Semantic Embedding for Entity Resolution
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Solution Overview
Problem
Current systems for automatically resolving natural language records into standard categories are inefficient and error-prone, relying on human intervention and manual annotation, which is time-consuming and expensive, and fail to capture richer relationships between words.
Innovation Solution
A computer process that uses a semantic embedding function to transform natural language records and category descriptions into vector representations, allowing for the identification of best match descriptions through a two-step screening and ranking process, including k-d tree search and word mover distance, to minimize manual processing and increase productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If hand coded systems with manual annotation are used for entity resolution, then the system can be updated with new relationships, but the process becomes time consuming and expensive
Solution Approach 1:
The system performs self-learning by automatically training embedding models on unlabeled corpora to discover relationships between entities without requiring manual annotation. The model self-updates its knowledge base by processing new training data, eliminating the need for human experts to manually code and update relationship rules.
2Device complexity
If pure string matching is used for entity resolution, then the system is simple to implement, but it fails to capture richer relationships between words
Solution Approach 1:
The system transforms the matching parameter from exact string equality to semantic similarity based on vector embeddings. Instead of comparing strings character-by-character, the system converts text to vector representations and computes similarity metrics, capturing semantic relationships while maintaining computational efficiency.
Solution Approach 2:
The system replaces the mechanical string matching process with a learned semantic model. Instead of deterministic character comparison, the system uses neural network embeddings that capture contextual meanings and relationships, substituting a rigid mechanical process with a flexible learned approach.
3Speed
If exact string match is required between query and database entries, then the matching process is fast and deterministic, but it cannot handle fuzzy or indirect relationships
Solution Approach 1:
The system performs partial matching by computing similarity scores rather than requiring exact matches. It ranks candidates by degree of similarity and selects the best match, allowing the system to handle fuzzy relationships while maintaining efficiency through approximate nearest neighbor search techniques.
Data Source
AI summary
A computer process for entity resolution of natural language records including training a semantic embedding function on a corpus of unlabeled training materials. The semantic embedding function can take a word and represent it as a vector, where the vector represents the word as it relates to the semantic information of the corpus of unlabeled training materials. The process may transform a list of normalized descriptions using the semantic embedding function into a list of vector representations of the descriptions. The process may transform words from a natural language record to a vector representation of the natural language record using the semantic embedding function, and may use a named entity recognizer. The process may find a best match description from the list of normalized descriptions using the list of vector representations of the descriptions and the vector representation of the natural language record, and may include using word mover distance.