To deliver services to users, central and local Public Administrations (PA) make extensive use of data. Various qualitative estimates suggest that databases contain 10-20 This work has two objectives: to summarize the experiences carried out over the past four years by the National Interuniversity Consortium for Informatics (CINI) in the Datalake project funded by the CRUI in collaboration with the Directorate General of Automated Information Systems (DGSIA) of the Ministry of Justice, in synergy with other related projects of the Ministry; and to demonstrate how the experiences, Proof of Concepts, and functional specifications produced can serve as a repository of functionalities for a “semantic document management system for PA,” which aims to evolve the information systems of PAs into platforms where unstructured data can be exploited and integrated with structured data to enhance and add value to the digital services provided by the PA, and where governance processes can be conducted using all knowledge expressed in documents and other forms of unstructured data. The judicial organization, proceedings, processes, user needs, functional structure of the Datalake, and implementation architecture are described, aiming towards a design and production pathway directed at all PAs.
Ital-IA 2024: 4th National Conference on Artificial Intelligence, organized by CINI, May 29-30, 2024, Naples, Italy * Corresponding author. † ChatGPT was used to translate original content written by the authors in Italian; the authors have read and revised the translation, ultimately agreeing on the final content. $ carlo.batini@unimib.it (C. Batini); matteo.palmonari@unimib.it (M. Palmonari); valerio.bellandi@unimi.it (V. Bellandi) © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License "Casellario Giudiziale") and databases of the Department Attribution 4.0 International (CC BY 4.0). of Penitentiary Administration and the Department of Juvenile and Community Justice being the main realizations. The Datalake project was initiated in 2019 by the DGSIA, which entrusted the CRUI and, subsequently, the Consorzio Interuniversitario Nazionale per l’Informatica (CINI), with a renewed line of research over the years, investigating the adoption of technologies based on natural language processing (NLP), knowledge graphs, machine learning, and, recently, generative AI, which can be most useful in carrying out the primary processes of civil and criminal cognition and execution. In 2022, Justice included Datalake among the projects funded with PNRR funds, whose implementation was entrusted through a tender to a group of three companies: Almaviva, Almawave, and Accenture.
The Datalake project focused on the preliminary investigation phase of criminal proceedings, criminal enforcement, and the cognition phase of civil proceedings.
The functionalities developed originate from a user requirements elicitation activity, which for the criminal procedure involves Prosecutors and the Judicial Police, and for the civil procedure involves Judges. The requirements were collected in the preliminary investigation phase, through the sharing of Proof of Concepts on deifned procedures, and subsequently through consultations on ongoing procedures. In the civil domain, interviews were conducted with Judges of the Court of Appeal of Milan, who will soon experiment with a first set of functionalities developed by the supplier Almawave. The outcome of the experiment will result in a new version of the system that can be adopted in all Courts of Appeal. The user needs are briefly described below.
Primary Activities – Civil Proceedings • Enrichment and exploration of legal knowledge during the process. • Semantic search for nominal entities, mentions, concepts, terms, phrases, and simple sentences, to analyze seriality and search for precedent cases. • Search for relevant judgments and case law with a single integrated access point. • Selection of judgments concerning topics (e.g., damages for privacy violations) not included in the system metadata. • Decision support in the cognition phase of the judgment and linking relevant acts and documents with the judgment. • Extraction from civil judgments of the outcome of the process (e.g., damage compensation, maintenance allowance in judicial divorce proceedings) and correlated salient features.
• Predictive models of the expected durations and variability of the proceedings based on their characteristics. • Assessment of the expected complexity of the proceedings for the distribution of workload among judges, identification of "bottlenecks", identification of signals and events that significantly impact the duration of the proceedings, and analysis of the impact of changes in laws, regulations, and practices. • Descriptive statistics on structured data and judg
ments. • Correlation analysis between salient characteristics and outcomes in civil proceedings for uniformity purposes (so-called "tabulation"). • Comparative analysis of trial durations in diferent sections and districts. • Specific searches and semantic aggregations for 3. Functionalities the discovery and confirmation of clues and evidence. The Proof of Concept developed and the functional spec• Integrated analysis of relational knowledge with ifications produced within the Datalake project concern visualization. the following macro-functionalities: Preparation, Seman• Selection of node clusters in a semantic graph tic Enrichment and Knowledge Integration, Semantic with certain properties. Search and Analysis, Knowledge Base Management, and • Reconstruction of relationships maintained by Quality Control. The following are the detailed functionsuspected individuals, composing the entire rela- alities.
tional network of the suspect. • Transcriptions and semantic enrichment of audio F1 - Preparation messages. 1. Document pre-processing (removal of special characters, correction of accented letters, removal of headers, removal of stamps, punctuation management) 2. OCR and generation of interpretable documents. 3. Identification of sections of the judgments:
preamble, case description, and decision.
4. Classification of texts within the files.
Semantic enrichment is performed by extracting
information from documents, especially named entities and
terms, and persisting the result of this extraction process
into semantic annotations. This process is in use in
legal AI to a large extent. The peculiar characteristic of
the proposed approach lies in the efort to consolidate
the knowledge extracted by linking diferent mentions
that refer to the same entities (exploiting background
knowledge bases like Wikipedia and clustering mentions
of the entities - of course, the majority - that are not
present in Wikipedia) [
Civil Trials. Various NLP techniques have been
applied to extract, link, and consolidate entity mentions
from judgments and produce semantic annotations that
associate the extracted entities with specific token
sequences in the judgments. In particular, the current
pipeline combines the following techniques [
Criminal Trials - Preliminary Investigations. For
documents related to preliminary investigations, a very
similar pipeline was applied for entity extraction and
subsequent document annotation, a similar semantic search
paradigm. A first discussion of the application of
entitycentric approaches to manage documents in preliminary
investigations can be found in [
Other developed functionalities include domain
concept extraction, text summarization, and georeferencing
of spatial entities. For all functionalities, accuracy
analyses were conducted based on scientific methodologies.
