Robust question answering over the web of linked data
Robust question answering over the web of linked data
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DOI：
10.1145/2505515.2505677
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发表时间：
2013-10
期刊：
影响因子：
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通讯作者：
Mohamed Yahya;K. Berberich;Shady Elbassuoni;G. Weikum
中科院分区：
文献类型：
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作者：
Mohamed Yahya;K. Berberich;Shady Elbassuoni;G. Weikum
知识库和关联数据网络已经成为搜索、推荐和分析的重要资产。自然语言问题是一种用户友好的模式，可以利用这些丰富的知识和数据。然而，问题回答技术在这种情况下并不稳定，因为问题必须被翻译成结构化查询，用户必须小心地措辞他们的问题。本文提倡一种新的方法，允许问题被部分翻译成宽松的查询，涵盖了用户的输入的基本，但不一定是所有方面。为了弥补遗漏，我们利用与实体和关系事实相关的文本来源。我们的系统将用户问题转换为扩展形式的结构化SPARQL查询，文本谓词附加到三重模式。我们的解决方案是基于一种新的优化模型，铸造成一个整数线性规划，联合分解和消歧的用户问题。我们证明了我们的方法的质量，通过实验与QALD基准。
Knowledge bases and the Web of Linked Data have become important assets for search, recommendation, and analytics. Natural-language questions are a user-friendly mode of tapping this wealth of knowledge and data. However, question answering technology does not work robustly in this setting as questions have to be translated into structured queries and users have to be careful in phrasing their questions. This paper advocates a new approach that allows questions to be partially translated into relaxed queries, covering the essential but not necessarily all aspects of the user's input. To compensate for the omissions, we exploit textual sources associated with entities and relational facts. Our system translates user questions into an extended form of structured SPARQL queries, with text predicates attached to triple patterns. Our solution is based on a novel optimization model, cast into an integer linear program, for joint decomposition and disambiguation of the user question. We demonstrate the quality of our methods through experiments with the QALD benchmark.