On top of a neural network-based dependency parser and a graph-based natural language processing module, we design a Prolog-based dialog engine that explores interactively a ranked fact database extracted from a text document. We reorganize dependency graphs to focus on the most relevant content elements of a sentence and integrate sentence identifiers as graph nodes. Additionally, after ranking the graph, we take advantage of the implicit semantic information that dependency links and WordNet bring in the form of subject–verb–object, “is-a” and “part-of” relations. Working on the Prolog facts and their inferred consequences, the dialog engine specializes the text graph with respect to a query and reveals interactively the document’s most relevant content elements. The open-source code of the integrated system is available at https://github.com/ptarau/DeepRank.

中文翻译：

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使用基于 Prolog 的对话引擎进行交互式文本图挖掘

在基于神经网络的依赖解析器和基于图形的自然语言处理模块之上，我们设计了一个基于 Prolog 的对话引擎，该引擎交互式地探索从文本文档中提取的排名事实数据库。我们重新组织依赖图以关注句子中最相关的内容元素，并将句子标识符集成为图节点。此外，在对图进行排序后，我们利用依赖链接和 WordNet 以主语-动词-宾语、“is-a”和“part-of”关系的形式带来的隐含语义信息。处理 Prolog 事实及其推断结果，对话引擎针对查询专门化文本图，并以交互方式显示文档最相关的内容元素。集成系统的开源代码可在https://github.com/ptarau/DeepRank.