图 书 馆
检索知识图事实的文本证据Retrieving Textual Evidence for Knowledge Graph Facts |
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| 课程网址: | http://videolectures.net/eswc2019_ercan_textual_evidence/ |
| 主讲教师: | Gonenc Ercan |
| 开课单位: | 比尔肯大学 |
| 开课时间: | 2019-09-16 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Knowledge graphs have become vital resources for semantic search and provide users with precise answers to their information needs. Knowledge graphs often consist of billions of facts, typically encoded in the form of RDF triples. In most cases, these facts are extracted automatically and can thus be susceptible to errors. For many applications, it can therefore be very useful to complement knowledge graph facts with textual evidence. For instance, it can help users make informed decisions about the validity of the facts that are returned as part of an answer to a query. In this paper, we therefore propose Open image in new window, an approach that given a knowledge graph and a text corpus, retrieves the top-k most relevant textual passages for a given set of facts. Since our goal is to retrieve short passages, we develop a set of IR models combining exact matching through the Okapi BM25 model with semantic matching using word embeddings. To evaluate our approach, we built an extensive benchmark consisting of facts extracted from YAGO and text passages retrieved from Wikipedia. Our experimental results demonstrate the effectiveness of our approach in retrieving textual evidence for knowledge graph facts. |
| 关 键 词: | 文本证据; 检索知识图事实; 语义网; Okapi BM25模型; 单词嵌入的语义匹配 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-09-21：cyh |
| 最后编审: | 2022-09-21：cyh |
| 阅读次数: | 11 |