Model Driven Domain Specific Search Framework
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Solution Overview
Problem
Conventional search engines lack the ability to understand domain-specific concepts and relationships, particularly in scientific and engineering domains, requiring extensive effort and knowledge to implement domain-specific search systems that can accurately extract entities, properties, and relations from unstructured text.
Innovation Solution
A processor-implemented method using a meta model, instance model, extraction model, and mention model to define domain elements, preprocess text, extract entities and relations, and index them into a graph knowledge base, allowing for domain-specific search queries to be translated and processed effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If keyword based search is used, then search simplicity is maintained, but domain concept understanding capability deteriorates
Solution Approach 1:
The patent introduces an intermediary layer between keyword search and domain concepts. A query translation module converts simple keyword queries into domain-specific queries using a query language that incorporates domain knowledge from an information model. This intermediary translation mechanism preserves search simplicity while enabling domain concept understanding without requiring users to learn complex domain syntax.
Solution Approach 2:
The system creates a copied representation of domain knowledge in the form of an information model that captures entities, properties, and relations. This model serves as a simplified copy of complex domain concepts that can be processed by the search system, allowing keyword-based queries to be transformed into meaningful domain-specific searches without exposing users to underlying complexity.
2Measurement precision
If domain specific search engine is implemented, then search accuracy is improved, but development effort and knowledge intensity increase
Solution Approach 1:
The patent creates a universal search system framework that can handle multiple domains through a common architecture. The information model, query translation module, and search engine form a reusable framework that can be adapted to different domains by changing the domain-specific information model, rather than building separate search engines for each domain. This reduces development effort while maintaining high search accuracy.
Solution Approach 2:
The system performs preliminary action by pre-defining domain knowledge in the information model before search operations. Entities, properties, and relations are modeled in advance, and the query translation module is pre-configured with domain-specific rules. This preliminary structuring of domain knowledge enables accurate search without requiring complex processing during actual search operations.
3Measurement precision
If domain knowledge is codified into text processing algorithms, then search precision is improved, but system adaptability to new domains deteriorates
Solution Approach 1:
The patent segments domain knowledge into distinct components: the information model (entities, properties, relations), the query language definition, and the translation rules. This segmentation allows each component to be independently modified for different domains without affecting the overall system architecture. The core search engine remains unchanged while domain-specific components are swapped or configured differently.
Solution Approach 2:
The system implements dynamic adaptability through configurable information models and query translation rules that can be adjusted for different domains. Rather than hardcoding domain knowledge, the system uses parameterized models and rules that can be dynamically configured, allowing the same search engine to adapt to new domains by loading different domain-specific configurations.
4Reliability
If extensive domain knowledge is integrated, then search effectiveness is improved, but processing time increases
Solution Approach 1:
The system performs preliminary action by pre-compiling domain knowledge into structured information models and pre-defining query translation rules during system initialization. This upfront processing organizes domain knowledge into efficient data structures that can be quickly accessed during search operations, reducing processing time while maintaining comprehensive domain knowledge integration.
Solution Approach 2:
The system creates simplified copied representations of domain knowledge in the information model that capture essential entities, properties, and relations without storing all raw domain data. This selective copying of critical domain knowledge enables effective search with reduced processing overhead compared to handling complete domain datasets.
Data Source
AI summary
This disclosure relates to a model driven framework for realizing domain specific search system for unstructured text. The framework has two parts: information extraction system for defining various models such as a meta model, an instance model, an extraction model and mention model and a generic domain agnostic search system that works by interpreting the models specified via the information extraction system. The components of the search system are completely domain agnostic with no hard coded knowledge of the domain. The search system interprets the domain models specified in terms of the meta model to impart domain specificity to the search engine. In this sense, the framework is domain agnostic and it can be tailored for a new domain by just specifying domain related information in terms of the meta model. The model driven approach obviates need for re-coding the search system for any new domain of interest.