Ontological Semantic Support for a Specific Domain

This paper attempts, out of necessity, to reach two different audiences—never such a great idea! For those familiar with the ontological semantic approach, it focuses, in Section 3, on the crucial issue of expanding ontological semantic resources to new specific domains, which is a decisive factor for spreading the use of the ontological semantic legacy resources and their enrichment and improvement by the fast-growing ontological semantic community. For the majority of the MCLC participants, it also quickly introduces the basic notions of the approach in Section 1 and attempts, in Section 2, to explain why it should be preferred to other approaches if one is in the business of developing the real applications and wants to do it on a principled theoretical basis. The major statement on the approach is, of course, Nirenburg and Raskin (2004), but this paper tries to bring some aspects to a sharper focus as well as updating and upgrading that text written in 1998-2001 and indicating the divergence within the approach. The paper is complemented, within this volume, by Spartz et al. (2005), to which most methodological details are delegated. 1 Ontological Semantics: Basics The massive 1994-2000 research effort that led to the establishment of the body of the theory of ontological semantics and the 1998-2002 effort at formulating its purview and premises (Nirenburg and Raskin 2004) have brought about the following legacy resources: • the 5,500 language-independent o ology, a tangled conceptual hierarchy, with each conceptual node containing a set of properties, each consisting of a slot, a facet, and a filler, where the first and the last are also concepts (see examples below); • the 40,000-lexical-entry English lexicon, with the meaning zone of each entry anchored in an ontological concept with the appropriate constraints on the property fillers (see examples below); • several smaller lexicons for a couple of dozen other languages, the biggest of those for Spanish; • several language-dependent onomastica (pl. of onomasticon), i.e., lexica of proper names (see examples below); • The Text Meaning Representation (TMR) language for a compositional and supracompositional representation of the meaning of the sentence and text in ontological terms (see examples below); • The prototype for the o Preprocessor taking care of: tokenization, ecological processing, morphological processing, and syntactic parsing; o Analyzer that assigns a TMR to sentences and texts; o Generator that transforms a TMR into a text in a natural language or into a set of data; • The fact repository containing every processed TMR and the procedures for searching, comparing, and modifying the property slots; • The acquisition toolbox (see Spartz et al. 2005 for details) for 3-tiered semiautomatic acquisition of ontological concepts and lexical entries, with the massive acquisition implemented by the minimallytrained lowest-tier (Tier 1) acquirers contributing only the intuitive native knowledge of word meaning elicited in a tightly controlled environment under the close automatically triggered supervision by a small number of higher-tiered acquirers (Tier 2, the acquisition managers, determining the class membership for the templates, and Tier 3, the master acquirers, developing the templates for new types of