Distributedness and Non-Linearity of LOLITA’s Semantic Network

Distributedness and Non-Linearity of LOLITA's Semantic Network Short S., Shiu S., Garigliano R. Laboratory ['or N~tural Language Engineering School of Computer Science University of D urh am South Road l)urham DH1 3LE, United Kingdom. sengan, shortOdurham, ac. uk Abstract 'Phis paper describes SemNet the in- ternal Knowledge t{epresentation lbr LOLI'I'A I . LOMTA is a large scale Nat- ural Language Engineering (NLE) sys- tem. As such the internal represen- tatiou must be richly expressive, mttu- ral (with respect to Natural Language), and e[[icient. In network representa- tions knowledge is gleaned by travers- ing the graph. The paper introduces two I)rol)crties, (distributedness and non- linearity) of networks which directly re- late to the efficiency by which knowledge is obtained. SemNet is shown to have the specified properties thus distinguish- ing it (in tc'rms of eIli('iency) ms a. suitable representation for large scale NI,E. 1 Introduction Natural Language Engineering (LRE, 1992) (Smith, 1995) is a more pragmatic approach to Natural Langnage Processing than traditional Computational Linguistics. It involves seeking a large scale solution to NLP by applying engi- neering principles to utilise all awfilable resources. q'his is in contrast to trying to scale up domain specific applications, or by first attempting to ob- tain a general theory of language. A core problenr for NLE is the design of the internal representation. An ideal repre- sentation should have several features includ ing: rich expressiveness, readability, cI[icient stor- age/retrieval of inf'ormation. Semantic networks have long been recognised as having the poten- tial to ['nlfil many of these requirements. This pa- per introduces two new criteria for semantic net- works distribut, edness and non-linearity and tl,argc_scalc, Objectd)ascd, Linguistic lnl, cracl,or, ~l%anslat, or, and Analyser discusses their relevance to NLE. They are par- ticularty 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 im- plemented Ibllowing an NLE methodology. Its in- ternal representation, SemNet, is a semantic net- work 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 trans- lation. Knowledge held in the network can be ex-- pressed for users by generating natm'al language @ore SernNet. The fmrdamcntal principle of Semantic Net- works is that inlbrmation is stored as nodes ~md ares, which represent concepts and relationships respectively. Within this framework a wide vari- ety 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 objec- tires. Ilowever, the paper does discuss aspects o[' @ese representations in order to highlight dit[~r- cnces 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 …

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