For the entity extraction pipeline, some results are
reported in [
Common Search Functionalities for Preliminary Investigations and Civil Proceedings. Search functionalities are inspired by the well-known faceted and semantic search paradigms, with additional and more experimental Question Answering (QA) capabilities based on the Retrieval Augmented Generation (RAG) paradigm.
Based on the semantic enrichment functionalities shown in the previous point, the entities that appear in the filters during the search phase can refer to mentions present in diferent documents; moreover, when a user explores, for example, a judgment, they can find all mentions of an entity throughout the document, a feature that can become particularly relevant for long judgments or other documents. The conceptual architecture for semantic search is shown in Fig 1. The components are: The above functionalities have all been demonstrated using DAVE, a prototype open-source application for semantic search developed in the context of this and the PON Next Generation UPP 1 project. A video demonstrating the proposed combination of semantic and conversational search on judgments of criminal trials published online is available at https://www.youtube.com/watch? v=XG7RsI3t-2Q. However, the data enrichment process developed in the project supports also other forms of search, such as Advanced search. This functionally supports advanced searches by combining various filters on document attributes. This module is included in many search applications on structured or semi-structured data, to complement the modules based on Keyword search and Faceted search; typically, the function of this module is to construct precise queries based on structured descriptions of documents.
Analysis Functionalities for Governance
Activities - Civil. The semantic organization of documents
obtained through semantic enrichment and integration
functionalities enabled by the Entity registry allows for
multiple statistics and correlations on structured data
linked to annotated documents, e.g., the number of
documents involving natural legal entities, the number and
average value of minors involved in divorce decisions,
correlation for tabulation purposes of the compensation
value and related features in non-pecuniary damage cases.
• Keyword search: Allows simple keyword searches. Further analyses concern survival curves of processes
This module can be useful as a starting point for and explanatory variables of temporal duration and
prothe search, before activating the faceted search. cess complexity. Several analysis functionalities were
• Faceted search: Combines keyword searches and developed within the PON Next Generation UPP project
iflters based on the attributes of the judgments. and other CRUI-funded projects. The following research
The module uses known technologies for index- based on the SICID system registers for the PCT was
ing and querying document databases (e.g., Elas- conducted (see [
A management control dashboard was created for the Court of Cassation. The adopted solution was to create a dashboard directly fed by the underlying database of the Court’s SIC register, with data updated four times a day.
All data were identified for: • Feeding the variables and indicators identified as necessary to describe the file path in the various phases and to calculate indices such as the
Disposition Time and the turnover index; • Building the historical series of such data from
January 2019.
Analysis Functionalities for Preliminary Investigations. Relational knowledge analysis with visualization (e.g., selection of clusters of nodes with certain properties) and anomaly detection.
Functionalities for Penal Execution. Integration for the social analysis of data relating to liberty restrictions/alternative penalties experienced by detainees during their lives.
3. Extraction of Lexicons: Involves extracting
lexicons of terms based on noun phrases from
judgments and organizing them into an ontology, with
specialization of the lexicon in the legal field
(finetuning).
4. Quality Assessment of NER and NEL: Evaluation
of the quality of Named Entity Recognition (NER)
and Named Entity Linking (NEL) [
In the functionalities of the Datalake, the following ontologies are used: • Top Ontology of Justice Procedures (cognition and execution): Consists of about 400 classes, represented through approximately 40 schemas in the Entity-Relationship model at different levels of integration/abstraction. • Ontology for Penal Execution: Consists of about 100 classes and 8 schemas in the EntityRelationship model, including all the databases related to penal execution.
The following additional ontologies are represented 1. Manual of Pseudonymization Trial Policies: Dif- in the form of two-level taxonomies: i) Top ontology of ferent types of pseudonymization are considered, preliminary investigations, ii) Top ontology of the civil and various types of data and document process- trial, iii) Domain ontologies of the civil process: banking, ing where pseudonymization is relevant (e.g., pub- labor, non-patrimonial damage from privacy violation, lication, linking databases, etc.) are identified, judicial separation, iv) Ontology for penal-cognition proalong with the properties that must be respected cedure: victim-perpetrator relationship. The top ontolin each case. A general method is provided that ogy of Justice procedures and penal execution were procan be followed for the diferent types of data duced through reverse engineering from logical schemas. processing relevant to the Datalake project. The ontologies for the victim-perpetrator relationship 2. Entity Registry Management: Includes creation, and non-patrimonial damage were produced by domain updating, deletion of entities, merging, and split- experts. The ontologies for banking and labor were proting of entities. duced from lexicons built through the analysis of judgments. governance, aligning with the strategic path of digital transformation of the country, currently being implemented in the National Strategic Hub. Including services for a semantic document system for the PA in the service architecture of the Hub would require the production of a common top ontology for the PA and high-level modeling of primary and governance processes, with subsequent customization by the individual PAs.