Procesamiento del Lenguaje Natural ISSN: 1135-5948 [email protected] Sociedad Española para el Procesamiento del Lenguaje Natural España

Periñán-Pascual, Carlos; Arcas-Túnez, Francisco Cognitive Modules of an NLP for Understanding Procesamiento del Lenguaje Natural, núm. 39, 2007, pp. 197-204 Sociedad Española para el Procesamiento del Lenguaje Natural Jaén, España

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How to cite Complete issue Scientific Information System More information about this article Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Journal's homepage in redalyc.org Non-profit academic project, developed under the open access initiative Procesamiento del Lenguaje Natural, nº39 (2007), pp. 197-204 recibido 30-04-2007; aceptado 22-06-2007

Cognitive Modules of an NLP Knowledge Base for Language Understanding

Carlos Periñán-Pascual Francisco Arcas-Túnez Universidad Católica San Antonio Universidad Católica San Antonio Campus de los Jerónimos s/n Campus de los Jerónimos s/n 30107 Guadalupe - Murcia (Spain) 30107 Guadalupe - Murcia (Spain) [email protected] [email protected]

Resumen : Algunas aplicaciones del procesamiento del lenguaje natural, p.ej. la traducción automática, requieren una base de conocimiento provista de representaciones conceptuales que puedan reflejar la estructura del sistema cognitivo del ser humano. En cambio, tareas como la indización automática o la extracción de información pueden ser realizadas con una semántica superficial. De todos modos, la construcción de una base de conocimiento robusta garantiza su reutilización en la mayoría de las tareas del procesamiento del lenguaje natural. El propósito de este artículo es describir los principales módulos cognitivos de FunGramKB, una base de conocimiento léxico-conceptual multipropósito para su implementación en sistemas del procesamiento del lenguaje natural. Palabras clave : Representación del conocimiento, ontología, razonamiento, postulado de significado.

Abstract : Some natural language processing systems, e.g. , require a knowledge base with conceptual representations reflecting the structure of human beings’ cognitive system. In some other systems, e.g. automatic indexing or , surface semantics could be sufficient, but the construction of a robust knowledge base guarantees its use in most natural language processing tasks, consolidating thus the of resource reuse. The objective of this paper is to describe FunGramKB, a multipurpose lexico- conceptual knowledge base for natural language processing systems. Particular attention will be paid to the two main cognitive modules, i.e. the and the cognicon. Keywords : Knowledge representation, ontology, reasoning, meaning postulate.

functional model in some important aspects 1 FunGramKB with the aim of building a more robust knowledge base. FunGramKB Suite 1 is a user-friendly On the one hand, FunGramKB is environment for the semiautomatic construction multipurpose in the sense that it is both of a multipurpose lexico-conceptual knowledge multifunctional and multilanguage. In other base for a natural language processing (NLP) words, FunGramKB can be reused in various system within the theoretical model of S.C. NLP tasks (e.g. information retrieval and Dik’s Functional Grammar (1978, 1989, 1997). extraction, machine translation, dialogue-based FunGramKB is not a literal implementation of systems, etc) and with several natural Dik’s lexical database, but we depart from the . 2

1 We use the name ‘FunGramKB Suite’ to refer to our tool and 2 English, Spanish, German, French and Italian ‘FunGramKB’ to the resulting knowledge base. are supported in the current version of FunGramKB.

ISSN: 1135-5948 © 2007 Sociedad Española para el Procesamiento del Lenguaje Natural Carlos Periñan-Pascual y Francisco Arcas-Túnez

