In recent years, accelerators such as Graphics Processing Units (GPUs), Field Programmable Gate Arrays (FPGAs), and Intelligence Processing Units (IPUs) have played a dominant role in speeding up workflows in science and engineering. They provide significant increases in computing power, but scientists need to learn how to effectively use these accelerators. To assist researchers, a training framework, called “Technology Laboratories”, was developed. The Technology Laboratories approach builds on situated learning and constructivism by introducing researchers to new computing concepts by scaffolding with existing knowledge of computing resources like GPUs and Jupyter Notebooks. In this approach, researchers are introduced to the newer computing methods by employing accelerators and interactive technologies familiar to them. The first iteration of the training framework was used to teach researchers how to use different methods of artificial intelligence and machine learning (AI/ML) through exercises built on familiar computing environments. New accelerator technologies like IPUs have focused on employing these AI/ML techniques, presenting an opportunity to now teach new computing accelerators using AI/ML models as the familiar computing method. Lave and Wenger's model of situated learning through a community of practice is appropriate for this learning. Additionally, Jerome Bruner's constructivist theory is part of the model as researchers are also required to construct their own knowledge about transforming their code and learning to adapt to a new type of hardware as they work through the individual characteristics of their code and research area. In this paper, we demonstrate the success of our training framework in using AI/ML methods to teach researchers about IPUs. We attracted interested participants from all over the United States, and they were able to complete hands-on exercises and run code on IPUs. We also had in-depth discussions with participants about the applications that are suitable for use with IPUs. By providing researchers with the skills and knowledge they need to use IPUs effectively, we are helping to advance scientific research and engineering workflows. Overall, our training framework provides a comprehensive and effective solution for researchers who want to take advantage of the power of novel accelerators in their work.

In many data-centric semantic web applications, it is desirable to use OWL to encode the Integrity Constraints (IC) that must be satisfied by instance data. However, challenges arise due to the Open World Assumption (OWA) and the lack of a Unique Name Assumption (UNA) in OWL’s standard semantics. In particular, conditions that trigger constraint violations in systems using the ClosedWorld Assumption (CWA), will generate new inferences in standard OWL-based reasoning applications. In this paper, we present an alternative IC semantics for OWL that allows applications to work with the CWA and the weak UNA. Ontology modelers can choose which OWL axioms to be interpreted with our IC semantics. Thus application developers are able to combine open world reasoning with closed world constraint validation in a flexible way. We also show that IC validation can be reduced to query answering under certain conditions. Finally, we describe our prototype implementation based on the OWL reasoner Pellet.

Many emerging semantic web applications include ontologies from one set of authors and instance data from another (often much larger) set of authors. Often ontologies are reused and instance data is integrated in manners unanticipated by their authors. Not surprisingly, many instance data rich applications encounter instance data that is not compatible with the expectations of the original ontology author(s). This line of work focuses on issues related to semantic expectation mismatches in instance data. Our initial results include a customizable and extensible service-oriented evaluation architecture, and a domain implementation called PmlValidator, which checks instance data using the corresponding ontologies and additional style requirements.