Generating language from conceptual graphs

Conceptual Structures in Practice, 2009

A conceptual graph (CG) is a graph representation for logic based on the semantic networks of artificial intelligence and the existential graphs of Charles Sanders Peirce. CG design principles emphasize the requirements for a cognitive representation: a smooth mapping to and from natural languages; an "iconic" structure for representing patterns of percepts in visual and tactile imagery; and cognitively realistic operations for perception, reasoning, and language understanding. The regularity and simplicity of the graph structures also support efficient algorithms for searching, pattern matching, and reasoning. Different subsets of conceptual graphs have different levels of expressive power: the ISO standard conceptual graphs express the full semantics of Common Logic (CL), which includes the subset used for the Semantic Web languages; a larger CG subset adds a context notation to support metalanguage and modality; and the research CGs are exploring an open-ended variety of extensions for aspects of natural language semantics. Unlike most notations for logic, CGs can be used with a continuous range of precision: at the formal end, they are equivalent to classical logic; but CGs can also be used in looser, less disciplined ways that can accommodate the vagueness and ambiguity of natural languages. This chapter surveys the history of conceptual graphs, their relationship to other knowledge representation languages, and their use in the design and implementation of intelligent systems.

Applications of Graph Transformations with Industrial Relevance, 2012

A graph grammar is a generative description of a graph language (a possibly infinite set of graphs). In this paper, we present a novel algorithm for inducing a graph grammar from a given set of 'positive' and 'negative' graphs. The algorithm is guaranteed to produce a grammar that can generate all of the positive and none of the negative input graphs. Driven by a heuristic specific-to-general search process, the algorithm tries to find a small grammar that generalizes beyond the positive input set. During the search, the algorithm employs a graph grammar parser to eliminate the candidate grammars that can generate at least one negative input graph. We validate our method by inducing grammars for chemical structural formulas and flowcharts and thereby show its potential applicability to chemical engineering and visual programming.

Computers & Mathematics with Applications, 1992

Conceptual graphs are a knowledge representation language designed as a synthesis of several different traditions. First are the semantic networks, which have been used in machine translation and computational linguistics for over thirty years. Second are the logic-based techniques of unification, lambda calculus, and Peirce's existential graphs. Third is the linguistic research based on Tosni~re's dependency graphs and various forms of case grammar and thematic relations. Fourth are the dataflow diagrams and Petri nets, which provide a computational me,~hani,m for relating conceptual graphs to external procedures and databases. The result is a highly expressive system of logic with a direct mapping to and from natural languages. The lambda calculus supports the definitions for a taxonomic system and provides a general mecha~m for restructuring knowledge bases. With the definitional mechanisms, conceptual graphs can be used as an intermediate stage between natural languages and the rules and frames of expert systems-an important feature for knowledge acquisition and for help and exphaatious. During the past five years, conceptual graphs have been applied to almost every aspect of AI, ranging from expert systems and natural langm~e to computer vision and neural networks. This paper surveys conceptual graphs, their development from each of these traditions, and the applications based on them.

1979

Conceptual graphs are both a language for representing knowledge and patterns for constructing models. They form models in the AI sense of structures that approximate some actual or possible system in the real world. They also form models in the logical sense of structures for which some set of axioms are true. When combined with recent developments in nonstandard logic and semantics, conceptual graphs can form a bridge between heuristic techniques of AI and formal techniques of model theory.

2008

In this paper we describe our progress towards building an Interlingua based machine transla- tion system, by capturing the semantics of the source language sentences in the form of Uni- versal Networking Language (UNL) graphs from which the target language sentences can be produced. There are two stages to the UNL graph generation: first, the conceptual argu- ments of a situation are identified in the form of semantically relatable sequences (SRS) which are potential candidates for linking with semantic relations; next, the conceptual rela- tions such as instrument, source, goal, reason or agent are recognized, irrespective of their different syntactic configurations. The system has been tested against gold standard UNL expressions collected from various sources like Oxford Advanced Learners' Dictionary, XTAG corpus and Framenet corpus. Results indicate the promise and effectiveness of our approach on the difficult task of interlingua generation from text.

