通过外部Web资源自动链接增强堆栈溢出中的知识共享

Sa Gao, Zhenchang Xing, Yukun Ma, Deheng Ye, Shang-Wei Lin
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引用次数: 5

摘要

引用外部web资源的url(例如,官方语言参考和API文档)是Stack Overflow等问答网站知识共享的有效机制。我们表明,url的引用频率遵循幂律分布,这意味着经常被引用的web资源可能会再次被引用。然而,缺乏有效的方法来管理和重用在问答讨论中提到的与实体相关的已经共享的web资源(例如api或编程概念)。由于URL引用是以一种特别的方式完成的,因此大量的实体提及并没有链接到相关的web资源。为了加强对Stack Overflow中已经共享的web资源的管理和重用,我们建立了Stack Overflow中已经共享的语言和api的官方文档知识库,并开发了一种web资源自动链接技术,将实体提及链接到知识库中的相关官方文档。自动网络资源链接的一个挑战是实体的提及常常具有模糊性,例如,不同语言之间相同的编程概念,不同库中相同的api名称。为了消除在实体提及的几个URL候选对象之间链接的正确web资源的歧义,我们的技术检查了实体提及的URL候选对象的全球流行程度,以及URL候选对象与提及实体的讨论线程的本地上下文相关性。我们对构建的知识库和我们的自动web资源链接技术的性能进行了大规模的评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing Knowledge Sharing in Stack Overflow via Automatic External Web Resources Linking
Referencing URLs of external web resources (e.g., official language references and API documents) is an effective mechanism for knowledge sharing in Q&A websites like Stack Overflow. We show that reference frequencies of URLs follow power law distribution, meaning that web resources that have been referenced frequently will likely to be referenced again. However, there lack of effective methods to manage and reuse already-shared web resources relevant to entities (e.g., APIs or programming concepts) that are mentioned in Q&A discussions. As URL references are done in an ad-hoc manner, large amounts of entity mentions have not been linked to relevant web resources. To enhance management and reuse of alreadyshared web resources in Stack Overflow, we build a knowledge base of official documentation of languages and APIs that have been shared in Stack Overflow, and develop an automatic web resources linking technique to linkify entity mentions to relevant official documentation in the knowledge base. A challenge in automatic web resources linking is that entity mentions often have ambiguity, for example, same programming concepts across different languages, same name APIs in different libraries. To disambiguate the right web resource to link among several URL candidates for an entity mention, our technique examines both the global popularity of the URL candidates for the entity mention and the local context relatedness of the URL candidates with the discussion thread in which the entity is mentioned. We conduct large scale evaluation of the built knowledge base and the performance of our automatic web resource linking technique.
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