Reymonrod G. Vasquez

Contextual ontology alignment of LOD with an upper ontology: a case study with proton

The Linked Open Data (LOD) is a major milestone towards realizing the Semantic Web vision, and can enable applications such as robust Question Answering (QA) systems that can answer queries requiring multiple, disparate information sources. However, realizing these applications requires relationships at both the schema and instance level, but currently the LOD only provides relationships for the latter. To address this limitation, we present a solution for automatically finding schema-level links between two LOD ontologies - in the sense of ontology alignment. Our solution, called BLOOMS+, extends our previous solution (i.e. BLOOMS) in two significant ways. BLOOMS+ 1) uses a more sophisticated metric to determine which classes between two ontologies to align, and 2) considers contextual information to further support (or reject) an alignment. We present a comprehensive evaluation of our solution using schema-level mappings from LOD ontologies to Proton (an upper level ontology) - created manually by human experts for a real world application called FactForge. We show that our solution performed well on this task. We also show that our solution significantly outperformed existing ontology alignment solutions (including our previously published work on BLOOMS) on this same task.

Automated review of natural language requirements documents: generating useful warnings with user-extensible glossaries driving a simple state machine

We present an approach to automating some of the quality assurance review of software requirements documents, and promoting best practices for requirements documentation. The system we describe -- the Requirements Analysis Tool (RAT) - has been deployed and is currently being used in pilot projects with large and complex requirements documents. Preliminary results indicate a reduction in time needed to review documents and reduction in requirements defects as well as a change in the way users think about writing requirements. Our approach allows users to write requirements in natural language instead of an artificial formalism. RAT enforces requirements documentation best practices such as using standardized syntaxes and internally-consistent use of terminology. It supports the use of user-extensible glossaries to define terms. The formalism driving RAT is a state-machine, which is used to classify requirements into types based on keywords and then verify that the requirements follow one of the best practice syntaxes supported by the tool. It generates helpful warning messages explaining where requirements are not following best practices and suggests ways to rectify.

Using syntactic and semantic analyses to improve the quality of requirements documentation

We discuss our experiences with deploying a tool called the Requirements Analysis Tool (RAT), which automatically reviews requirements documents for clarity and content based issues using a variety of syntactic and semantic techniques. The tool has been deployed at over 500 large software projects. We provide an overview of our syntactic approach, which is based on enforcing restrictions on both sentence structure and vocabulary in a way that is carefully chosen to align with best practices. We discuss how RAT analyzes natural language text to find defects such as terminological inconsistencies and missing contextual information. Structured content from requirements is then represented as a semantic graph and RAT performs semantic analysis to help users perform interaction analysis. We present a number of case studies based on real world deployments of RAT which demonstrate number of improvements in the projects’ requirements ranging from clearer sentence structure to more complete requirements.