Modern Web based GIS systems have responded significantly to semantic Web technology as it offers opportunities to overcome interoperability and integration problems. There are abundant needs especially for the systems intending to provide more than just a map with basic geographical information. More sophisticated systems can offer more than navigation services and can integrate with several data sources, thereby providing a richer, wider and highly usable information service to be used in business, governmental and different life domains. Search is an essential part of any GIS system because of the huge amount of data representing different meanings that are stored in one or distributed data sources. A model is presented which focuses on searching for geospatial information to answer query semantics rather than query syntax. This model used the most recent and approved standards among the semantic Web communities, and was applied on TerraFly a GIS system. Since ranking is a critical factor in measuring the quality of any search engine, a ranking algorithm is also proposed and evaluated.

Approved for public release; distribution is unlimited / Recent military operations have redefined the way modern warfare is waged. In a deliberate effort to achieve and retain information dominance and decision superiority, many innovative technologies have emerged to assist the human war fighter. Unquestionably, these technologies have generated resounding successes on the battlefield, the likes of which have never been seen. With all the success, however, there are still areas for improvement as the potential exists for further reducing already short sensor-to-shooter times. The current World Wide Web (WWW) is largely a human-centric information space where humans exchange and interpret data ([2] Berners-Lee, 1, 1999). The Semantic Web (SWEB) is not a separate Web, but an extension of the current one in which content is given well-defined meaning, better enabling computers and people to work in cooperation (Berners-Lee et al). The result is the availability of the various backgrounds, experiences, and abilities of the contributing communities through the self-describing content populating the SWEB ([2] Berners-Lee, 1999). This thesis assesses current SWEB technologies that promise to make disparate data sources machine interpretable for use in the construction of actionable knowledge with the intent of further reducing sensor-to-shooter times. The adoption of the SWEB will quietly be realized and soon machines will prove to be of greater value to war fighting. When machines are able to interpret and process content before human interaction and analysis begins, their value will be further realized. This off-loading, or delegation, will produce faster sensor-to-shooter times and assist in achieving the speed required to achieve victory on any battlefield. / Lieutenant, United States Navy / Major, United States Army

Thesis (M.S. in Information Technology Management)--Naval Postgraduate School, September 2003. / Thesis advisor(s): Alexander Bordetsky, Douglas P. Homer. Includes bibliographical references (p. 255-262). Also available online.

Thesis (M.A.)--California State University Channel Islands, 2007. / Submitted in partial fulfillment of the requirements for the degree of Masters Of Science in Computer Science. Title from PDF t.p. (viewed October 22, 2009).

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Querying a database to retrieve an answer, telling a robot to perform an action, or teaching a computer to play a game are tasks requiring communication with machines in a language interpretable by them. Semantic parsing is the task of converting human language to a machine interpretable language. While human languages are sequential in nature with latent structures, machine interpretable languages are formal with explicit structures. The computational linguistics community have created several treebanks to understand the formal syntactic structures of human languages. In this thesis, we use these to obtain formal meaning representations of languages, and learn computational models to convert these meaning representations to the target machine representation. Our goal is to evaluate if existing treebank syntactic representations are useful for semantic parsing. Existing semantic parsing methods mainly learn domain-specific grammars which can parse human languages to machine representation directly. We deviate from this trend and make use of general-purpose syntactic grammar to help in semantic parsing. We use two syntactic representations: Combinatory Categorial Grammar (CCG) and dependency syntax. CCG has a well established theory on deriving meaning representations from its syntactic derivations. But there are no CCG treebanks for many languages since these are difficult to annotate. In contrast, dependencies are easy to annotate and have many treebanks. However, dependencies do not have a well established theory for deriving meaning representations. In this thesis, we propose novel theories for deriving meaning representations from dependencies. Our evaluation task is question answering on a knowledge base. Given a question, our goal is to answer it on the knowledge base by converting the question to an executable query. We use Freebase, the knowledge source behind Google’s search engine, as our knowledge base. Freebase contains millions of real world facts represented in a graphical format. Inspired from the Freebase structure, we formulate semantic parsing as a graph matching problem, i.e., given a natural language sentence, we convert it into a graph structure from the meaning representation obtained from syntax, and find the subgraph of Freebase that best matches the natural language graph. Our experiments on Free917, WebQuestions and GraphQuestions semantic parsing datasets conclude that general-purpose syntax is more useful for semantic parsing than induced task-specific syntax and syntax-agnostic representations.

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