Interactive Text Graph Mining with a Prolog-based Dialog Engine

TL;DR

This paper presents a novel interactive system combining neural parsing, graph-based NLP, and a Prolog dialog engine to explore and extract relevant content from text documents through semantic graph analysis.

Contribution

It introduces a Prolog-based dialog engine that interacts with a ranked semantic graph derived from dependency parsing and WordNet, enabling targeted content exploration.

Findings

Effective integration of neural parsing with logic programming.

Interactive exploration of document content through semantic graphs.

Open-source implementation available for further research.

Abstract

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 . Under consideration in Theory and Practice of Logic Programming (TPLP).