Business Intent-Assisted Search Using K-Partite Metadata Graphs
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Solution Overview
Problem
Current search query processing methods lack effectiveness in providing relevant results by failing to adequately incorporate business intent parameters, leading to incomplete and irrelevant search outcomes for organization users.
Innovation Solution
A method involving the generation of a k-partite metadata graph based on search topics and business intent parameters, which filters and identifies relevant asset subsets within an asset catalog, ensuring search results are contextually relevant and accessible to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search query processing methods are used, then the search system is simple and easy to operate, but the search results lack relevance and completeness
Solution Approach 1:
The search processing is segmented into distinct phases: obtaining business intent parameters, filtering the metadata graph based on search topic, filtering based on business intent parameters, and generating search results. This segmentation allows complex processing to be broken down into manageable steps, improving result relevance while maintaining operational clarity.
Solution Approach 2:
A metadata graph is introduced as an intermediary structure between the search query and the final search results. The metadata graph serves as a mediator that incorporates both search topic information and business intent parameters, enabling the system to bridge the gap between simple user queries and complex business context requirements.
2Quantity of substance
If business intent parameters are integrated into search processing, then search result completeness improves, but processing time increases
Solution Approach 1:
The system performs preliminary filtering of the metadata graph based on the search topic before incorporating business intent parameters. This preliminary action reduces the dataset size early in the process, making the subsequent business intent-based filtering more efficient and reducing overall processing time while maintaining result completeness.
Solution Approach 2:
The system extracts and utilizes only the relevant business intent parameters needed for the search query rather than processing all available data. By taking out and focusing on the essential parameters, the system achieves complete search results without unnecessarily processing the entire dataset, thereby reducing processing time.
3Manufacturing precision
If multiple filtering steps are applied to the metadata graph, then search result accuracy improves, but system complexity increases
Solution Approach 1:
The filtering process applies different criteria at different stages: first filtering by search topic to identify relevant nodes, then filtering by business intent parameters to refine results. Each filtering step focuses on specific local qualities of the data, improving accuracy step-by-step without requiring all filtering criteria to be applied simultaneously, thus managing system complexity.
Data Source
AI summary
A method and system for business intent-assisted search. A business intent may generally refer to information, respective to an organization user, which may pertain to or describe the engagement of the organization user within and/or outside their organization (e.g., a commercial business, an education institution, etc.). Embodiments disclosed herein, accordingly, implement search query processing based on the business intent modeled for the search query submitter (e.g., an organization user). Further, a recall of any returned information, relevant to the search query, may be contingent on and/or directly correlated with a completeness of the modeled business intent for any given organization user.