# Knowledge Graph Anchored Information-Extraction for Domain-Specific Insights

arXiv: https://arxiv.org/abs/2104.08936 · HTML: https://mlsanity.com/paper/2104.08936/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 18 Apr 2021.

## Builds on (most cited first, 3 of 3)

- [Variational Knowledge Graph Reasoning](https://mlsanity.com/paper/1803.06581/) (2018)
- [Deep Semantic Role Labeling with Self-Attention](https://mlsanity.com/paper/1712.01586/) (2017)
- [Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach](https://mlsanity.com/paper/1707.00166/) (2017)

## Built on by (most cited first, 3 of 3)

- [Template Filling for Controllable Commonsense Reasoning](https://mlsanity.com/paper/2111.00539/) (2021)
- [Information Extraction in Domain and Generic Documents: Findings from Heuristic-based and Data-driven Approaches](https://mlsanity.com/paper/2307.00130/) (2023)
- [From Ambiguity to Explicitness: NLP-Assisted 5G Specification Abstraction for Formal Analysis](https://mlsanity.com/paper/2308.03277/) (2023)

## Abstract (arXiv)

The growing quantity and complexity of data pose challenges for humans to consume information and respond in a timely manner. For businesses in domains with rapidly changing rules and regulations, failure to identify changes can be costly. In contrast to expert analysis or the development of domain-specific ontology and taxonomies, we use a task-based approach for fulfilling specific information needs within a new domain. Specifically, we propose to extract task-based information from incoming instance data. A pipeline constructed of state of the art NLP technologies, including a bi-LSTM-CRF model for entity extraction, attention-based deep Semantic Role Labeling, and an automated verb-based relationship extractor, is used to automatically extract an instance level semantic structure. Each instance is then combined with a larger, domain-specific knowledge graph to produce new and timely insights. Preliminary results, validated manually, show the methodology to be effective for extracting specific information to complete end use-cases.