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Decades of research confirm that interpretation and environmental education on public lands can accomplish a wide variety of positive outcomes for participants, ranging from personal learning and growth to stewardship behaviors both onand off-site. This research note offers a brief summary of the state-of-the-field of interpretation and environmental education research as applied to public lands. It highlights the general state of knowledge and identifies opportunities for researchers to further enhance our understanding about education on public lands to maximize benefits for visitors and managers alike. In particular, we emphasize the value of large-scale comparative studies as well as collaborative approaches to adaptive management, in which researchers support active experimentation through iterative data collection and analysis within a learning network of multiple program providers. This latter approach promotes evidenced-based learning within a larger community practice in which participants can benefit from the diverse knowledge, experiences, and data that each brings into the network. 

While OWL and RDF are by far the most popular logic-based languages for Semantic Web Ontologies, some well-designed ontologies are only available in languages with a much richer expressivity, such as first-order logic (FOL) or the ISO standard Common Logic. This inhibits reuse of these ontologies by the wider Semantic Web Community. While converting OWL ontologies to FOL is straightforward, the reverse problem of finding the closest OWL approximation of an FOL ontology is undecidable. However, for most practical purposes, a ``good enough'' OWL approximation need not be perfect to enable wider reuse by the Semantic Web Community. This paper outlines such a conversion approach by first normalizing FOL sentences into a function-free prenex conjunctive normal (FF-PCNF) that strips away minor syntactic differences and then applying a pattern-based approach to identify common OWL axioms. It is tested on the over 2,000 FOL ontologies from the Common Logic Ontology Repository.