图 书 馆
链接的数据为基础的概念建议：在开放式创新方案的不同方法的比较Linked Data-based Concept Recommendation: Comparison of Different Methods in Open Innovation Scenario |
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| 课程网址: | http://videolectures.net/eswc2012_damljanovic_innovation/ |
| 主讲教师: | Danica Damljanović |
| 开课单位: | 谢菲尔德大学 |
| 开课时间: | 2012-07-04 |
| 课程语种: | 英语 |
| 中文简介: |  概念的推荐是一种广泛使用的技术的目的是帮助用户选择合适的标签，提高其网络搜索经验和众多的其他任务。在寻找潜在的解决问题的开放式创新（OI）的情况下，建议的概念是至关重要的意义，因为它可以帮助找到合适的话题，直接或横向相关创新问题。这样的主题，然后可以用来识别相关的专家。在本文中，我们提出了两个链接的数据为基础的概念为主题发现的推荐方法。第一个–称为hyproximity 仅利用的特殊性的链接的数据结构，而另一种适用于一个著名的信息检索方法–称随机索引到关联数据。我们的性能进行比较，两种方法对基线的金标准和基于用户研究为基础的评估，使用从我公司的实际问题和解决方案。 |
| 课程简介: | Concept recommendation is a widely used technique aimed to assist users to chose the right tags, improve their Web search experience and a multitude of other tasks. In finding potential problem solvers in Open Innovation (OI) scenarios, the concept recommendation is of a crucial importance as it can help to discover the right topics, directly or laterally related to an innovation problem. Such topics then could be used to identify relevant experts. In this paper, we propose two Linked Data-based concept recommendation methods for topic discovery. The first one – called hyProximity - exploits only the particularities of Linked Data structures, while the other one applies a well-known Information Retrieval method – called Random Indexing - to the linked data. We compare the performance of the two methods against the baseline in the gold standard-based and user study-based evaluations, using the real problems and solutions from an OI company. |
| 关 键 词: | 网络搜索; 信息检索; 数据链接 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2021-06-25：zyk |
| 阅读次数: | 44 |