A scalable ontology reasoner via incremental materialization

Ontology based knowledge management systems have a lot of potential: their applicability ranges from artificial intelligence, e.g., for knowledge representation and natural language processing, to information integration and retrieval systems, requirements analysis, and, most lately, to semantic web applications and workflow management systems. However the huge complexity of reasoning for ontologies with large TBoxes and/or ABoxes is often a barrier to their applicability in real-world settings especially those which are time sensitive. Materialization is a promising solution for scalable reasoning over ontologies with large ABoxes as it derives the implicit knowledge of an ontology and makes it available in a relational database. Although materialization can reduce the query answering time of an ontology, it has limitations in applications which require frequent update to the knowledge base. To overcome this problem, we developed a tool for incremental materialization which identifies the fragment of the ontology that needs to be updated due to the ABox or TBox change, thereby reducing the complexity of the exhaustive forward chaining required.