Semantic Reasoning System for Data Clustering and Inference
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Solution Overview
Problem
Traditional search and retrieval systems are inadequate for large datasets as they fail to identify semantically relevant documents beyond keyword matches, leading to data explosion and accuracy issues when dealing with diverse domains.
Innovation Solution
A multi-stage semantic reasoning method and system that converts data artifacts into semantic representations, clusters them based on entities and relationships, and applies a multi-tiered network of inference engines to infer additional relationships, using domain-independent and domain-specific techniques to manage data volume and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional keyword search techniques are used, then search speed is maintained, but semantic relevance and accuracy deteriorate when dealing with large diverse corpuses
Solution Approach 1:
The patent segments the large corpus into multiple domain-specific sub-corpuses or clusters. By dividing the vast data into manageable domain segments, the system can apply domain-specific inference rules and ontologies to each segment, thereby maintaining high semantic relevance accuracy without being overwhelmed by the diversity and volume of the entire corpus.
Solution Approach 2:
The system dynamically adjusts inference parameters such as reasoning depth, confidence thresholds, and ontology selection based on the detected domain of the query and corpus segments. This parameter adaptation allows the system to optimize semantic inference for different domains while maintaining overall system efficiency and accuracy across diverse data types.
2Measurement precision
If inference algorithms are applied to large volumes of documents, then semantic relationships are improved, but storage and computing resources are significantly inflated
Solution Approach 1:
The patent extracts only the essential semantic relationships and entities from documents using targeted inference algorithms, rather than processing entire documents. By extracting and storing only the critical semantic triples and relationships, the system maintains high semantic accuracy while significantly reducing the volume of data that needs to be stored and processed.
Solution Approach 2:
The system performs preliminary semantic extraction and relationship inference during the indexing phase, creating a pre-processed semantic representation of the corpus. This preliminary action allows the main query processing to work with already-structured semantic data rather than raw documents, reducing both storage requirements and query-time computational resources.
3Adaptability or versatility
If inference algorithms are applied to large volumes of documents spanning multiple domains, then semantic coverage is improved, but accuracy deteriorates due to the frame problem
Solution Approach 1:
The patent applies different inference algorithms, ontologies, and reasoning strategies tailored to specific domains rather than using a single universal approach. Each domain segment receives customized semantic processing with domain-appropriate inference rules, ensuring high accuracy within each domain while maintaining broad multi-domain coverage through the segmented architecture.
4Productivity
If traditional search engines are used, then processing speed is maintained, but ability to identify semantically relevant documents without keywords deteriorates
Solution Approach 1:
The patent merges traditional keyword-based search mechanisms with semantic inference and reasoning capabilities into a unified search system. The keyword search provides fast initial retrieval, while semantic inference enhances results by identifying documents with relevant meaning even when exact keywords are absent, thereby maintaining search efficiency while reducing semantic information loss.
Data Source
AI summary
A method and system for analyzing a corpus of data artifacts is disclosed. The method comprises obtaining, by a computer, a semantic representation of the data artifacts, where the semantic representation indicates (1) entities identified in the data artifacts, and (2) semantic relationships among the entities as indicated by the data artifacts. The method further comprises clustering the data artifacts into clusters of semantically related data artifacts based on the semantic representation and inferring additional semantic relationships between pairs of the entities. The inferring comprises applying, on a cluster-by-cluster basis, a multi-tiered network of inference engines to a portion of the semantic representation corresponding to the cluster, where the multi-tiered network of inference engines includes a domain-independent inference tier and a domain-specific inference tier.