Abstract
Search has become a ubiquitous, everyday activity, but finding the right information at the right time in an electronic document collection can still be a very challenging process. Significant time is being spent on identifying suitable search terms, exploring matching documents, rephrasing the search request and assessing whether a document contains the information sought. Once another user is ...
Abstract
Search has become a ubiquitous, everyday activity, but finding the right information at the right time in an electronic document collection can still be a very challenging process. Significant time is being spent on identifying suitable search terms, exploring matching documents, rephrasing the search request and assessing whether a document contains the information sought. Once another user is faced with a similar information need, the whole process starts again. There is significant potential in cutting down on this activity by taking a user straight to the required information. As well as delivering technical information and vital regulatory information, a knowledge management solution is concerned with capturing valuable insight and experience in order to share it amongst workers. A search engine has been developed and deployed to technical support staff and we were able to assess its impact on mobile workers. The architecture is based on open-source software to satisfy the basic search functionality, such as indexing, search result ranking, faceting and spell checking. The search engine indexes a number of knowledge repositories relevant to the field engineers. On top of that we have developed an adaptive query suggestion mechanism called Sunny Aberdeen. Query suggestions provide an interactive feature that can guide the user through the search process by providing alternative terminology or suggesting ‘best matches’. In our search engine, the query suggestions are generated and adapted over time using state-of-the-art machine learning approaches, which exploit past user interactions with the search engine to derive query suggestions. Apart from continuously updating the suggestions, this framework is also capable of reflecting current search trends as well as forgetting relations that are no longer relevant. Query log analysis of the system running in a real-life context indicates that the system was able to cut down the number of repeat faults and speeds up the decision process for sending out staff to certain jobs.
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