ONTOPRIMA: A Prototype for Automating Ontology Population

Abstract

ABSTRACT

A strategy for archives metadata representation on CIDOC-CRM and knowledge discovery

“…This process is achieved by applying Natural Languages Processing (NLP) techniques. OntoPrima [15] is a NLP-based Ontology Population system that extracts instances of concepts and instances of relations from text, to populate a given ontology based on NLP techniques for language processing, semantic web techniques (RDFS, RDF, Jena APIs) for knowledge modeling and representation, and on domain expert's intervention to validate extracted instances. This topic is explored in other works such as [6,16,18].…”

“…The matching between the sentences terms (nouns, adjectives, prepositions, verbs, named entities) and classes, properties and instances of an ontology is a common step in natural language interpretation for querying an ontology or mining text to populate an ontology [6,15,21,22].…”

“…These activities can be baptisms, weddings or deaths, and the information is organized in a list of names and tagged by the role of the relationship that connects to the 'Recipient' of the unit. For instance, to illustrate this pattern, consider the Item unit entitled "REGISTO DE BAPTISMO", 15 which refers to the baptism happening of the person named "Ana", 'Recipient' of the unit, and its 'Scope and content' element has the following text value: "Pais: Manuel de Oliveira e Rufina Maria Avos maternos: Manuel da Fonseca e Rosa da Silva Avós paternos: José de Oliveira e Jacinta de Oliveira Padrinhos: Manuel Martins Ramos e Maria Francisca Data de nascimento: 10 de Fevereiro de 1812" ("Parents: Manuel de Oliveira and Rufina Maria Maternal grandparents: Manuel da Fonseca and Rosa da Silva Paternal grandparents: José de Oliveira and Jacinta de Oliveira Godparents: Manuel Martins Ramos and Maria Francisca Birthdate: 10th February, 1812") Applying to this text some NLP tools (e.g. tagger and lemmatization) and some grammar rules, it is possible to extract the names of each person and the corresponding role of the relationship that links each person to "Ana" through the baptism activity.…”

Ontology Population Reusing Resources for Dialogue Intent Detection: Generic and Multilingual Approach

“…In this sense, a well-known challenge to the Semantic Web community is how to reduce the amount of manual work in the ontology population task. As noted by Kontopoulos et al (2017), most approaches for ontology population make use of textual input and rely on natural language processing techniques to obtain the necessary knowledge to populate the ontology (Corcoglioniti et al, 2016;Makki, 2017), whereas approaches that rely on data that is structured at some degree -which are of interest for this work-are less common (Leshcheva et al, 2017;Kontopoulos et al, 2017). Considering this, the authors in Kontopoulos et al (2017) use structured knowledge in Linked Data for ontology population.…”

“…In an ontology-based approach, to populate the domain ontology with this information, it is very important that the maximum number of these possibilities is considered. Doing so manually would be a high time-and resource-consuming task, and the tendency is to apply semi-automatic or automatic techniques (Makki, 2017;Benabbas et al, 2018). According to this tendency, this work presents a generic strategy that aims to overcome the challenge of instantiating the domain ontology with intents and their trigger words by semiautomatically exploiting existing lexical resources, without reducing the quality of the results and decreasing time and costs in the adaptation to different scenarios and applications.…”