Triplet Query Search for Unstructured Data Relationships
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Standard search engines face challenges in efficiently identifying relationships between concepts within unstructured data, requiring users to manually sift through numerous documents to find relevant information, as they primarily match query terms to documents rather than searching for relationships between elements.
Innovation Solution
The method involves creating enhanced queries with variations of relation terms, processing these queries on unstructured text-based data to extract values for variable elements within triplets, and presenting the results in a graphical user interface (GUI) as a dependency tree, allowing for iterative selection and refinement of queries to uncover relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard search engines match query terms to documents, then document retrieval is achieved, but relationship identification between concepts is inefficient
Solution Approach 1:
The query is segmented into a triplet structure (first token, relation term, second token) where each element represents a specific conceptual component. This segmentation allows the system to systematically search for and identify relationships between concepts by breaking down the search task into discrete relational elements rather than treating it as a holistic document matching problem.
Solution Approach 2:
The patent introduces an intermediary processing layer that receives the triplet query, generates enhanced queries with relation term variations, and systematically processes search results to extract relationship information. This intermediary layer bridges the gap between simple document retrieval and complex relationship identification, enabling efficient concept relationship search without requiring users to manually analyze numerous documents.
2Reliability
If users manually sift through documents to find relevant information, then comprehensive review is achieved, but time consumption increases
Solution Approach 1:
The system performs preliminary actions by automatically generating enhanced queries with relation term variations and pre-processing search results to identify potential relationship matches. This preliminary processing filters and organizes information before presentation to the user, ensuring that only relevant relationship candidates are displayed, thereby maintaining information accuracy while eliminating the need for users to manually review unrelated documents.
Solution Approach 2:
The system incorporates feedback mechanisms where the initial search results are analyzed to extract relationship information, which then informs subsequent query refinements. The extracted relationships are presented to users for validation, and this feedback loop continues iteratively to improve relationship identification accuracy while minimizing the time users need to spend on manual verification.
3Adaptability or versatility
If search engines retrieve numerous documents, then coverage is improved, but user burden increases
Solution Approach 1:
The system extracts only the essential relationship information from the search results, pulling out specific triplet patterns (first token, relation term, second token) that directly answer the user's conceptual relationship query. This extraction process filters out extraneous document content and presents only the relevant relationship data, maintaining comprehensive search coverage while significantly reducing the user burden of sifting through numerous documents.
Solution Approach 2:
The patent transitions the search output from a traditional one-dimensional document list to a structured triplet relationship representation. By organizing results as conceptual relationships with defined roles (first token, relation term, second token), the system adds a dimensional structure that makes relationship identification immediate and intuitive, eliminating the need for users to manually analyze document content while preserving comprehensive search coverage.
Data Source
AI summary
There is provided a method for searching an unstructured dataset with a query, comprising: receiving a query comprising a value for a first token of a triplet, and a value for a relation term defining a relationship between the first token and a second token of the triplet, wherein the second token is defined as a variable element set with an undefined value, creating a plurality of enhanced queries for the query, each one of the plurality of enhanced queries including variations of the relation term, providing the plurality of enhanced queries for search by a search engine on at least one dataset of unstructured text-based data, receiving a plurality of documents in response to the search, analyzing the plurality of documents for extracting at least one value for the variable element of the triplet, and providing the at least one value for the variable element.