Harnessing 'Il Manifesto' Newspaper Archive for Knowledge Base Creation: Techniques and Findings in the 'MeMa' Project
Authors: Robert J. Alexander, Matteo Bartocci, Oriana Persico, Guido Vetere (Isagog S.R.L., Università Guglielmo Marconi, Il Manifesto Soc. Coop., Human Ecosystems Relazioni S.R.L., 2023)
Topic: Lexical and Semantic Resources and Analysis

Abstract
The historical archive of the newspaper il Manifesto is a valuable asset protected by the Italian Ministry of Cultural Heritage. The MeMa project aims to create an 'intelligent archive' using AI principles, fostering collaboration and transparency. The platform, built around Apache Jena and open linguistic technologies, addresses the specific needs of the newspaper community. This paper presents the platform’s architecture, knowledge base construction process, and future directions, emphasizing journalism enhancements through AI while respecting Il Manifesto’s principles. Italiano. L'archivio storico del quotidiano il Manifesto è tutelato dal Ministero dei Beni Culturali. Il progetto MeMa mira a creare un 'archivio intelligente' basato su una intelligenza artificiale che favorisce la collaborazione e la trasparenza. La piattaforma, costruita attorno ad Apache Jena e tecnologie linguistiche aperte, risponde alle esigenze specifiche della comunità del giornale. Questo contributo presenta l'architettura della piattaforma, il processo di costruzione della base di conoscenza e le direzioni future, discutendo il potenziamento del giornalismo attraverso l’intelligenza artificiale nel rispetto dei principi de Il Manifesto.

Introduction
The historical archive of the newspaper il Manifesto is an asset protected by the Italian Ministry of Cultural Heritage as being of particular interest. The archive includes a paper collection starting from 1971 and a digitized collection starting from the 1990s. Now entrusted to the Nuovo Manifesto Società Cooperativa Editrice, which has published the newspaper and its digital editions since 2013, the cooperative is committed to maintaining and improving the archive, as well as ensuring free access and digital consultation facilities for all interested. The digital archive, produced in phases over the years, reflects the historical and technological evolution of the publishing sector. Initially, the database included 10,013 digitized files containing about 160,000 articles, with some gaps in the years 1985-1986 and 1994-2002. Il Manifesto considers an 'intelligent archive' to be the cornerstone of its digital strategy, seeking to align it with new technologies and investing in research and development accordingly. The MeMa (Memoria Manifesta) project began in 2020 in partnership with Salvatore Iaconesi and Oriana Persico, aiming to develop new archive infrastructure based on Artificial Intelligence. This 'Community AI' is built on principles of openness, transparency, collaboration, and non-extractiveness, designed to foster productive relationships between the archive, the editorial staff, user communities, and society. In 2023, when the project was resumed, the new board decided to continue with the original plan, evolving it toward Linked Open Data and leveraging recent advances in language and knowledge technologies. The goal was to create a standards-based Knowledge Graph (KG) using editorial metadata and structured information extracted from article text—a concept previously explored, though unique in its adaptation to the specific workflows and organizational habits of il Manifesto. Commercial platforms, such as Neo4j or Ontotext, offer similar solutions, but success here required a customized platform that aligns with the newspaper community’s processes for producing, organizing, enriching, and experiencing content. Additionally, MeMa, as a sociotechnical platform, encourages user curation and contributions—from readers, archivists, and journalists—to collaboratively evolve the AI, including correcting errors typical of current NLP technologies. Consequently, we began designing a custom platform around an open graph database core, specifically Apache Jena, and incorporating open linguistic technologies suited to Italian. This solution falls within the scope of Enterprise Knowledge Graphs, which are becoming established as rational counterparts to generative linguistic technologies based on neural models. This paper provides an initial account of our analysis, design, and development progress, as well as plans for addressing the socio-technical needs identified thus far. Our contribution serves as a 'reality check' on applying knowledge and language technologies to complex texts produced by an Italian publishing community over more than 40 years. Broadly, our research focuses on the interaction between digital systems and users to ensure transparent and accessible content for diverse user communities. Linguistically, the texts reflect both a specific idiolect and diachronic variations over time. This paper is structured as follows: Section 2 offers an architectural overview of the platform, Section 3 details the knowledge base construction process, and Section 4 discusses challenges and potential future directions. Notably, automatic content generation is not part of the AI-driven journalism enhancements intended by il Manifesto.

Conclusion
Constructing MeMa’s Knowledge Graph (KG) provides a valuable opportunity to assess the current NLP landscape within a real Italian content production setting. The KG will be made accessible later this year through a SPARQL endpoint and a dataset collection. At this stage, our experience highlights both the potential and limitations of NLP technologies when applied to a large corpus of newspaper articles spanning a significant time period, characterized by sophisticated use of the Italian language. In general, structured knowledge extraction can be achieved with various levels of granularity by integrating NLP processors, including named entity recognizers, event recognizers, role labelers, keyword and topic extractors. Pre-trained multilingual large language models (LLMs) based on generative transformers are expected to gradually replace the supervised methods that have dominated these technologies over the past decade, simplifying the extraction of meaningful semantic information. However, these neural technologies are not entirely free from errors, particularly due to the inventive linguistic generation they may produce. Enabling the user community to 'educate' the AI—by monitoring and correcting its results—remains our primary approach. Transparent logical structures, such as Knowledge Graphs, provide robust support for this kind of user engagement. The way information automatically extracted from text can be conceptualized and critically examined by user communities will profoundly influence the integration of AI within human ecosystems.