Question Paraphrase Generation for Question Answering System

The queries to a practical Question Answering (QA) system range from keywords, phrases, badly written questions, and occasionally grammatically perfect questions. Among different kinds of question analysis approaches, the pattern matching works well in analyzing such queries. It is costly to build this pattern matching module because tremendous manual labor is needed to expand its coverage to so many variations in natural language questions. This thesis proposes that the costly manual labor should be saved by the technique of paraphrase generation which can automatically generate semantically similar paraphrases of a natural language question. Previous approaches of paraphrase generation either require large scale of corpus and the dependency parser, or only deal with the relation-entity type of simple question queries. By introducing a method of inferring transformation operations between paraphrases, and a description of sentence structure, this thesis develops a paraphrase generation method and its implementation in Chinese with very limited amount of corpus. The evaluation results of this implementation show its ability to aid humans to efficiently create a pattern matching module for QA systems as it greatly outperforms the human editors in the coverage of natural language questions, with an acceptable precision in generated paraphrases

Data integration through service-based mediation for web-enabled information systems

The Web and its underlying platform technologies have often been used to integrate existing software and information systems. Traditional techniques for data representation and transformations between documents are not sufficient to support a flexible and maintainable data integration solution that meets the requirements of modern complex Web-enabled software and information systems. The difficulty arises from the high degree of complexity of data structures, for example in business and technology applications, and from the constant change of data and its representation. In the Web context, where the Web platform is used to integrate different organisations or software systems, additionally the problem of heterogeneity arises. We introduce a specific data integration solution for Web applications such as Web-enabled information systems. Our contribution is an integration technology framework for Web-enabled information systems comprising, firstly, a data integration technique based on the declarative specification of transformation rules and the construction of connectors that handle the integration and, secondly, a mediator architecture based on information services and the constructed connectors to handle the integration process

Retrieving information from heterogeneous freight data sources to answer natural language queries

textThe 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.Civil, Architectural, and Environmental Engineerin

The semantics of similarity in geographic information retrieval

Similarity measures have a long tradition in fields such as information retrieval artificial intelligence and cognitive science. Within the last years these measures have been extended and reused to measure semantic similarity; i.e. for comparing meanings rather than syntactic differences. Various measures for spatial applications have been developed but a solid foundation for answering what they measure; how they are best applied in information retrieval; which role contextual information plays; and how similarity values or rankings should be interpreted is still missing. It is therefore difficult to decide which measure should be used for a particular application or to compare results from different similarity theories. Based on a review of existing similarity measures we introduce a framework to specify the semantics of similarity. We discuss similarity-based information retrieval paradigms as well as their implementation in web-based user interfaces for geographic information retrieval to demonstrate the applicability of the framework. Finally we formulate open challenges for similarity research