The role of ontologies in creating and maintaining corporate knowledge: a case study from the aero industry

The Designers’ Workbench is a system, developed to support designers in large organizations, such as Rolls-Royce, by making sure that the design is consistent with the specification for the particular design as well as with the company’s design rule book(s). The evolving design is described against a jet engine ontology. Currently, to capture the constraint information, a domain expert (design engineer) has to work with a knowledge engineer to identify the constraints, and it is then the task of the knowledge engineer to encode these into the Workbench’s knowledge base (KB). This is an error prone and time consuming task. It is highly desirable to relieve the knowledge engineer of this task, and so we have developed a tool, ConEditor+ that enables domain experts themselves to capture and maintain these constraints. The tool allows the user to combine selected entities from the domain ontology with keywords and operators of a constraint language to form a constraint expression. Further, we hypothesize that to apply constraints appropriately, it is necessary to understand the context in which each constraint is applicable. We refer to this as “application conditions”. We show that an explicit representation of application conditions, in a machine interpretable format, along with the constraints and the domain ontology can be used to support the verification and maintenance of constraints

Initial Experiments with TPTP-style Automated Theorem Provers on ACL2 Problems

This paper reports our initial experiments with using external ATP on some corpora built with the ACL2 system. This is intended to provide the first estimate about the usefulness of such external reasoning and AI systems for solving ACL2 problems.Comment: In Proceedings ACL2 2014, arXiv:1406.123

ENIGMA: Efficient Learning-based Inference Guiding Machine

ENIGMA is a learning-based method for guiding given clause selection in saturation-based theorem provers. Clauses from many proof searches are classified as positive and negative based on their participation in the proofs. An efficient classification model is trained on this data, using fast feature-based characterization of the clauses . The learned model is then tightly linked with the core prover and used as a basis of a new parameterized evaluation heuristic that provides fast ranking of all generated clauses. The approach is evaluated on the E prover and the CASC 2016 AIM benchmark, showing a large increase of E's performance.Comment: Submitted to LPAR 201