FICS 2010

International audienceInformal proceedings of the 7th workshop on Fixed Points in Computer Science (FICS 2010), held in Brno, 21-22 August 201

Symbol–Relation Grammars: A Formalism for Graphical Languages

AbstractA common approach to the formal description of pictorial and visual languages makes use of formal grammars and rewriting mechanisms. The present paper is concerned with the formalism of Symbol–Relation Grammars (SR grammars, for short). Each sentence in an SR language is composed of a set of symbol occurrences representing visual elementary objects, which are related through a set of binary relational items. The main feature of SR grammars is the uniform way they use context-free productions to rewrite symbol occurrences as well as relation items. The clearness and uniformity of the derivation process for SR grammars allow the extension of well-established techniques of syntactic and semantic analysis to the case of SR grammars. The paper provides an accurate analysis of the derivation mechanism and the expressive power of the SR formalism. This is necessary to fully exploit the capabilities of the model. The most meaningful features of SR grammars as well as their generative power are compared with those of well-known graph grammar families. In spite of their structural simplicity, variations of SR grammars have a generative power comparable with that of expressive classes of graph grammars, such as the edNCE and the N-edNCE classes

UGURU: a natural language UNIX consultant

UGURU is a natural language conversation program, implemented in Prolog, which can manage a wide knowledge base of facts about Unix. The range and wording of questions that it understands are based on surveys taken of students, mostly Unix beginners. UGURU is also designed to accept statements in English that can be added as facts to the knowledge base. Each fact is represented as a binding set: a verb-oriented semantic net with the characteristics of directed acyclic graphs. The main actions taken by UGURU are divided between two primary modules, a parser and a retriever. To produce a binding set from an input, the parser incorporates a new kind of object-oriented grammar of several levels, parallel tracing of distinct parse trees by independent units called recognizers, the concurrent use of both syntactic and semantic knowledge, and a pragmatic criterion that requires the system to mimic the sequence of human parsing. The retriever, invoked to answer input questions, seeks to match the binding set representing the question to a fact in the knowledge base by performing semantic transformations on the two sets