Conceptual Graph Matching Research Articles

The intensional semantic conceptual graph matching algorithm based on conceptual sub-graph weight self-adjustment

Semantic computing is an important task in the research on natural language processing. On solving the problem of the inaccurate conceptual graph matching, this paper proposes an algorithm to compute the similarity of conceptual graphs, based on conceptual sub-graph weight self-adjustment. The algorithm works by basing itself on the intensional logic model of Chinese concept connotation, using intensional semantic conceptual graph as knowledge representation method and combining itself with the computation method of E-A-V structures. When computing the similarity of conceptual graphs, the algorithm can give the homologous weight to the sub-graph according to the proportion of how much information the sub-graph contains in the whole conceptual graph. Therefore, it can achieve better similarity results, which has also been proved in the experiments of this paper.

The intensional semantic conceptual graph matching algorithm based on conceptual sub-graph weight self-adjustment

The intensional semantic conceptual graph matching algorithm based on conceptual sub-graph weight self-adjustment

Illustrate It! An Arabic Multimedia Text-to-Picture m-Learning System

Multimedia learning is the process of building mental representation from words associated with images. Due to the intuitiveness and vividness of visual illustration, many texts to picture systems have been proposed. However, we observe some common limitations in the existing systems, such as the retrieved pictures may not be suitable for educational purposes. Also, finding pedagogic illustrations still requires manual work, which is difficult and time-consuming. The commonly used systems based on the best keyword selection and the best sentence selection may suffer from loss of information. In this paper, we present an Arabic multimedia text-to-picture mobile learning system that is based on conceptual graph matching. Using a knowledge base, a conceptual graph is built from the text accompanied with the pictures in the multimedia repository as well as for the text entered by the user. Based on the matching scores of both conceptual graphs, matched pictures are assigned relative rankings. The proposed system demonstrated its effectiveness in the domain of Arabic stories, however, it can be easily shifted to any educational domain to yield pedagogical illustrations for organizational or institutional needs. Comparisons with the current state-of-the-art systems, based on the best keyword selection and the best sentence selection techniques, have demonstrated significant improvements in the performance. In addition, to facilitate educational needs, conceptual graph visualization and visual illustrative assessment modules are also developed. The conceptual graph visualization enables learners to discover relationships between words, and the visual illustrative assessment allows the system to automatically assess the performance of a learner. The profound user studies demonstrated the efficiency of the proposed multimedia learning system.

Conceptual graph matching: a flexible algorithm and experiments

Abstract Graph matching is recognized as a central problem across a variety of application areas, and application-specific matchers have been developed with different simplifying assumptions to reduce the computational complexity. Graph matching is viewed as a form of plausible reasoning when conceptual information contained in graphs are considered, and thus requires an underlying algorithm flexible and general enough to accommodate application-specific matching heuristics and schemes that determine the degree of plausibility. This paper presents such an algorithm, based on the notion of association graphs, developed for matching Sowa's conceptual graphs (CG). While the general subgraph isomorphism problem is known to be NP-complete, matching graphs containing conceptual information appears to be computationally tractable. Following the detailed description of the algorithm, some experimental results are shown to discuss the time complexity of the algorithm and its practicality.