Flexible Pseudo-Parsing of Semi-Structured Text
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Solution Overview
Problem
Existing systems face challenges in efficiently identifying and parsing semi-structured text blocks, which contain multiple entities and identifiers, due to varying structure templates across sources and domains, leading to difficulties in determining identifier associations and consuming valuable resources when maintaining large structured databases.
Innovation Solution
A method and system utilizing natural language processing (NLP) to identify high confidence alignments of entities and identifiers, generating structure templates, and annotating documents with metadata, while populating a temporary query-specific knowledge base to rapidly extract entities and identifiers in future searches, thus minimizing storage resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If structure templates are maintained in large structured databases to parse semi-structured text, then parsing accuracy is improved, but storage resources are consumed
Solution Approach 1:
The patent generates structure templates on-demand for each query rather than maintaining permanent templates in a database. The temporary knowledge base stores only the generated templates for the current query session, discarding them after use. This disposable approach eliminates long-term storage requirements while maintaining parsing accuracy for each query.
Solution Approach 2:
The system performs preliminary alignment identification and structure template generation before actual parsing operations. By pre-identifying high-confidence alignments and generating appropriate templates in advance of the parsing task, the system ensures accurate parsing without needing to store pre-defined templates for all possible cases.
2Adaptability or versatility
If structure templates vary across different sources and domains, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system dynamically generates structure templates based on the specific query and semi-structured text block rather than using static pre-defined templates. The template generation adapts to different sources and domains automatically by analyzing the actual data patterns in each query, eliminating the need to manage complex template variations across multiple sources.
Solution Approach 2:
The system uses the semi-structured text blocks themselves to generate their own parsing templates through alignment identification. Each text block serves as its own template source, automatically determining its structure without requiring external template definitions or complex template management infrastructure.
3Productivity
If entities and identifiers are extracted rapidly for future queries, then productivity is improved, but storage resources increase
Solution Approach 1:
The patent implements a temporary knowledge base that stores extracted entity-alignments and generated templates only for the duration of the current query session. After processing, these temporary structures are discarded. This enables rapid extraction for each query without accumulating storage overhead from previous queries, as the temporary data structures are reused rather than permanently stored.
Data Source
AI summary
An embodiment for extracting information from semi-structured text is provided. The embodiment may include identifying one or more high confidence alignments of one or more entities and identifiers in a set of documents. The embodiment may also include analyzing one or more blocks of semi-structured text containing the one or more entities and identifiers. The embodiment may further include identifying one or more known alignments in each of the one or more blocks of semi-structured text. The embodiment may also include generating a structure template. The embodiment may further include applying the structure template to each of the one or more blocks of semi-structured text. The embodiment may also include annotating the set of documents with metadata reflecting the structure template and a location of each of the one or more blocks of semi-structured text.