2014

In order to help computational linguists, we have conceived and developed a linguistic software engineering environment, whose goal is to set up reusable and evolutive toolkits for natural language processing. This environment is based on a set of natural language processing components, at the morphologic, syntactic and semantic levels. These components are generic and evolutive, and can be used separately or with specific problem solving units in global strategies built for man-machine communication (according to the general model developed in the Language and Cognition group: Caramel). All these tools are complemented with graphic interfaces, allowing users outside the field of Computer Science to use them very easily. In this paper, we will present first the syntactic analysis, based on a chart parser that uses a LFG grammar for French, and the semantic analysis, based on conceptual graphs. Then we will show how these two analyses collaborate to produce semantic representations a...

2007

Abstract This paper presents a Conceptual Graph (CG) framework to the Generation of Referring Expressions (GRE). Employing Conceptual Graphs as the underlying formalism allows a rigorous, semantically rich, approach to GRE. A number of advantages over existing work are discussed. The new framework is also used to revisit existing complexity results in a fully rigorous way, showing that the expressive power of CGs does not increase the theoretical complexity of GRE.

Proceedings of the 17th annual meeting on Association for Computational Linguistics -, 1979

The augmented transition network (ATN) is a formalism for writing parsing grammars that has been much used in Artificial Intelligence and Computational Linguistics. A few researchers have also used ATNs for writing grammars for generating sentences. Previously, however, either generation ATNs did not have the same semantics as parsing ATNs, or they required an auxiliary mechanism to determine the syntactic structure of the sentence to be generated. This paper reports a generalization of the ATN formalism that allows ATN grammars to be written to parse labelled directed graphs. Specifically, an ATN grammar can be written to parse a semantic network and generate a surface string as its analysis. An example is given of a combined parsing-generating grammar that parses surface sentences, builds and queries a semantic network knowledge representation, and generates surface sentences in response. The method also assumes that semantic nodes have such syntactic information stored with them as number and definiteness of nominals, and tense, aspect, mood, and voice of propositions.

Lecture Notes in Computer Science, 2000

We study Sowa's conceptual graphs (CGs) with both existential and universal quantifiers. We explore in detail the existential fragment. We extend and modify Sowa's original graph derivation system with new rules and prove the soundness and completeness theorem with respect to Sowa's standard interpretation of CGs into first order logic (FOL). The proof is obtained by reducing the graph derivation to a question-answering problem. The graph derivation can be equivalently obtained by querying a Definite Horn Clauses program by a conjunction of positive atoms. Moreover, the proof provides an algorithm for graph derivation in a pure proof-theoretic fashion, namely by means of a slight enhancement of the standard PROLOG interpreter. The graph derivation can be rebuilt step-by-step and constructively from the resolution-based proof. We provide a notion of CGs in normal form (the table of the conceptual graph) and show that the PROLOG interpreter also gives a projection algorithm between normal CGs. The normal forms are obtained by extending the FOL language by witnesses (new constants) and extending the graph derivation system. By applying iteratively a set of rules the reduction process terminates with the normal form of a conceptual graph. We also show that graph derivation can be reduced to a question-answering problem in propositional datalog for a subclass of simple CGs. The embedding into propositional datalog makes the complexity of the derivation polynomial.

1997

In order to help computational linguists, we have conceived and developed a linguistic software engineering environment, whose goal is to set up reusable and evolutive toolkits for natural language processing. This environment is based on a set of natural language processing components, at the morphologic, syntactic and semantic levels. These components are generic and evolutive, and can be used separately or with specific problem solving units in global strategies built for manmachine communication (according to the general model developed in the Language and Cognition group: Caramel). All these tools are complemented with graphic interfaces, allowing users outside the field of Computer Science to use them very easily. In this paper, we will present first the syntactic analysis, based on a chart parser that uses a LFG grammar for French, and the semantic analysis, based on conceptual graphs. Then we will show how these two analyses collaborate to produce semantic representations an...