Semantic Tag Generation for Novel Context Discovery
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional knowledge graphs struggle to address novel contexts in natural language queries or search queries that involve hidden relations between latent concepts or entities with different surface realizations, as these contexts may not directly refer to existing entities or concepts in curated knowledge sources.
Innovation Solution
A method and system for determining semantic tags by processing documents to identify similar documents, generating keywords that represent novel or hidden concepts, and training a model to expand queries and create indexes, using techniques like TFIDF and cosine similarity to identify relevant terms across related documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional knowledge graphs are used to represent facts and capture relationships among entities, then accuracy in fact-based information retrieval is improved, but the ability to address novel contexts with hidden relations between latent concepts deteriorates
Solution Approach 1:
The system performs preliminary action by generating semantic tags for documents before query processing. These semantic tags capture hidden concepts and latent relationships in advance, so when a novel query arrives, the system can quickly match it against pre-computed semantic tags rather than relying solely on traditional knowledge graph lookups. This preliminary enrichment allows the system to handle novel contexts effectively while maintaining accuracy for fact-based queries.
Solution Approach 2:
Semantic tags serve as an intermediary between traditional knowledge graphs and novel queries. Instead of directly querying the knowledge graph for hidden relationships, the system uses semantic tags as a mediator layer that captures latent concepts from document collections. This intermediary layer bridges the gap between structured knowledge graphs and unstructured novel contexts, enabling the system to address both fact-based and novel queries effectively.
2Adaptability or versatility
If semantic tags representing hidden concepts are generated for documents, then the ability to address novel contexts is improved, but system complexity increases
Solution Approach 1:
The system segments the complex task of handling novel queries into two independent components: (1) offline semantic tag generation from document collections, and (2) online query matching using pre-computed tags. This segmentation allows the complex semantic analysis to be performed once during offline processing, while online query processing remains simple and efficient. The segmentation reduces overall system complexity by separating heavy computation from real-time operations.
Solution Approach 2:
The system performs preliminary action by pre-computing semantic tags for all documents in the collection before query processing. This offline preprocessing captures hidden concepts and latent relationships in advance, so the online query system only needs to perform simple matching operations. This preliminary action shifts computational complexity from the time-critical query path to offline batch processing, effectively reducing the perceived system complexity during operation.
3Loss of information
If documents are processed to identify similar documents and generate semantic tags, then hidden concepts are revealed, but processing time increases
Solution Approach 1:
The system uses periodic action by performing semantic tag generation in periodic offline batches rather than continuously for every query. Document similarity computation and semantic tag generation are executed periodically on updated document collections, while query processing uses the pre-computed tags in real-time. This periodic batching allows comprehensive semantic analysis to be performed at intervals rather than continuously, significantly reducing the time impact on query operations while still revealing hidden concepts effectively.
Solution Approach 2:
The system performs preliminary action by computing document similarities and generating semantic tags in advance during offline processing phases. This preliminary computation identifies hidden concepts and latent relationships before any queries are processed, so the time-consuming similarity analysis and semantic tag generation do not occur during online query processing. The preliminary action shifts the time cost to offline batch operations, making the system responsive to queries while still revealing hidden concepts comprehensively.
Data Source
AI summary
Documents from a set of related documents in a domain are processed to identify keywords associated with each document. The documents are then further processed to identify the documents that are the most similar to each other. For each document, some or all of the keywords that are associated with the similar documents, but not the document itself, are selected as semantic tags for the document. These semantic tags determined for a document represent novel or hidden concepts and contexts that may relate to the document, but that do not actually appear in the document. The documents are used to train a model that generate semantic tags for a document or for keywords associated with the document. The generated model can then be used for a variety of purposes such the creation of an index for a set of documents or for query expansion.