Knowledge Graph Anchored Information-Extraction for Domain-Specific Insights
This addresses the challenge for businesses in rapidly changing domains to identify critical information without relying on expert analysis or domain-specific ontologies, though it appears incremental.
The paper tackles the problem of extracting timely insights from complex domain-specific data by proposing a pipeline that combines NLP techniques with a knowledge graph, showing preliminary effectiveness for completing end use-cases.
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.