Post-Logical Verification of Ontology and Lexicons : The Ontological Semantic Technology Approach

This paper outlines the methods of practical preautomatic and automatic verification of evolving lexicons and an ontology used to process natural language meaning, and several approaches that can be taken to speed up the process and decrease the cost of such verification. The main methodological direction of this type of verification is increased automation in conjunction with confirmation by native speakers with basic instructions rather than by fully trained linguist knowledge engineers. It is assumed that ontological and lexical verification based on mathematical logic has been completed prior to the outlined verification. Keywords—ontological semantic technology, ontology verification, lexicon verification, natural language meaning I. ONTOLOGICAL SEMANTIC TECHNOLOGY Ontological Semantic Technology (OST; see [3, 6, 8]) is a modified and improved version of Ontological Semantics [5] encompasses a theory, methodology, and implementation of a system that attempts to understand natural language text. OST is based on the computer-accessible description of meanings of the words, listed according to their syntactic role in the sentence, taking into account relevant morphological features. At the core of OST are repositories of world and linguistic knowledge, acquired semi-automatically within the approach, and used to disambiguate the different meanings of words and sentences and to represent them. These repositories, also known as the static knowledge resources, consist of the ontology, containing language-independent concepts, relationships between them; one lexicon per supported language (for example, English), containing word senses anchored in the language-independent ontology, which is used to represent their meaning; the Proper Name Dictionary (PND), which contains names of people, countries, organizations, etc., and their description anchoring them in ontological concepts and interlinking them with other PND entries; and a common sense rules resource. The process of acquisition of lexicons and ontology was discussed in [5] (Ch. 9) and practiced in prior and subsequent implementations of Ontological Semantics, including the most recent implementation at RiverGlass, Inc. The tools used for acquisition and maintenance of the resources ensure the consistency of the resources to the degree possible, not least in terms of available developer time. Again, this consistency is easiest checked on a purely formal basis, such as syntactic errors (e.g., missing parentheses) in the formal representation language, and on a logical basis, for example, that no lexicon restriction contradicts an ontology restriction or that no ontological concept is more general than its parent. The lexicon and ontology, together with the PND and common sense rules, are used by the Semantic Text Analyzer (STAn), a software, developed by RiverGlass Inc., that produces Text Meaning Representations (TMRs) from the text that it processes. The format of TMRs conforms to the format and interpretation of the ontology. The processed TMRs are entered into InfoStore, a dynamic knowledge resource of OST, from which information is used for further processing and reasoning. STAn consists of a collection of modules, each of which fires when a condition that the module is designed to handle is met. STAn reads text, sentence by sentence, by consulting the lexicon and ontology about possible interpretations of each clause in the sentence, combining them, and reporting a ranked number of unambiguous text meaning representations (TMRs) at the grain size provided by the text, as accommodated by the resources. The TMRs that are produced are weighted by the software according to the extent of their fit into each module’s interpretation. Thus, it has several major components that could contribute to the incorrect processing of a sentence: the software itself, with each module taking part in it, the lexicon corresponding to the language of the processed text, and the ontology. STAn itself is tested “qualitatively” in weekly plenary team sessions that focus on recent code changes and new linguistic issues that OST has matured to handling. It is also “quantitatively” tested by running a set of sentences before and after each code change and comparing the TMRs from before and after. Any difference is flagged, and the TMR is generated after each module has run to narrow down the set of suspects. This paper, however, will focus on lexicon and ontology verification, assuming the fixed (and bug-free) release of the software. In general, it is comparatively easy to ensure that lexicons and the ontology are consistent from a logical point of view (e.g., [2] and [1], with a different formalism). The main challenge for post-logical verification is that they may misrepresent the world as expressed by natural language and lead to incorrect interpretations (cf. also [7]). It is thus crucial to the success of the application to not only check the logical representation of ontology and lexicons, both asserted and inferred, but also how well it works with real text sentences. The tests for the descriptive appropriateness of the resources need to be automated to the highest degree, not least because they have to be complete with respect to every entity in the knowledge resources: every property of every concept and every lexical sense further specifying meaning with these concepts and properties. In addition to automation, the remaining amount of human involvement needs to be shifted from the shoulders of expensive highly trained linguist knowledge engineers to those of cheaper native speakers with minimal instructions. The linguist is rather to focus on the creation of the automated verification methods that the native speakers will interact with. This effort is the focus of the present paper. The motivation and principles of the division of labor are somewhat similar to those in the “Boas” rapid deployment system of Level 2 machine translation for new languages created at an earlier phase of ontological semantics [4]. II. OST ONTOLOGY VERIFICATION An OST ontology is a collection of concepts and relationships between them as well as attributes describing the concepts, governed by a subsumption mechanism. OST ontology can be thought of as a fuzzy ontology, as described in [10]: given a set of objects D, where D is the disjoint union of Dc (concepts) and Dd (literals), and given its interpretation function I, for every fuzzy concept B, object x is an element of B with some degree I[B](x)  [0, 1]; for every relation Rel, I[Rel](x, y) ⊆ Dc x Dc  [0, 1]; for every attribute A; I[A](x, a) ⊆ Dc x Dd  [0, 1]. We will assume that x∈B if I[B](x)  (0, 1]. With this in mind, the following is true for C and D, using [12]: I [C D](x) = max{I [C](x), I [D](x)} I [and C D](x) = min{I [C](x), I [D](x)} I [(Rel(D))](x)= maxy∈D{I [Rel](y, x)} I [(Rel(and C D))](x) = min{I [Rel(C)](x), I [Rel(D)](x)} I [Rel(C D)](x) = max{I [Rel(C)](x), I [Rel(D)](x)} I [C(Rel(D))](x) = min{I [C](x), I [Rel(D)](x)} I[C(Rel1(D))(Rel2(E))](x) = min{I[C(Rel1(D))](x), I[C(Rel2(E))](x)} For the construction of the ontology, we follow the subsumption definition of [11]: C subsumes D iff ∀x∈D: