Using Instance Texts to Improve Keyword-Based Class Retrieval

In this paper we investigate the keyword based class retrieval problem, which we define as how to identify ontological classes that best match a keyword based query. Most previous applications use simple syntactic matching approaches on the class labels and/or comments, or expand the keyword query by using lexicons such as Word Net, but fail to retrieve relevant resources in many scenarios. Instead of relying on external sources, we investigate this problem by using the annotations of instances associated with classes in the knowledge base. We propose a general framework of this approach, which consists of two phases: the keyword query is first used to locate relevant instances, then we induce the classes given this list of weighted matched instances. If we identify sufficient text for the instances, then the first phase can be solved by a traditional information retrieval (IR) query, however the second phase might be cast in different ways: as an additive value function, as an IR problem with instance as queries, or as an instance-based ontology alignment problem. With many applicable strategies initiated from different viewpoints, we find that some of them are mathematically equivalent or very similar. In the experiments we compare our proposed framework to simple syntactic approaches and evaluate different strategies.