Open data standards (e.g. LandXML, TransXML) have been widely recognized as a solution to the interoperability issue in exchanging digital data in the transportation sector. Since these schemas include rich sets of data types covering a wide range of disciplines across all project phases, model view definitions (MVDs) which define subsets of a schema are required to specify what types of data to be shared in accordance with a specific exchange scenario. The traditional method for generating MVDs is time consuming and tedious as developers have to manually search for entities and attributes names that semantically match to the data exchange requirements. This paper presents a computational method that automatically maps users' keywords to semantics-equivalent data labels (classes and attributes) in LandXML data schema. The study employs a lexical database of civil engineering terms to interpret users' intention from their keywords. The study also introduces a context-aware entity search algorithm that is able to find equivalent or most similar entities for a given keyword. The developed method has been experimented on a set of keywords extracted from an asset management manual. The experiment results show that the design algorithm is successful in generating partial LandXML branches from keywords.
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