Mining for Topics to Suggest Knowledge Model Extensions

LORENZETTI CARLOS MARTÍN; MAGUITMAN ANA GABRIELA; LEAKE DAVID; MENCZER FILLIPO; REICHHERZER THOMAS

ACM Transactions on Knowledge Discovery from Data

ACM

Año: 2016 vol. 11

1556-4681

Electronic concept maps, interlinked with other concept maps and multimedia resources, can provide rich knowledge models to capture and share human knowledge. This article presents and evaluates methods to support experts as they extend existing knowledge models, by suggesting new context-relevant topics mined from Web search engines. The task of generating topics to support knowledge model extension raises two research questions: first, how to extract topic descriptors and discriminators from concept maps; and second, how to use these topic descriptors and discriminators to identify candidate topics on the Web with the right balance of novelty and relevance. To address these questions, this article first develops the theoretical framework required for a "topic suggester" to aid information search in the context of a knowledge model under construction. It then presents and evaluates algorithms based on this framework and applied in EXTENDER, an implemented tool for topic suggestion. EXTENDER has been developed and tested within CmapTools, a widely used system for supporting knowledge modeling using concept maps. However, the generality of the algorithms makes them applicable to a broad class of knowledge modeling systems, and to Web search in general.