This STTR Phase II project aims to produce a software application that dramatically improves a manager's ability to allocate resources to productive uses. With advances in Online Analytical Processing (OLAP) and ontology technology, the tool has the potential to enable the discovery of future supply and demand imbalances for teams of business analysts. The objective is to produce at least one Investable Inconsistency per day by the end of the research period.
The Phase I project produced unanticipated innovations that may have broad utility in both the OLAP field and the ontology field, and with these innovations, the software platform shows promise for transforming the essential practice of analysis in the field of market research in support of investment decisions. The Phase II project, if successful will result in technology that extends this promise to a broad audience, educating users in best practices for investment analysis and enabling them to materially improve their allocation of resources.
This STTR project built on academic research in ontology engineering to produce a graph database-driven software application for structured note-taking. The application uses logical inference to automate compilation of comparisons of interrelated assumptions in investment research. These comparisons may enable investors to anticipate more imbalances in supply and demand and rectify them with investments, and this application is currently supporting professional investment management services. This structured note-taking software also advanced the state of the art in ontology authoring by improving tabular manipulation of higher arity and cardinality in general graphs and ontologies. It may prove useful for any comparison along multiple attributes of items in a graph. In addition to this graph database-driven software application for structured note-taking, the STTR project also produced tools for mapping content from two-dimensional spreadsheet tables to ontologies expressed in the Web Ontology Language (OWL). We developed a mapping language based on the Manchester OWL Syntax,i whose main goal is to transform each tuple found in the semi-structured table to an instance of an OWL class whose properties are defined by the tuple’s attributes (i.e., its row and column header values). Please refer to our publications related to this work for a more technical discussion of the mapping language.ii,iii We leveraged this language, as well as the plug-in architecture of Stanford’s Protégé ontology authoring environment, to create a new plug-in that enables mapping from two-dimensional spreadsheet tables to OWL ontologies being authored in the Protege environment. This plug-in, called MappingMaster, is general-purpose and has been released to the public as part of the standard Protégé open-source distribution.iv i Manchester OWL Syntax: www.w3.org/TR/owl2-manchester-syntax/ ii M. J. O'Connor, C. Halaschek-Wiener, M. A. Musen. 9th International Semantic Web Conference (ISWC), Shanghai, China, Springer-Verlag, 6497, 194-208. Published in 2010. iii Martin J. O’Connor, Christian Halaschek-Wiener, Mark A. Musen. M2: a Language for Mapping Spreadsheets to OWL OWL: Experiences and Directions (OWLED), Sixth International Workshop, San Francisco, CA. 2010. iv MappingMaster Protégé Plug-in Homepage: http://protege.cim3.net/cgi-bin/wiki.pl?MappingMaster
