Deep Web Service Organization via Attribute Clustering
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Solution Overview
Problem
Current search engine technologies are inadequate for navigating and exploring web services and online databases, as they do not support queries for specific domains, input attributes, or similar services, limiting users' ability to find relevant web services.
Innovation Solution
A method that organizes web services by grouping and clustering attributes using hyperclique mining and hierarchical agglomerative clustering algorithms, constructing a graph representation of communities to facilitate navigation and querying of deep web databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If keyword-based search is used, then search simplicity is maintained, but search capability for domain-specific and attribute-based queries deteriorates
Solution Approach 1:
The patent segments the monolithic search process into distinct phases: metadata collection from web services, organization into communities based on shared attributes, and hierarchical clustering. This segmentation allows the system to maintain simple keyword interface while adding sophisticated backend processing capabilities for domain-specific and attribute-based queries.
Solution Approach 2:
The patent introduces metadata and community structures as intermediary layers between the simple keyword interface and the complex web service database. These intermediaries enable the system to process sophisticated queries without requiring complex user input, bridging the gap between ease of use and search capability.
2Adaptability or versatility
If web services are organized into communities with graph representation, then navigation and exploration capability is improved, but system complexity increases
Solution Approach 1:
The patent adds a new dimension of organization by creating graph representations where web services are connected through shared attributes and communities. This graph structure provides multiple navigation paths and relationships that weren't accessible in traditional flat search, enhancing exploration capability while managing complexity through structured relationships.
Solution Approach 2:
The patent performs preliminary organization of web services into communities and establishes graph relationships in advance, before users perform searches. This pre-processing creates the navigation infrastructure that enables efficient exploration without adding complexity to the user interface or real-time query processing.
3Loss of information
If deep Web services are crawled and analyzed, then knowledge extraction and query support is improved, but data processing time and resources increase
Solution Approach 1:
The patent performs crawling and analysis of deep Web services in advance to extract metadata and organize services into communities. This preliminary action captures and stores knowledge about service attributes, domains, and relationships before queries are executed, enabling fast retrieval without repeating the expensive analysis process for each query.
Solution Approach 2:
The patent extracts essential metadata and organizational information from deep Web services during the preliminary crawling phase. By taking out only the critical attributes, domains, and relationship data needed for navigation and querying, the system avoids processing the entire depth of each web service on every query, reducing time loss while maintaining knowledge extraction quality.
Data Source
AI summary
A method for organizing deep Web services is provided. In one aspect, the method obtains a collection of sources and their associated attributes and/or input modes, for instance, using a crawling algorithm. The method uses this information to organize the sources into communities. A mining algorithm such as the hyperclique mining algorithm is used to obtain cliques of highly correlated attributes. A clustering algorithm such as the hierarchical agglomerative clustering algorithm is used to further cluster the cliques of attributes into larger cliques, which in the present disclosure is referred to as signatures. The sources that are associated with each signature form a community and a graph representation of the communities is constructed, where the vertices are communities and the edges are the shared attributes.