Show simple item record

dc.contributor.advisorLeite, Fernanda
dc.creatorSeedah, Dan Paapanyin Kofien
dc.date.accessioned2015-02-09T14:43:17Zen
dc.date.issued2014-12en
dc.date.submittedDecember 2014en
dc.identifier.urihttp://hdl.handle.net/2152/28341en
dc.descriptiontexten
dc.description.abstractThe ability to retrieve accurate information from databases without an extensive knowledge of the contents and organization of each database is extremely beneficial to the dissemination and utilization of freight data. The challenges, however, are: 1) correctly identifying only the relevant information and keywords from questions when dealing with multiple sentence structures, and 2) automatically retrieving, preprocessing, and understanding multiple data sources to determine the best answer to user’s query. Current named entity recognition systems have the ability to identify entities but require an annotated corpus for training which in the field of transportation planning does not currently exist. A hybrid approach which combines multiple models to classify specific named entities was therefore proposed as an alternative. The retrieval and classification of freight related keywords facilitated the process of finding which databases are capable of answering a question. Values in data dictionaries can be queried by mapping keywords to data element fields in various freight databases using ontologies. A number of challenges still arise as a result of different entities sharing the same names, the same entity having multiple names, and differences in classification systems. Dealing with ambiguities is required to accurately determine which database provides the best answer from the list of applicable sources. This dissertation 1) develops an approach to identify and classifying keywords from freight related natural language queries, 2) develops a standardized knowledge representation of freight data sources using an ontology that both computer systems and domain experts can utilize to identify relevant freight data sources, and 3) provides recommendations for addressing ambiguities in freight related named entities. Finally, the use of knowledge base expert systems to intelligently sift through data sources to determine which ones provide the best answer to a user’s question is proposed.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.subjectFreight dataen
dc.subjectHeterogeneous data sourcesen
dc.subjectFreight ontologyen
dc.subjectNatural language processingen
dc.subjectAmbiguityen
dc.subjectDisambiguationen
dc.subjectKnowledge systemsen
dc.titleRetrieving information from heterogeneous freight data sources to answer natural language queriesen
dc.typeThesisen
dc.date.updated2015-02-09T14:43:18Zen
dc.description.departmentCivil, Architectural, and Environmental Engineeringen
thesis.degree.departmentCivil, Architectural, and Environmental Engineeringen
thesis.degree.disciplineCivil Engineeringen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelDoctoralen
thesis.degree.nameDoctor of Philosophyen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record