Maria Pia di Buono

Terminology and Knowledge Representation. Italian Linguistic Resources for the Archaeological Domain

Knowledge representation is heavily based on using terminology, due to the fact that many terms have precise meanings in a specific domain but not in others. As a consequence, terms becomes unambiguous and clear, and at last, being useful for conceptualizations, are used as a starting point for formalizations. Starting from an analysis of problems in existing dictionaries, in this paper we present formalized Italian Linguistic Resources (LRs) for the Archaeological domain, in which we integrate/couple formal ontology classes and properties into/to electronic dictionary entries, using a standardized conceptual reference model. We also add Linguistic Linked Open Data (LLOD) references in order to guarantee the interoperability between linguistic and language resources, and therefore to represent knowledge.

Semi-automatic Indexing and Parsing Information on the Web with NooJ

Due to the large amount of data available on the Web, indexing information represents a crucial step to guarantee fast and accurate Information Retrieval (IR). Indexing content allows to find relevant documents on the basis of a user’s query. Numerous researches discuss the use of automated indexing, considered faster and cheaper than manual systems. However, in order to produce the index, using algorithms, entails low precision, low recall, and generic results [1]. This is the reason why in this paper we propose a NooJ-based system, by means of which we will develop a search engine able to process online documents, starting from a natural language query, and to return information to users. To do this, and in order to analyze user’s request, we will employ software automations to apply NooJ and its Linguistic Resources (LRs).

How to Populate Ontologies

The Cultural Heritage (CH) domain brings critical challenges as for the application of Natural Language Processing (NLP) and ontology population (OP) techniques. Actually, CH embraces a wide range of content, variable by type and properties and semantically interlinked whit other domains.This paper presents an on-going research on language treatment based on Lexicon-Grammar (LG) approach for improving knowledge management in the CH domain. We intend to show how our language formalization technique can be applied for both processing and populating a domain ontology.

Knowledge management and Cultural Heritage repositories: Cross-Lingual Information Retrieval strategies

In the last years important initiatives, like the development of the European Library and Europeana, aim to increase the availability of cultural content from various types of providers and institutions. The accessibility to these resources requires the development of environments which allow both to manage multilingual complexity and to preserve the semantic interoperability. The creation of Natural Language Processing (NLP) applications is finalized to the achievement of CrossLingual Information Retrieval (CLIR). This paper presents an ongoing research on language processing based on the LexiconGrammar (LG) approach with the goal of improving knowledge management in the Cultural Heritage repositories. The proposed framework aims to guarantee interoperability between multilingual systems in order to overcome crucial issues like cross-language and cross-collection retrieval. Indeed, the LG methodology tries to overcome the shortcomings of statistical approaches as in Google Translate or Bing by Microsoft concerning Multi-Word Unit (MWU) processing in queries, where the lack of linguistic context represents a serious obstacle to disambiguation. In particular, translations concerning specific domains, as it is has been widely recognized, is unambiguous since the meanings of terms are mono-referential and the type of relation that links a given term to its equivalent in a foreign language is biunivocal, i.e. a one-to-one coupling which causes this relation to be exclusive and reversible. Ontologies are used in CLIR and are considered by several scholars a promising research area to improve the effectiveness of Information Extraction (IE) techniques particularly for technical-domain queries. Therefore, we present a methodological framework which allows to map both the data and the metadata among the language-specific onto

Endpoint for Semantic Knowledge (ESK)

This work arises from the evaluation of the existing methods used to process knowledge stored in on-line repositories and databases.