Creating a Multilingual Terminological Resource using Linked Data: the case of Archaeological Domain in the Italian language
Authors: Giulia Speranza, Carola Carlino, Sina Ahmadi (Università di Napoli L'Orientale, National University of Ireland, 2019)
Topic: Latin Resources

Abstract
The lack of multilingual terminological resources in specialized domains constitutes an obstacle to the access and reuse of information. In the technical domain of cultural heritage and, in particular, archaeology, such an obstacle still exists for the Italian language. This paper presents an effort to fill this gap by collecting linguistic data using existing Collaboratively-Constructed Resources and those on the Web of linked data. The collected data are then used to linguistically enrich the ICCD Archaeological Finds Thesaurus– a monolingual Italian thesaurus. Our terminological resource contains 446 terms with translations in four languages and is publicly available in the Resource Description Framework (RDF) in the Ontolex-Lemon model.

Introduction
Multilingual domain-specific linguistic resources, such as thematic dictionaries and terminological resources (terminologies further in the text), are knowledge repositories providing information about terms and their semantic relationships in a specific domain and across languages. Currently, most European languages, including Italian, lack terminologies in the field of cultural heritage (Dong, 2017). With cultural heritage one defines the tangible and intangible objects that constitute the culture of each society such as monuments but also songs, traditions, and history (Doerr, 2009). Given the expanding amount of cultural data on the Semantic Web and a plethora of publicly-available resources in various languages as Linked Open Data (LOD), the Web provides solutions for enhancing multilingualism in terminologies (Brugman et al., 2008). Nowadays, many Collaboratively-Constructed Resources (CCRs), or Collaborative Knowledge Bases (CKBs), such as Wiktionary1 and Wikipedia2, are created by decentralized communities of volunteers in different domains. CCRs differ from Linguistic Knowledge Bases (LKBs), such as WordNet (Miller, 1995) and FrameNet (Baker et al., 1998), which are instead created by experts in specific fields with higher quality control. Some scholars, such as Muller and Gurevych (2008) and Hovy et al. (2013), pointed out several weaknesses of LBKs such as the low coverage of domain-specific vocabulary, restriction to common vocabulary and the difficulty in continuous maintenance resulting in outdated data. Moreover, despite the application of CCRs in various natural language processing (NLP) tasks (Zesch et al., 2008; Nakayama et al., 2008; Meyer and Gurevych, 2012), processing heterogeneous and often unstructured data linguistically requires syntactic, lexical, and ontological information (Bouayad-Agha et al., 2012; Davies, 2009). This can be efficiently addressed thanks to the current advances in applying computational techniques to the disciplines of the humanities, known as digital humanities (DH), and accessibility of linguistic resources on the Web with movements such as the Linguistic Linked Open Data (LLOD) (Chiarcos et al., 2013). Regarding the field of cultural heritage, multilingualism is still a challenge due to the tendency of experts to store terminologies monolingually (Vavliakis et al., 2012). We investigated some online multilingual terminologies such as the Getty Vocabularies3 (Baca and Gill, 2015) which contains thesauri in art, architecture, and cultural objects, iDAI.vocab–the German Archaeological Institute archaeological vocabulary4, the UNESCO Thesaurus5, the European Heritage Network thesauri6 and the Loterre Controlled Vocabulary in art and archaeology7. Among these resources, only the Art & Architecture Thesaurus (AAT) by Getty and the iDAI.vocab are exploitable due to a partial domain-specific similarity with our dataset; nevertheless, none of them provide lexicographic descriptions of the terms. In this paper, we propose an approach for semi-automatically creating a multilingual terminology in the technical domain of archaeology and cultural heritage by enriching an existing Italian ontology with linguistic information. Our approach can be applied to any domain and language. Our case study is the archaeological thesaurus provided by the Central Institute for Catalogue and Documentation (ICCD) for describing archaeological finds in Italian (Felicetti et al., 2013). The enriched information is evaluated by annotators, and then converted into the Ontolex-Lemon model in the Resource Description Framework (RDF). Our resource provides linguistic information of 446 Italian terms with translations in four languages.

Conclusion
In this paper, we demonstrated the usage of LOD and CCR in enriching terminological ontologies. As a case study, we used an ontology in Italian in the field of cultural heritage and archaeology to create multilingual terminologies. The results of the manual evaluation and implementation process show that leveraging such resources is a valid option for enriching ontologies linguistically. Nonetheless, since CCRs are created by a community effort, a manual verification was carried out for creating gold-standard datasets. Finally, the effort of this study can be framed within the more general context of contributing to the implementation and advancement of the multilingual Web of Data and the LLOD movement. The multilingual resource that we are proposing can be used in several professional figures among which lexicographers, translators, museum and exhibition experts, archaeologists and researchers. Further experiments will concern retrieving MWEs as we have not included them in the current study due to the scarce availability on Wikidata and Wiktionary. MWEs are a topic increasingly handled in NLP, and their processing is fundamental for NLP tasks ranging from POS tagging to Machine Translation to obtain better and more reliable results (Monti et al., 2018). We are also interested in creating gold concepts more efficiently, particularly using topic modelling techniques, and integrating more resources, particularly ConceptNet (Liu and Singh, 2004) which contains many resources such as WordNets and DBpedia.