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首页> 外文会议>2015 8th International Conference on u- and e-Service, Science and Technology >Towards Networks of Search Engines and Other Digital Experts: A Distributed Intelligence Approach
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Towards Networks of Search Engines and Other Digital Experts: A Distributed Intelligence Approach

机译：迈向搜索引擎和其他数字专家网络的一种分布式智能方法

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摘要

systems, general-and special-purpose search engines. Instead of choosing between e.g. Google, Bing and Baidu, a user may want to get integrated results from all three search engines (and more) via a single search query. The user may also want results orsuggestions to questions implied by, but not explicitly stated, in his query. Ideally, the combined results from different searchengines will be relevance-ranked, taking into account each user's individual preferences. There exist "expert systems" thatintegrate results or recommendations from multiple different websites and/or other search engines -- e.g., the meta-search engines for finding the best flights and airfares. However, these meta-search engines do not (i) relevance-rank on behalf of the end-user, (ii) learn over time, which websites/individual search engines are most trustworthy and relevant to a particular user, (iii) maintain a quality assurance model of the individual sources of information or recommendation that they harvest, or (iv) create sub-queries or new queries based on inference of the user intent, and not merely what the user has explicitly asked for. We propose a unified framework to address all these issues. In particular, our goal is to enable the end-user to seamlessly obtain integrated expertise from a variety of sources, so that those recommendations are ranked based on both (a) the user's preferences and (b) different individual sources' of recommendation reputation and trustworthiness. Our vision for achieving these goals is to have a decentralized, transparent market-place of search engines, recommenders and knowledge bases, where the burdens of integrating, ranking and evaluating quality of different knowledge sources are taken off the end-user.
机译：系统，通用和专用搜索引擎。而不是在例如Google，必应和百度，用户可能希望通过一个搜索查询从所有三个搜索引擎（以及更多搜索引擎）中获得综合结果。用户可能还希望得到其查询中隐含但未明确说明的问题的结果或建议。理想情况下，考虑到每个用户的个人偏好，将来自不同搜索引擎的合并结果进行相关性排名。存在“专家系统”，其集成来自多个不同网站和/或其他搜索引擎的结果或推荐，例如，用于查找最佳航班和机票的元搜索引擎。但是，这些元搜索引擎没有（i）代表最终用户的相关性等级；（ii）随着时间的流逝，哪些网站/个人搜索引擎最值得信赖并且与特定用户相关；（iii）维护他们收集的单个信息源或推荐的质量保证模型，或者（iv）基于用户意图的推断而不仅仅是用户明确要求的内容，创建子查询或新查询。我们提出一个统一的框架来解决所有这些问题。特别是，我们的目标是使最终用户能够无缝地从各种来源获得集成的专业知识，以便根据（a）用户的偏好和（b）不同的个人来源的推荐声誉和值得信赖。我们实现这些目标的愿景是建立一个分散，透明的搜索引擎，推荐者和知识库市场，在该市场上，整合，排名和评估不同知识源的质量的负担将由最终用户承担。

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