Domain-specific named entity disambiguation in historical memoirs

This paper presents the results of the extraction of named entities from a collection of historical memoirs about the italian Resistance during the World War II. The methodology followed for the extraction and disambiguation task will be discussed, as well as its evaluation. For the semantic annotations of the dataset, we have developed a pipeline based on established practices for extracting and disambiguating Named Entities. This has been necessary, considering the poor performances of out-of-the-box Named Entity Recognition and Disambiguation (NERD) tools tested in the initial phase of this work.Questo articolo presenta l’attività di estrazione di entità nominate realizzata su una collezione di memorie relative al periodo della Resistenza italiana nella Seconda Guerra Mondiale. Verrà discussa la metodologia sviluppata per il processo di estrazione e disambiguazione delle entità nominate, nonché la sua valutazione. L’implementazione di una metodologia di estrazione e disambiguazione basata su lookup si è resa necessaria in considerazione delle scarse prestazioni dei sistemi di Named Entity Recognition and Disambiguation (NERD), come si evince dalla discussione nella prima parte di questo lavoro

Event-based Access to Historical Italian War Memoirs

The progressive digitization of historical archives provides new, often domain specific, textual resources that report on facts and events which have happened in the past; among these, memoirs are a very common type of primary source. In this paper, we present an approach for extracting information from Italian historical war memoirs and turning it into structured knowledge. This is based on the semantic notions of events, participants and roles. We evaluate quantitatively each of the key-steps of our approach and provide a graph-based representation of the extracted knowledge, which allows to move between a Close and a Distant Reading of the collection.Comment: 23 pages, 6 figure

Knowledge Extraction for Art History: the Case of Vasari’s The Lives of The Artists (1568)

Knowledge Extraction (KE) techniques are used to convert unstructured information present in texts to Knowledge Graphs (KGs) which can be queried and explored. Despite their potential for cultural heritage domains, such as Art History, these techniques often encounter limitations if applied to domain-specific data. In this paper we present the main challenges that KE has to face on art-historical texts, by using as case study Giorgio Vasari's The Lives of The Artists. This paper discusses the following NLP tasks for art-historical texts, namely entity recognition and linking, coreference resolution, time extraction, motif extraction and artwork extraction. Several strategies to annotate art-historical data for these tasks and evaluate NLP models are also proposed

Knowledge Extraction for Art History: the Case of Vasari’s The Lives of The Artists (1568)

Knowledge Extraction (KE) techniques are used to convert unstructured information present in texts to Knowledge Graphs (KGs) which can be queried and explored. Despite their potential for cultural heritage domains, such as Art History, these techniques often encounter limitations if applied to domain-specific data. In this paper we present the main challenges that KE has to face on art-historical texts, by using as case study Giorgio Vasari’s The Lives of The Artists. This paper discusses the following NLP tasks for art-historical texts, namely entity recognition and linking, coreference resolution, time extraction, motif extraction and artwork extraction. Several strategies to annotate art-historical data for these tasks and evaluate NLP models are also proposed

Musical Meetups: a Knowledge Graph approach for Historical Social Network Analysis

The large-scale analysis of historical events data makes it possible to trace key points of cultural and social exchange in history. There has been research focused on facilitating the integration and interpretation of events from heterogeneous sources (such as memoirs, books, and biographies) mainly considering events as a sequence of spatiotemporal objects. However, exploring and discovering new connections (e.g., collaborations, interactions) between people does require characterising those events with dimensions that are relevant to the scholarly enquiry such as the actual participants and nature of the event. This paper describes the concept of historical meetup to represent the encounters (for instance, collaborations, exchanges, links) between personalities of European history and formalise its constituent parts as an ontology. Furthermore, we report on preliminary work undertaken to generate a Knowledge Graph of historical meetups extracted from encyclopedic sources, i.e. biographies collected from Wikipedia. We discuss our results and illustrate the challenges of extracting such type of knowledge from biographical sources. The current experimental setting explores historical meetups in the European musical culture between 1800 and 1945. Our work sketches the basis for applying event knowledge graphs to cultural and social history research, providing support for the analysis, and exchange of ideas and practices

Building Lightweight Ontologies for Faceted Search with Named Entity Recognition: Case WarMemoirSampo

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