On the other hand, our knowledge base is different types (Tulving, 1985): lexico-conceptual, because it comprises two general levels of information: a lexical level • Semantic knowledge, which stores and a cognitive level. In turn, these levels are cognitive information about words; it is made up of several independent but interrelated a kind of mental . components: • Procedural knowledge, which stores information about how events are Lexical level (i.e. linguistic knowledge): performed in ordinary situations—e.g. • The lexicon stores morphosyntactic, how to ride a bicycle, how to fry an pragmatic and collocational egg...; it is a kind of manual for information of words. everyday actions. • The morphicon helps our system to • Episodic knowledge, which stores handle cases of inflectional information about specific biographic morphology. events or situations—e.g. our wedding- day; it is a kind of personal scrapbook. Cognitive level (i.e. non-linguistic knowledge): Therefore, if there are three types of • The ontology is presented as a knowledge involved in human reasoning, there hierarchical structure of all the must be three different kinds of knowledge that a person has in mind when talking schemata. These schemata are successfully about everyday situations. mapped in an integrated way into the cognitive • The cognicon stores procedural component of FunGramKB: knowledge by means of cognitive macrostructures, i.e. script-like • Semantic knowledge is represented in schemata in which a sequence of the form of meaning postulates in the stereotypical actions is organised on the ontology. basis of temporal continuity, and more • Procedural knowledge is represented in particularly on James Allen's temporal the form of cognitive macrostructures model (Allen, 1983, 1991; Allen and in the cognicon. Ferguson, 1994). • Episodic knowledge can be stored as a • The onomasticon stores information case base. 3 about instances of entities, such as people, cities, products, etc. A key factor for successful reasoning is that all these knowledge schemata (i.e. meaning The main consequence of this two-level postulates, cognitive macrostructures and cases) design is that every lexical module is language- must be represented through the same formal dependent, while every cognitive module is language, so that information sharing could take shared by all languages. In other words, place effectively among all cognitive modules. computational lexicographers must develop one Our formal language is partially founded on lexicon and one morphicon for English, one Dik’s model of semantic representation (1978, lexicon and one morphicon for Spanish and so 1989, 1997), which was initially devised for on, but knowledge engineers build just one machine translation (Connolly and Dik, 1989). ontology, one cognicon and one onomasticon to Computationally speaking, when storing process any language input cognitively. Section cognitive knowledge through FunGramKB 2 gives a brief account on the psychological Suite, a syntactic-semantic checker is triggered, foundation of FunGramKB cognitive level, and so that consistent well-formed constructs can be sections 3 and 4 describe the two main stored. Moreover, a parser outputs an XML- cognitive modules in that level, i.e. the formatted feature-value structure used as the ontology and the cognicon. input for the reasoning engine, so that

2 Cognitive knowledge in natural language understanding 3 FunGramKB can be very useful in case-based In cognitive psychology, common-sense reasoning, where problems are solved by knowledge is usually divided into three remembering previous similar cases and reusing general knowledge.

198 Cognitive Modules of an NLP Knowledge Base for Language Understanding inheritance and inference mechanisms can be establishes a high degree of connectivity among applied. Both the syntactic-semantic validator conceptual units by taking into account of meaning postulates and the XML parser were semantic components which are shared by their written in C#. meaning postulates. In order to incorporate human beings’ commonsense, our ontology 3 FunGramKB ontology must identify the relations which can be established among conceptual units, and hence Nowadays there is no single right methodology among lexical units. However, displaying for ontology development. Ontology design semantic similarities and differences through tends to be a creative process, so it is probable taxonomic relations turns out to be more that two designed by different chaotic than through meaning postulates linked people have a different structuring (Noy and to conceptual units. McGuinness, 2001). To avoid this problem, the ontology model should be founded on a solid 3.3 Three-layered ontological model methodology. The remaining of this section describes five methodological criteria applied to FunGramKB ontology distinguishes three FunGramKB ontology, some of which are different conceptual levels, each one of them based on principles implemented in other NLP with concepts of a different type: metaconcepts, projects (Bouaud et al., 1995; Mahesh, 1996; basic concepts and terminals. Figure (1) Noy and McGuinness, 2001). The definition of illustrates these three types of concepts. these criteria in the analysis and design phases of the ontology model and the strict application #ENTITY of these guidelines in the development phase → #PHYSICAL contributed to avoid some common errors in conceptual modelling. → #OBJECT → #SELF_CONNECTED_OBJECT 3.1 Symbiosis between universality and → +ARTIFICIAL_OBJECT linguistic motivation → +CORPUSCULAR FunGramKB ontology takes the form of a universal concept taxonomy, where ‘universal’ → +SOLID means that every concept we can imagine has → +BALL an appropriate place in this ontology. On the other hand, our ontology is linguistically → $FOOTBALL motivated, as a result of its involvement with the semantics of lexical units, although the Figure 1: Example of ontological structuring in knowledge stored in our ontology is not specific FunGramKB to any particular language. Metaconcepts, preceded by symbol #, 3.2 Subsumption as the only taxonomic constitute the upper level in the taxonomy. The relation analysis of the upper level in the main linguistic At first sight, it can seem that the exclusive use ontologies—DOLCE (Gangemi et al., 2002; of the IS-A relation can impoverish the Masolo et al., 2003), Generalized Upper Model ontological model. Indeed, a consequence of (Bateman, 1990; Bateman, Henschel and this restriction on the taxonomic relation is Rinaldi, 1995), Mikrokosmos (Beale, Nirenburg found in the modelling of the upper level, and Mahesh, 1995; Mahesh and Nirenburg, where metaconcepts #ENTITY, #EVENT and 1995; Nirenburg et al., 1996), SIMPLE (Lenci, #QUALITY arrange nouns, verbs and 2000; Lenci et al., 2000; Pedersen and Keson, adjectives respectively in cognitive dimensions. 1999; SIMPLE Specification Group, 2000; However, the fact that concepts linked to Villegas and Brosa, 1999), SUMO (Niles and lexical units of different grammatical categories Pease, 2001a, 2001b)—led to a metaconceptual are not explicitly connected in our ontological model whose design contributes to the model doesn’t prevent FunGramKB to relate integration and exchange of information with those lexical units in the cognitive level through other ontologies, providing thus standardization their meaning postulates. Indeed, our ontology and uniformity. Since metaconcepts reflect

