Notwithstanding its acknowledged richness, the SIMPLE semantic model does not offer the representational vocabulary for encoding some conceptual links holding between events and their participants and among co-participants in events. Although critical for boosting performance in many NLP application tasks, such deep lexical information is therefore only partially encoded in the SIMPLE-CLIPS Italian semantic database. This paper reports on the enrichment of the SIMPLE relation set by some expressive means, namely semantic relations, borrowed from the EuroWordNet model and their implementation in the SIMPLE-CLIPS lexicon. The original situation existing in the database, as to the expression of this type of information is described and the loan descriptive vocabulary presented. Strategies based on the exploitation of the source lexicon data were adopted to induce new information: a wide range of semantic - but also syntactic - information was investigated for singling out word senses candidate to be linked by the new relations. The lexicon enrichment by 5,000 new relations instantiated so far has therefore been carried out as a largely automated, low-effort and cost-free process, with no heavy human intervention. The redundancy set off by such an extension of information is being addressed by the implementation of inheritance in the SIMPLE-CLIPS database (Del Gratta et al., 2008).

This paper presents the application of inheritance to the formal taxonomy (is-a) of a semantically rich Language Resource based on the Generative Lexicon theory, SIMPLE-CLIPS. The aim is to lighten the representation of its semantic layer by reducing the number of encoded relations. A prediction calculation on the impact of introducing inheritance regarding space occupancy is carried out, yielding a significant space reduction of 22%. This is corroborated by its actual application, which reduces the number of explicitly encoded relations in this lexicon by 18.4%. Later on, we study the issues that inheritance poses to the Language Resources, and discuss sensitive solutions to tackle each of them, including examples. Finally, we present a discussion on the application of inheritance, from which two side effect advantages arise: consistency enhancement and inference capabilities.

In the few last years, due to the increasing importance of the web, both computational tools and resources need to be more and more visible and easily accessible to a vast community of scholars, students and researchers. Furthermore, high quality lexical resources are crucially required for a wide range of HLT-NLP applications, among which word sense disambiguation. Vast and consistent electronic lexical resources do exist which can be further enhanced and enriched through their linking and integration. An ILC project dealing with the link of two large lexical semantic resources for the Italian language, namely ItalWordNet and PAROLE-SIMPLE-CLIPS, fits this trend. Concrete entities were already linked and this paper addresses the semi-automatic mapping of events and abstract entities. The lexical models of the two resources, the mapping strategy and the tool that was implemented to this aim are briefly outlined. Special focus is put on the results of the linking process: figures are reported and examples are given which illustrate both the linking and harmonization of the resources but also cases of discrepancies, mainly due to the different underlying semantic models.

ItalWordNet (IWN) and PAROLE/SIMPLE/CLIPS (PSC), the two largest electronic, general-purpose lexical resources of Italian language present many compatible aspects although they are based on two different lexical models having their own underlying principles and peculiarities. Such compatibility prompted us to study the feasibility of semi-automatically linking and eventually merging the two lexicons. To this purpose, the mapping of the ontologies on which basis both lexicons are structured was performed and the sets of semantic relations enabling to relate lexical units were compared. An overview of this preliminary phase is provided in this paper. The linking methodology and related problematic issues are described. Beyond the advantage for the end user to dispose of a more exhaustive and in-depth lexical information combining the potentialities and most outstanding features offered by the two lexical models, resulting benefits and enhancements for the two resources are illustrated that definitely legitimize the soundness of this linking and merging initiative.

The study which is reported here aims at investigating the extent to which the conceptual and representational tools provided by a lexical model designed for the semantic representation of general language may suit the requirements of knowledge modelling in a domain-specific perspective. A general linguistic ontology and a set of semantic links, which allow classifying, describing and interconnecting word senses, play a central role in structuring and representing such knowledge. The health and medicine vocabulary has been taken as a case study for this investigation.