title: D4.6: Software for knowledge extraction from text – context – 2nd version (V1.0)
title: Software report DRAFT
titleType: AlternativeTitle
Creators: name: Tripodi, Rocco
name: Graciotti, Arianna
Publisher: name: Zenodo
Year: 2024

description: Deliverable D4.6, Software for knowledge extraction from text – context – 2nd version, presents the second release of knowledge extraction models of the Polifonia project. The task of knowledge extraction, especially when it is concerned with historical documents, is particularly complex and difficult. The historical nature of the Polifonia project, that deals with the preservation and promotion of musical heritage, poses serious challenges to the analysis of textual documents. Even though, in recent years Natural Language Processing (NLP) has witnessed many progress, first with the introduction of the transformer architecture [1] and then with the use of this architecture to develop Large Language Models (LLM) such as ChatGPT [2], these progresses are mainly focused on the analysis of contemporary and clean documents. By contrast, the documents collected and organized by the Polifonia project1 [3] are historical and from different sources, such as OCR processed periodicals and books. The first challenge posed by the historical nature of the material is related with linguistic variations. In fact, if we want to use a pre-trained language model to encode the words in a sentence, it will be difficult for it to deal with words or syntactic structures never seen in contemporary internet documents. The situation can get even worse if in addition to linguistic variations we have to deal also with OCR errors that can cause unexpected behaviour in LLM, usually trained on clean text. Furthermore, another variable that hinders the reuse of recent NLP models is the different distribution of entities in historical and contemporary texts. State-of-the-art NLP models for entity linking, the task of associating a mention in a text with the description of this mention in a knowledge base such as Wikipedia, is different and can cause the linker to produce inconsistent associations, linking, for example, the mention of a person in a XVIII century book with an entity that lived in XX century, only because the training set used by this model contains more references to contemporary entity than to historical ones [4] or because the reference knowledge base does not contain minor historical entities. Taking all these issues into account, in this deliverable we improved our knowledge extraction pipeline (Polifonia Knowledge Extractor2) under different aspects:• presenting Text2AMR2FRED, a text-to-Knowledge Graph (KG) pipeline that transforms multilingual natural language text into OWL-compliant RDF Knowledge Graphs (Section 2);• improving the quality of text by developing a new set of models for the correction of OCR errors in historical documents (Section 3);• creating a new benchmark for the evaluation of entity linking models, centered on historical OCRed document and on the music domain;• developed a new time-aware model for historical entity linking that will replace the general entity linker of our pipeline (Section 4).All these improvements will contribute to the general goal of WP4: making musical heritage knowledge available through dedicated knowledge graphs that can be easily queried. The produced models have been all evaluated on standard and ad-hoc benchmarks with good results and will continue to be under analysis until the end of the project, when we will release our final deliverable that deals with the development of methodologies and tools to evaluate knowledge extraction methods.