199 Carlos Periñan-Pascual y Francisco Arcas-Túnez cognitive dimensions, they are not assigned frames at the lexical level. 4 meaning postulates. Therefore, our On the other hand, a meaning postulate is a metaconcepts play the role of ‘hidden set of one or more logically connected categories’, i.e. concepts which aren’t linked to predications (e 1, e 2... e n), which are cognitive any lexical unit so that they can serve as hidden constructs carrying the generic features of the superordinates and avoid circularity. concept. 5 Concepts, and not words, are the Basic concepts, preceded by symbol +, are building blocks for the formal description of used in FunGramKB as defining units which meaning postulates, so a meaning postulate enable the construction of meaning postulates becomes a language-independent semantic for basic concepts and terminals, as well as knowledge representation. To illustrate, some taking part as selection preferences in thematic predications in the meaning postulates of an frames. The starting point for the identification entity, event and quality are presented in of basic concepts was the defining vocabulary examples (4), (5) and (6) respectively: 6 in Longman Dictionary of Contemporary (4) BIRD English (Procter, 1978), though deep revision +(e : BE (x : BIRD) was required in order to perform cognitive 1 1 Theme (x :VERTEBRATE) ) mapping. 2 Referent *(e : HAVE (x ) (x : FEATHER Finally, terminals are headed by symbol $. 2 1 Theme 3 m & 2 LEG WING) ) The borderline between basic concepts and & 2 Referent *(e : FLY (x ) ) terminals is based on their definitory potential 3 1 Theme to take part in meaning postulates. (5) KISS +(e 1: TOUCH (x 1: PERSON) Agent (x 2)Theme 3.4 Non-atomicity of conceptual units (f 1: 2 LIP) Instrument (f 2: (e 2: LOVE (x 1)Agent (x ) ) | (e : GREET (x ) In FunGramKB, basic and terminal concepts 2 Theme 2 1 Agent (x ) )) ) are not stored as atomic symbols but are 2 Theme Reason provided with a rich internal structure (6) HUGE consisting of semantic properties such as the +(e 1: BE (x 2)Theme (x 1: HUGE) Attribute ) thematic frame or the meaning postulate. +(e 2: BE (x 1)Theme (x 3: SIZE) Referent ) On the one hand, every event in the ontology +(e 3: BE (x 2)Theme (x 4: m BIG) Attribute ) is assigned one thematic frame, i.e. a For instance, predications in example (1) prototypical cognitive construct which states have the following natural language the number and type of participants involved in equivalents: the cognitive situation portrayed by the event. In turn, predicate frames of verbs in the lexicon Birds are always vertebrates. are constructed from thematic frames in the A typical bird has many feathers, two legs ontology. For instance, hundir and zozobrar are and two wings. Spanish verbs which trigger the same thematic A typical bird flies. frame, since both of them are linked to the same concept (example 1). Dik (1997) proposes using words from the

(1) SINK (x 1)Agent (x 2)Theme (x 3: LIQUID ^ own language when describing meaning MUD) Location (x 4)Origin (x 5)Goal (f 1: postulates, since meaning definition is an SLOW) Speed internal issue of the language. However, this strategy contributes to lexical ambiguity due to However, these verbs can differ in their the polysemic nature of the defining lexical predicate frames, since they show different profiled arguments (examples 2-3).

