Search (4 results, page 1 of 1)

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Abstract

Understanding search engine users' intents has been a popular study in information retrieval, which directly affects the quality of retrieved information. One of the fundamental problems in this field is to find a connection between the entity in a query and the potential intents of the users, the latter of which would further reveal important information for facilitating the users' future actions. In this article, we present a novel research method for mining the actionable intents for search users, by generating a ranked list of the potentially most informative actions based on a massive pool of action samples. We compare different search strategies and their combinations for retrieving the action pool and develop three criteria for measuring the informativeness of the selected action samples, that is, the significance of an action sample within the pool, the representativeness of an action sample for the other candidate samples, and the diverseness of an action sample with respect to the selected actions. Our experiment, based on the Action Mining (AM) query entity data set from the Actionable Knowledge Graph (AKG) task at NTCIR-13, suggests that the proposed approach is effective in generating an informative and early-satisfying ranking of potential actions for search users.

Theme

Suchmaschinen

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Abstract

Jeden Tag erscheint eine Flut neuer wissenschaftlichen Publikationen. Für Forscher ist es schwierig, dabei den Überblick zu behalten. Aktualität und Relevanz der Ergebnislisten von Suchmaschinen wie Google, scholar.google.com und wissenschaftlichen Suchportalen entsprechen oft nicht den Erwartungen der Forscher. Vorgestellt wird eine Methode, die als Finden ohne Suchen (FwS = finding without searching) bezeichnet wird. Diese Methode nutzt künstliche Intelligenz in Kombination mit ausdrucksstarken benutzerdefinierten Regeln für Benachrichtigungen über neue Publikationen über eine App.

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Theme

Suchmaschinen

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Abstract

There is no information search system can assist young children's known picture book search needs since the information is not organized according to their cognitive abilities and needs. Therefore, this study explored young children's known picture book search strategies and extracted picture book search elements by simulating a search scenario and playing a picture book search game. The study found 29 elements children used to search for known picture books. Then, these elements are classified into three dimensions: The first dimension is the concept category of an element. The second dimension is an element's status in the story. The third dimension indicates where an element appears in a picture book. Additionally, it revealed a young children's general search strategy: Children first use auditory elements that they hear from the adults during reading. After receiving error returns, they add visual elements that they see by themselves in picture books. The findings can not only help to understand young children's known-item search and reformulation strategies during searching but also provide theoretical support for the development of a picture book information organization schema in the search system.