Research On The Key Techniques Of Semantic Networks Based Knowledge Collaboration

Semantic Networks(SN)is a powerful knowledge representing and processing method.This thesis studies three tightly interrelated aspects of knowledge collaboration based on semantic networks:(a)SN based knowledge representation,(b) SN based knowledge processing,(c)inputing and outpuing between semantic networks and natural language.This thesis made following contributions:Firstly,the semantic networks based complex knowledge representation is studied.The types of knowledge were analyzed,and a categorization of knowledge is proposed.A new extension of semantic networks,called Abstract Semantic Network (ASN)is proposed.ASN provides an abstracting mapping between a subnetwork of a SN and a node of that SN.ASN can be used to represent complex knowledge types and relations,such as multi-view knowledge and inconsitant knowledge, transformational knowledge and procedural knowledge.Base on ASN,the graph transformation on semantic networks is inspected.And a new ASN based graph grammar,called the Universal Graph Grammar(UGG),is proposed.The main improvement of UGG is that it can represent the conversion between nodes and edges during graph transformation.And an ASN and UGG based representation of reasoning rules is proposed for reasoning on semantic networks.Secondly,the semantic networks based knowledge processing is sdudied.A fast labeled graph matching algorithm,called Graph Explorer(GE)algorithm,is presented. GE algorithm can be categorized into the non-indexed tree searching algorithms of determinismic graph/subgraph matching.It converts graph matching problem into a path search problem in the space of states of partially matched subgraphs.It avoids repeated label checking by using state tree structure to caching and fast visiting matched nodes and edge.By a carefully optimized searching path,GE algorithm avoided invalid search states in great deal and improved the performance to almost linear to the number of edges of pattern graph when the ambiguity is low.It employs a dynamic state queue to overcome the stack overflow problem of recursive call of traditional graph matching algorithms based on tree searching.And it can handle extra large semantic network with size up to 10,000 nodes.The analaysis and experiment show that the performance of GE is better than performance of similar algorithms. The graph mathching algorithm is applied to the recognition,reasoning and query on semantic networks.And a knowledge merging method is proposed also.Thirdly,the natural language processing for semantic networks is studied.A new grammar,called the Semantic Networks Grammar(SNG),is proposed to provision and guide the natural language generation from semantic networks and natural language understanding to semantic networks.The SNG presents a model of semantic topology,mainly including a semantic star and a semantic tree,as the intermediate structure of meaning,and it presents a model of language structure to describe the internal structure of language.It defines a semantic pattern to serialize and deserialize a semantic star.It uses transformative production to serialize and deserialize a semantic tree.Fourthly,the process model and disambiguation of grammar parsing are studied. A so called Lattice Model,which comprises a Semantic Lattice and a Langugage Lattice,is proposed to describe the parsing process of structural grammars.After analysis of the root causes of grammar parsing ambiguities,a Hierarchical Classification(HC)approach is proposed to response to both the under-classification and over-classification problems.The HC method improved conventional Head-Driven PSG into Classified Phase Structure Grammar(HC-PSG)by replacing flat features space and flat rule set of PSG with a classification hierarchy and a hierarchical rule set.Then the conventional PCFG is upgraded to a Hierarchically Classified Probabilistic Context-Free Grammar(HC-PCFG)to provide basic disambiguation.HC-PCFG uses an approach of pattern cluster to resolve the ambiguous rules,uses a Maximum-Entropy Local Disambiguation to eliminate invalid branches as early as possible.The Hierarchical Classification(HC)method is extended to the context disambiguation.The result of experiments on Peen Chinese Treebank proved the effectiveness of the HC method.A Semantic Network Language Generation(SNLG)solution base on the SNG is proposed.The SNLG provides a trivialization procedure to convert an arbitrary semantic star into a trivial semantic tree to be serialized by SNG.During content planning,an improved distance-based context planning approach is proposed.For discourse planning,a trivialization time splitting method is presented to make well-formed sentence,and a splitting time aggregation method is proposed to improve the readability of sentence.As verification and demonstration of the theories and technologies of this thesis, finally,a semantic network middleware,called Knoware,had been implemented to overally apply above theories and technologies.A fully semantized semantic wiki system,called NaturalWiki,is implemented based on Knoware to verify and demonstrate all theories and methods of this thesis.