# Interactive Text Graph Mining with a Prolog-based Dialog Engine

arXiv: https://arxiv.org/abs/2008.00956 · HTML: https://mlsanity.com/paper/2008.00956/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2020.

## Built on by (most cited first, 1 of 1)

- [A Gaze into the Internal Logic of Graph Neural Networks, with Logic](https://mlsanity.com/paper/2208.03093/) (2022)

## Abstract (arXiv)

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).