Meta-Model Semantic Network for Enterprise Data Search
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Solution Overview
Problem
Conventional search engines are inaccurate in searching enterprise data as they fail to consider the semantic meaning of keywords, leading to incorrect results due to the lack of contextual understanding of business objects and documents.
Innovation Solution
A meta-model semantic network is generated to provide semantic information and meaning to enterprise data, allowing for the creation of semantic objects and relations that enable context-aware searching by mapping enterprise data to semantic objects and relations, thereby enhancing search accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional search engines search based on keyword matching only, then the search process is simple and fast, but the search accuracy is poor due to lack of semantic understanding
Solution Approach 1:
The system performs preliminary action by pre-generating a meta-model semantic network that captures semantic relationships between enterprise data elements before searches are executed. This pre-computed semantic structure enables accurate contextual understanding during search operations without adding complexity to the search execution process itself
Solution Approach 2:
The meta-model semantic network serves as an intermediary layer between the search engine and enterprise data. This intermediary structure provides semantic context and relationships, enabling accurate keyword interpretation while keeping the search engine itself relatively simple
2Reliability
If conventional search engines return results based on keyword presence, then the search operation is fast, but the results are irrelevant due to lack of contextual meaning
Solution Approach 1:
The semantic network is constructed in advance, storing pre-computed semantic relationships and contexts. During search operations, the system quickly queries this pre-built structure rather than computing semantic relationships in real-time, maintaining fast response times while improving result relevance
Solution Approach 2:
The system creates a semantic copy or representation of enterprise data relationships in the meta-model network. This copied semantic structure can be queried efficiently without accessing the original complex enterprise data systems, reducing search processing time while improving result accuracy
Data Source
AI summary
In an embodiment, a method is provided for utilizing a meta-model semantic network. In this method, a meta-model of the enterprise data is obtained. The meta-model provides semantic information regarding a definition of a business object. The meta-model is then used to generate a rule definition that maps enterprise data to a semantic object definition and a semantic relation definition. With the rule definition, embodiments may then generate a semantic object and a semantic relation from data extracted from the enterprise data. The semantic object and semantic relation are stored in the meta-model semantic network.