4 (2) hundir (x 1)NP / S / Agent (x 2)NP / DO / Theme The difference between thematic frames and hundir (x 2)NP / S / Theme predicate frames is partially grounded on the distinction between argument roles and participant (3) zozobrar (x 2)NP / S / Theme roles in Goldberg’s Construction Grammar (1995). In other words, these lexical units are linked 5 Periñán Pascual and Arcas Túnez (2004) to the same thematic frame at the cognitive describe the formal grammar of well-formed predications for meaning postulates in FunGramKB. level, but the instantiation of this thematic 6 frame can make divergences occur in predicate For the sake of clarity, the names of conceptual units have been oversimplified.

200 Cognitive Modules of an NLP Knowledge Base for Language Understanding units. In addition, describing the meaning of very difficult to apply any case-based reasoning words in terms of other words leads to some on them. linguistic dependency (Vossen, 1994). Instead, In FunGramKB, meaning postulates are not FunGramKB employs concepts for the formal sufficient to describe commonsense knowledge, description of meaning postulates, resulting in but they contribute actively to build ‘cognitive an interlanguage representation of meaning. macrostructures’ in the cognicon. In other An alternative could have been to use words, our knowledge base integrates semantic second-order predicate logics for the formal knowledge from the ontology with procedural representation of lexical meaning. However, the knowledge from the cognicon, resulting in a problem lies not only on the little expressive correlation that almost no NLP system has power of predicate logics, but also on the fact achieved yet. These schemata are described as that standard logics use monotonic reasoning, ‘macrostructures’ because they are more which isn’t robust enough for the simulation of comprehensive constructions than meaning human beings’ . postulates. While meaning postulates are ontology-oriented knowledge representations, 3.5 Meaning postulates as ontological cognitive macrostructures organize knowledge organizers in scenes according to temporality and causality parameters. On the other hand, these Our ontology structuring complies with the macrostructures are described as ‘cognitive’ similarity, specificity and opposition principles because they are built with conceptual units applied to the meaning postulates of concepts. from the ontology. Unlike most natural Firstly, all subordinate concepts must share the language understanding systems, expectations meaning postulate of their superordinate about what is about to happen in a particular concept (i.e. similarity principle). Secondly, all situation are not lexical but conceptual, so subordinate concepts must have a meaning different lexical realizations with the same postulate which states a distinctive feature (or meaning in the same or different languages differentiae ) not present in the meaning correspond to the same expectation in postulate of its superordinate concept (i.e. FunGramKB. specificity principle). Finally, differentiae in the In example (7), we present some meaning postulates of sibling concepts must be predications of the cognitive macrostructure incompatible one another (i.e. opposition Eating_at_restaurants : principle). (7) (e 1: ENTER (x 1: CUSTOMER) Theme (x 2: 4 FunGramKB cognicon RESTAURANT) Goal (f 1: (e 2: BE (x 1) (x 3: HUNGRY) Attrribute )) Reason ) Text understanding must not be restricted to the (e 3: ACCOMPANY (x 4: WAITER) Theme comprehension of individual sentences, but it (x 1)Referent (f 2: TABLE) Goal ) must involve the integration of all this (e 4: SIT (x 1)Theme (x 5: f 1)Location ) information into a ‘situation model’ (Zwaan (e 5: BRING (x 4)Theme (x 6: MENU ^ and Radvansky, 1998) with the purpose of WINE_LIST) Referent (f 3: x 1)Goal ) reconstructing the textual world underlying to (e 6: REQUEST (x 1)Theme (x 7: FOOD | the literal sense of the linguistic realizations BEVERAGE) Referent (x 4)Goal ) which make up the text surface. The task of (e 7: TELL (x 4)Theme (x 8: (e 8: COOK (x 9: reconstructing the situation model of an input COOK) Theme (x 10 : FOOD) Referent )Referent text requires NLP systems to hold human (x 9)Goal ) beings' commonsense knowledge in the form of (e 9: BRING (x 4)Theme (x 11 : generic cognitive structures which can facilitate BEVERAGE) Referent (f 4: BAR) Source ) inferences and predictions as well as information selection and management. Since The main advantage of this approach is that scripts were devised by Schank and Abelson meaning postulates and cognitive (1977), little effort has been made to build a macrostructures are represented through the large-scale database of procedural-knowledge same formal language, so that knowledge can schemata. For example, both expectation be shared more effectively between packages (Gordon, 1999) and ThoughtTreasure FunGramKB cognitive modules, particularly (Mueller, 1999) are systems which contain facts when reasoning mechanisms are triggered. and rules about ordinary situations, but it is

