The article describes I.PaC (Infrastructure and Digital Services for Cultural Heritage), the digital framework designed as a central hub for managing descriptive data and digital objects from cultural institutions at a national level. The paper investigates the use of Artificial Intelligence (AI) within the I.PaC infrastructure to enhance the quality of descriptive data, to add value to digital objects, and to assist users in navigating cultural portals.
The great number of Italian cultural properties presents numerous challenges in terms of accessibility, conserva- I.PaC - Infrastructure and Digital Services for Cultural Hertion, and enhancement of cultural heritage. To address itage [2] - is the data space dedicated to the preservation, these challenges, a dedicated digital infrastructure for management, and valorization of the Italian digital culcultural heritage has been developed with the aim of: tural heritage.
• making cultural heritage accessible to a global au- This digital space collects descriptive data and digital
dience, enabling the discovery of artworks, monu- objects related to Italian cultural properties from archives,
ments, and historical documents from anywhere libraries, museums, and cultural sites across the country.
in the world and improving their accessibility and The comprehensive repository ensures that valuable
culfruition; tural artifacts and their associated metadata are preserved
• encouraging the digitalization of cultural proper- for future generations and made accessible to researchers,
ties, ensuring their preservation for future gener- educators, and the general public.
ations; The services provided by I.PaC are organized into four
main areas: (
For the first three areas, I.PaC is exploring the use of artificial intelligence models to improve, enrich, and extract data. These AI models are designed to enhance the accuItal-IA 2024: 4th National Conference on Artificial Intelligence, organized by CINI, May 29-30, 2024, Naples, Italy * Corresponding author. $ margherita.porena@cultura.gov.it (M. Porena); antonella.negri@cultura.gov.it (A. Negri); luigi.cerullo@cultura.gov.it (L. Cerullo) © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0). pass other types of entities, such as events and literary works. This is essential for ensuring that diferent records referring to the same object are accurately reconciled, maintaining the integrity and eficiency of the I.PaC graph.
Currently, two additional AI applications within I.PaC are being tested: • the development of models aimed at linking identified entities to controlled vocabularies and domain-specific terminological tools that describe cultural properties, • the enrichment of the graph with information extracted from textual contexts. racy and depth of cultural data, promoting continuous For the first aspect, many cultural properties are deevolution in the way cultural information is managed and scribed using unstructured texts that do not refer to stanshared [3]. By leveraging AI, I.PaC aims to facilitate more dardized vocabularies or thesauri, making access to inforeficient data processing, uncover hidden connections be- mation less immediate. The project aims to create models tween cultural entities, and provide users with richer, that link these descriptions to standard categories from more contextualized information about Italy’s cultural controlled vocabularies, despite the challenge posed by heritage. the highly specialized and domain-specific nature of such terminologies. 3. AI applied to descriptive data For the second aspect, the team is working on AI models that extract data from unstructured texts to integrate it into the I.PaC data model in a structured form, thus simplifying the search process and increasing the informative value of the graph.
In the graphs area, one of the main problems is that the data managed by the I.PaC graph comes from various sources, which may assign diferent identifiers to otherwise identical entities. This can lead to an overabundance of entities that, in reality, refer to the same object. A typical example is "Agent" entities (like Leonardo Da Vinci), which is registered in multiple systems with diferent identifiers, creating in this way diferent entities.
To solve this problem, innovative AI algorithms have been employed. These algorithms intelligently analyze the context of each entity, taking into account important details like ates and places of birth, qualifications, and biographical information. By doing so, they can group entities that are nominally diferent but semantically identical, efectively reducing duplication.
In the context of agent reconciliation, AI faces a particularly challenging task due to the often limited descriptive data available. Frequently, the only information provided is the agent’s full name, with no chronological references or additional identifying details. In such cases, the AI must employ advanced techniques to analyze the works associated with the agents. For artworks, the AI can attempt to identify stylistic similarities by examining features such as brushwork, technique, and recurring motifs. For bibliographic works, it can focus on similarities related to the subject matter, comparing themes related to the work. These methods enable the AI to suggest potential matches, overcoming the limitations imposed by the lack of detailed data.
Plans are in place to expand this approach to encom
One of the primary goals of I.PaC is to manage a great number of digital objects that come from various cultural institutions and organizations across the country.
Among the various functionalities ofered, I.PaC is experimenting with a content processing system using a range of artificial intelligence techniques, from Machine Learning to generative models. This initiative aims to achieve two primary objectives: on one hand, to generate new digital content or media; on the other, to extract meaningful information from existing content.
