News agencies produce thousands of multimedia stories describing events happening in the world that are either scheduled such as sports competitions, political summits and elections, or breaking events such as military conflicts, terrorist attacks, natural disasters, etc. When writing up those stories, journalists refer to contextual background and to compare with past similar events. However, searching for precise facts described in stories is hard. In this paper, we propose a general method that leverages the Wikidata knowledge base to produce semantic annotations of news articles. Next, we describe a semantic search engine that supports both keyword based search in news articles and structured data search providing filters for properties belonging to specific event schemas that are automatically inferred.
翻译:新闻机构制作了数千个多媒体故事,描述世界上发生的事件,如体育竞赛、政治峰会和选举,或突发事件,如军事冲突、恐怖袭击、自然灾害等。 记者在撰写这些故事时,参考背景背景,并与过去类似事件进行比较。然而,寻找故事中描述的准确事实是很困难的。在本文中,我们提出了一个一般方法,利用维基数据知识库制作新闻文章的语义说明。接下来,我们描述了一个语义搜索引擎,支持基于关键词的新闻报道搜索和结构化的数据搜索,为自动推断属于特定事件图案的属性提供过滤器。