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.
@article{arxiv.2104.08936,
title = {Knowledge Graph Anchored Information-Extraction for Domain-Specific Insights},
author = {Vivek Khetan and Annervaz K M and Erin Wetherley and Elena Eneva and Shubhashis Sengupta and Andrew E. Fano},
journal= {arXiv preprint arXiv:2104.08936},
year = {2021}
}