Distributedness and Non-Linearity of LOLITA's Semantic Network

'Phis paper describes SemNet the internal Knowledge t{epresentation lbr LOLI'I'A I . LOMTA is a large scale Natural Language Engineering (NLE) system. As such the internal representatiou must be richly expressive, mttural (with respect to Natural Language), and e[[icient. In network representations knowledge is gleaned by traversing the graph. The paper introduces two I)rol)crties, ( d i s t r i b u t e d n e s s and nonl i ne a r i t y ) of networks which directly relate to the efficiency by which knowledge is obtained. SemNet is shown to have the specified properties thus distinguishing it (in tc'rms of eIli('iency) ms a. suitable representation for large scale NI,E. 1 I n t r o d u c t i o n Natural Language Engineering (LRE, 1992) (Smith, 1995) is a more pragmatic approach to Natural Langnage Processing than traditional Computat ional Linguistics. It involves seeking a large scale solution to NLP by applying engineering principles to utilise all awfilable resources. q'his is in contrast to trying to scale up domain specific applications, or by first at tempting to obtain a general theory of language. A core problenr for NLE is the design of the internal representation. An ideal representation should have several features includ ing: rich expressiveness, readability, cI[icient storage/retrieval of inf'ormation. Semantic networks have long been recognised as having the potential to ['nlfil many of these requirements. This paper introduces two new criteria for semantic networks distribut, edness and n o n l i n e a r i t y and tl,argc_scalc, Objectd)ascd, Linguistic lnl, cracl,or, ~l%anslat, or, and Analyser discusses their relevance to NLE. They are particularty relevant in large networks where search etliciency is vital to real-time system operation. The large scale NLE system I,OLITA (Long, 1993) (Smith, 1995) has been designed and implemented Ibllowing an NLE methodology. Its internal representation, SemNet, is a semantic network satisfying the above t%atures. The system analyses complex text, and expresses its meaning in SemNet. '['his information can then be used to perform reasoning, information retriew~l, or translation. Knowledge held in the network can be ex-pressed for users by generating natm'al language @ore SernNet. The fmrdamcntal principle of Semantic Networks is that inlbrmation is stored as nodes ~md ares, which represent concepts and relationships respectively. Within this framework a wide variety of networks exist, e.g. K I,-ONE based systems (Woods, 1992), SNePS/ANALOG (All, 1993), and (?oneeptual Graph 'Cheery (Sowa, 198/1). I)i-. reel comparison with these would not be justified as each has bec'n designed with different objectires. Ilowever, the paper does discuss aspects o[' @ese representations in order to highlight dit[~rcnces and why the authors believe ScmNet is a powerfifl (with respect to search) representation for large scale NLE. The rest of this paper is organised as ['of lows. Section 2 introduces distribntedncss and non-linearity as criteria ['or judging networks amd explains their significance for NLE. Section 3 describes the core of SemNet. Section 4 discusses the distrilmtedness and nonqinearity of SemNet and some other well known network representations. Section 5 draws conclusions. 2 D i s t r i b u t e d n e s s and N o n L i n e a r i t y A synta.ctic representation will have a semantic model. The degree to whieh such a representation