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5 Reasoning engine in FunGramKB 6 Conclusion An NLP application is actually a knowledge- In NLP, knowledge is usually applied to the based system, so it must be provided with a input text for two main tasks: (e.g. spell knowledge base and a reasoning engine. Two checking, syntactic ambiguity resolution, etc) reasoning processes have been devised to work and partial understanding (e.g. lexical with FunGramKB cognitive modules: ambiguity resolution, , MicroKnowing and MacroKnowing. etc). Full natural language understanding is MicroKnowing (Micro conceptual- hardly performed. Indeed, deep semantics for Know ledge Spreading ) is a multi-level process NLP is currently very limited, perhaps because performed by means of two types of reasoning most applications exploit WordNet as a source mechanisms: inheritance and inference. Our of information. Moreover, researchers do not inheritance mechanism strictly involves the even agree on how much semantic information transfer of one or several predications from a is sufficient to achieve the best outcome. superordinate concept to a subordinate one in However, it is thought that performance is the ontology. On the other hand, our inference improved if the system is provided with a mechanism is based on the structures shared robust knowledge base and a powerful between predications linked to conceptual units inference component (Vossen, 2003). In fact, which do not take part in the same subsumption the main problem in the successful relation within the ontology. Cyclical development of natural language understanding application of the inheritance and inference systems lies on the lack of an extensive mechanisms on our meaning postulates allow commonsense knowledge base. Since FunGramKB to minimize redundancy as well commonsense is mainly made up of semantic as keeping our knowledge base as informative and procedural knowledge, which FunGramKB as possible. When the language engineer stores in the form of meaning postulates and modifies an existing meaning postulate or cognitive macrostructures respectively, we can builds a new one, just before being stored, conclude that FunGramKB can help language FunGramKB Suite automatically performs the engineers to design more intelligent NLP MicroKnowing for that meaning postulate in applications. order to check the compatibility of the newly- incorporated predications with other Bibliography predications involved in the reasoning process. Allen, J. 1983. Maintaining knowledge about The language engineer is informed about any temporal intervals. Communications of the incompatibility with inferred or inherited ACM, 26 (11): 832-843. predications. In addition, FunGramKB Suite displays the whole MicroKnowing process step Allen, J. 1991. Time and time again: the many by step, enabling us to verify inference and ways to represent time. International inheritance conditions in a transparent way. 7 Journal of Intelligent Systems, 6 (4): 341- Currently we are working on the 355. MacroKnowing (Macro conceptual-Know ing Allen, J. and G. Ferguson. 1994. Actions and Spreading ), i.e. the process of integrating events in interval temporal logic. Journal of meaning postulates from the ontology with the Logic and Computation, 4 (5): 531-579. cognitive macrostructures in the cognicon in order to spread the procedural knowledge Bateman, J.A. 1990. Upper modeling: a general stored in FunGramKB. This interaction of organization of knowledge for natural semantic and procedural knowledge, so language processing. Workshop on distinctive of human reasoning, is hardly found Standards for Knowledge Representation in NLP systems to date. Systems . Santa Barbara. Bateman, J.A., R. Henschel, and F. Rinaldi. 1995. The Generalized Upper Model 2.0. Technical report. IPSI/GMD, Darmstadt. Beale, S., S. Nirenburg, and K. Mahesh. 1995. 7 Periñán Pascual and Arcas Túnez (2005) give Semantic analysis in the Mikrokosmos an accurate description of MicroKnowing in machine translation project. Proceedings of FunGramKB.

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