An approach to Automated Learning of Conceptual Graphs from Text

Many document collections are private and accessible only by selected people. Especially in business realities, such collections need to be managed, and the use of an external taxonomic or ontological resource would be very useful. Unfortunately, very often domain-specific resources are not available, and the development of techniques that do not rely on external resources becomes essential. Automated learning of conceptual graphs from restricted collections needs to be robust with respect to missing or partial knowledge, that does not allow to extract a full conceptual graph and only provides sparse fragments thereof. This work proposes a way to deal with these problems applying relational clustering and generalization methods. While clustering collects similar concepts, generalization provides additional nodes that can bridge separate pieces of the graph while expressing it at a higher level of abstraction. In this process, considering relational information allows a broader perspective in the similarity assessment for clustering, and ensures more flexible and understandable descriptions of the generalized concepts. The final conceptual graph can be used for better analyzing and understanding the collection, and for performing some kind of reasoning on it.