Knowledge Graph Anchored Information-Extraction for Domain-Specific Insights

• URL: http://arxiv.org/abs/2104.08936v2
• Date: Tue, 20 Apr 2021 02:44:06 GMT
• Title: Knowledge Graph Anchored Information-Extraction for Domain-Specific Insights
• Authors: Vivek Khetan, Annervaz K M, Erin Wetherley, Elena Eneva, Shubhashis Sengupta, and Andrew E. Fano
• Abstract summary: We use a task-based approach for fulfilling specific information needs within a new domain. A pipeline constructed of state of the art NLP technologies is used to automatically extract an instance level semantic structure.
• Score: 1.6308268213252761
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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