In this initial phase, 7 specific use cases have been
selected to test how AI can enhance digital resources and
enrich the graph. These use cases are:
• (
Additional dificulties include identifying sections
that are physically separate but logically part of
the same article, handling articles that continue
on diferent, often distant, pages of the resource,
and dealing with advertisements that can
physically and logically separate various parts of the
same article.
• (
Specifically, for cadastral maps, the AI must also
recognize the cadastral parcels indicated in the
image. The challenge in this task is that many
ancient and modern maps have handwritten data,
making it dificult to recognize diferent types of
handwriting. Additionally, there is often no
extended textual context available that could help
the AI correct extraction errors using semantic
context.
• (
Over time, numerous paper catalog records have been created to describe cultural heritage items, representing a valuable informational resource that needs to be recovered. In many cases, the only information available about certain cultural heritage items is contained in these paper records.
This use case involves extracting information from these digitized catalog records to map the extracted metadata to the current national information representation models. The challenge here lies in the significant variation in the layouts used in these catalogue records and the difering information each type of records requires. It is not possible to identify a specific layout or consistently recurring data (except for some basic information, such as the catalog number or the classification of the item). Therefore, the technology must be capable of extracting the information, recognizing its semantics, and mapping it to the relevant descriptive data model.
Technologies for the last three use cases have already been successfully tested, demonstrating the feasibility and efectiveness of the proposed solutions. However, in the coming months, these successfully tested technolo- Figure 3: Alphy, the AI-powered generative chatbot for the gies will require fine-tuning to improve performance and Alphabetica portal navigation achieve increasingly precise results. For the other use cases, a proof of concept (PoC) is currently being carried out by two competing companies. Upon completion of knowledge base, making the user experience this phase, the best results will be evaluated, and the more informative and engaging. All information most suitable solution will be selected. The final choice generated by the AI is highlighted in the chat, will consider both the technologies used and the devel- ensuring compliance with current regulations. oped pipeline, which must be capable of processing the resource in an automatic manner, ensuring all required outputs. Human intervention will only be necessary for result validation, thus ensuring an eficient and scalable process for managing cultural heritage resources.
Currently, another chatbot is being developed for navigating the General Catalog of Cultural Heritage [6], which contains data on cultural properties from Italian museum and other cultural institutions. Unlike the first case, this experiment aims to use generative AI to process RDF data, 5. Generative AI to enhance organized according to the ArCo ontology network [7] and accessible through SPARQL queries. The goal is to information retrieval convert natural language questions into SPARQL queries, thus facilitating access to information. In this context, I.PaC provides also services to enhance information re- the generative AI must use Retrieval-Augmented Gentrieval in the form of chatbots that use generative ar- eration (RAG) [8] because it needs to comprehend the tificial intelligence to assist users in navigating portals semantics of the ontology and suggest research paths. dedicated to cultural heritage. This approach allows the AI to provide more accurate
The first project to have been realized, still in pub- and contextually relevant responses by dynamically inlic experimentation, is Alphy, designed with the goal of tegrating and retrieving pertinent information from the assisting users in navigating and accessing information knowledge graph, thereby enhancing the overall user in Alphabetica [5], the portal of Italian libraries created experience in accessing and exploring the vast cultural by the Istituto Centrale per il Catalogo Unico delle Bib- heritage data. lioteche Italiane (ICCU). The application of generative artificial intelligence is crucial in three key phases of the interaction process between the chatbot and the user: 6. Conclusions • user intent interpretation: during this phase, the In conclusion, the development and implementation of AI analyzes the user’s input to accurately identify the I.PaC - Infrastructure and Digital Services for Cultural their intentions; Heritage - represents an important advancement in the • mapping intentions to three search templates: in management and valorization of Italian cultural heritage. this phase, the system guides the user’s inten- Through leveraging cutting-edge artificial intelligence tions towards three key templates: Works, Pro- technologies, from machine learning to generative modtagonists, and Themes; els, I.PaC not only aims to preserve and make accessible • analysis and enrichment of results: in the third cultural properties but also to innovate the way these phase, the chatbot reviews the results obtained treasures are studied and known. The exploration into AIfrom the Alphabetica indexes, enriching the driven enhancements, including descriptive data analysis response with additional information from its and digital object processing, can bridge the gap between historical legacy and modern accessibility. The introduction of AI-powered chatbots like Alphy for navigating cultural portals points out the commitment to enhancing user experience and information retrieval. Thanks to the continuous refinement of AI applications and extension of digital services, I.PaC is a powerful example of how culture, technology, and education come together, ensuring that cultural heritage is not only preserved but made accessible in new ways for generations to come.