When humans perform inductive learning, they often enhance the process with background knowledge. With the increasing availability of well-formed collaborative knowledge bases, the performance of learning algorithms could be significantly enhanced if a way were found to exploit these knowledge bases. In this work, we present a novel algorithm for injecting external knowledge into induction algorithms using feature generation. Given a feature, the algorithm defines a new learning task over its set of values, and uses the knowledge base to solve the constructed learning task. The resulting classifier is then used as a new feature for the original problem. We have applied our algorithm to the domain of text classification using large semantic knowledge bases. We have shown that the generated features significantly improve the performance of existing learning algorithms.
Propositionalization approaches rely on relational data to serve as external knowledge. They use several operators to create first-order logic predicates connecting existing data and relational knowledge. cheng2011automatedfull devised a generic propositionalization framework using linked data via relation-based queries. FeGeLOD @cite_12 also uses linked data to automatically enrich existing data. FeGeLOD uses feature values as entities and adds related knowledge to the example, thus creating additional features.
@cite_12
2087159174
The quality of the results of a data mining process strongly depends on the quality of the data it processes. A good result is more likely to obtain the more useful background knowledge there is in a dataset. In this paper, we present a fully automatic approach for enriching data with features that are derived from Linked Open Data, a very large, openly available data collection. We identify six different types of feature generators, which are implemented in our open-source tool FeGeLOD. In four case studies, we show that our approach can be applied to different problems, ranging from classical data mining to ontology learning and ontology matching on the semantic web. The results show that features generated from publicly available information may allow data mining in problems where features are not available at all, as well as help improving the results for tasks where some features are already available.