Client service capability matching

In order to tailor web-content to the requirements of a device, it is necessary to access information about the attributes of both the device and the web content Profiles containing such information from heterogeneous sources may use many different terms to represent the same concept (eg Resolution/Screen_Res/Res). This can present problems for applications which try to interpret the semantics of these terms In this thesis, we present an architecture which, when given profiles describing a device and web service, can identify terms that are present in an ontology of recognised terms in the domain of device capabilities and web service requirements The architecture can semi-automatically identify unknown terms by combining the results of several schemamatching applications. The ontology can be expanded based on end-user’s interaction with the semi-automatic matchers and thus over time the application’s ontology will grow to include previously unknown terms

TRC-Matcher and enhanced TRC-Matcher. New Tools for Automatic XML Schema Matching

Siirretty Doriast

Accelerating Innovation Through Analogy Mining

The availability of large idea repositories (e.g., the U.S. patent database) could significantly accelerate innovation and discovery by providing people with inspiration from solutions to analogous problems. However, finding useful analogies in these large, messy, real-world repositories remains a persistent challenge for either human or automated methods. Previous approaches include costly hand-created databases that have high relational structure (e.g., predicate calculus representations) but are very sparse. Simpler machine-learning/information-retrieval similarity metrics can scale to large, natural-language datasets, but struggle to account for structural similarity, which is central to analogy. In this paper we explore the viability and value of learning simpler structural representations, specifically, "problem schemas", which specify the purpose of a product and the mechanisms by which it achieves that purpose. Our approach combines crowdsourcing and recurrent neural networks to extract purpose and mechanism vector representations from product descriptions. We demonstrate that these learned vectors allow us to find analogies with higher precision and recall than traditional information-retrieval methods. In an ideation experiment, analogies retrieved by our models significantly increased people's likelihood of generating creative ideas compared to analogies retrieved by traditional methods. Our results suggest a promising approach to enabling computational analogy at scale is to learn and leverage weaker structural representations.Comment: KDD 201