused from these and other existing resources in CHIPS&BITS is out of the scope of this paper, where the aim is just to say that this can be done and that interoperability with them is supported.
Related specifically to the computing domain, [
From the technological viewpoint, some works exist that analyze the possibilities for cooperation
between ontologies and graph DBs. [
Computing is starting to be considered par of Cultural Heritage, not only a means, due to the rapid obsolescence and advancements in technology calling for preservation. Luckily, we still have the opportunity to get knowledge from the pioneers who are still alive. The CHIPS&BITS project’s aim is to collect, interrelate, preserve and deliver knowledge on the history of computing. The peculiarities and complexity of the domain are such that all available data models are inadequate for it. We proposed a new data schema in the form of an ontology, that enables the use of advanced AI solutions, and might be the core of a new cataloguing standard. This domain is also generating significant economic flow (due to publishing and educational initiatives, collectors’ exchanges, event organization and job opportunities): CHIPS&BITS aims at leveraging this in the perspective of the Web economy. CHIPS&BITS proposes lfexible solutions to provide tailored behaviors for the variety of potential users and stakeholders involved, and guarantees privacy for sensitive information associated to the economic exploitation. Most of the proposed solutions are general and applicable to any kind of CH.
Population of the knowledge base has started, and other work is currently undergoing or planned in several directions: refining and extending the ontologies, populating the knowledge base, developing systems for automated population of the knowledge base from existing information sources, and the development of tools that can serve the needs of the various kinds of stakeholders. In particular, the design of a platform for providing professional support to enthusiasts and collectors (e.g., e-commerce and auction functions, matching of ofers and requests for items and for competences, etc.).
This research was partially supported by projects CHANGES “Cultural Heritage Active Innovation for Sustainable Society” (PE00000020), Spoke 3 “Digital Libraries, Archives and Philology” and FAIR “Future AI Research” (PE00000013), spoke 6 “Symbiotic AI”, funded by the Italian Ministry of University and Research NRRP initiatives under the NextGenerationEU program.