Path-Constrained Random Walks for Query Expansion
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Solution Overview
Problem
Conventional query expansion techniques face challenges such as data sparseness and ambiguity in search intent, particularly in log-based methods, which can lead to ineffective retrieval of relevant documents due to limited context and rare query issues.
Innovation Solution
The implementation of path-constrained random walks on a labeled and directed graph representing clickthrough data, allowing for the computation of scores for candidate query expansion terms or relevant documents based on traversals between nodes, thereby enhancing query expansion and document matching by incorporating diverse path types and relation-specific models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If log-based QE techniques use a global model pre-computed from search logs, then the model can capture correlation between query terms and document terms, but data sparseness impacts effectiveness because a significant portion of queries have few or no clicks in the search logs
Solution Approach 1:
The patent introduces path-constrained random walks as an intermediary mechanism that operates on a graph structure built from clickthrough data. This intermediary allows the system to leverage global correlation patterns while handling individual query sparsity through graph-based path traversal, where nodes represent queries, documents, and terms, and edges represent co-occurrence relationships. The random walks propagate information across the graph to generate expansion terms even for queries with limited direct click data.
Solution Approach 2:
The patent transforms the traditional flat query-document term correlation model into a multi-dimensional graph structure with three types of nodes (queries, documents, terms) and multiple edge types representing different relationships. This dimensional transformation allows the system to capture complex contextual relationships and disambiguate search intent by analyzing paths through different dimensions of the graph, rather than relying solely on direct term co-occurrence statistics.
2Productivity
If log-based QE techniques use a global model, then expansion terms can be generated on the fly, but ambiguity of search intent detrimentally impacts the techniques because the model may fail to distinguish different search intents for the same term
Solution Approach 1:
The patent applies local quality by allowing different parts of the graph to have different properties and relationships. Each node (query, document, term) maintains its local context through incoming and outgoing edges, enabling the system to capture local search intent nuances while still benefiting from global patterns. The path-constrained random walks traverse locally relevant paths based on the specific query context, generating expansion terms that are tailored to the local search intent rather than applying a uniform global model.
Solution Approach 2:
The patent introduces dynamics by making the query expansion process adaptive to the specific query context. The path-constrained random walks dynamically select which paths to traverse based on the input query and its context, allowing the system to adapt to different search intents in real-time. The graph structure and walk parameters can be adjusted dynamically based on query characteristics, enabling flexible handling of ambiguous terms while maintaining fast on-the-fly generation.
3Adaptability or versatility
If path-constrained random walks are executed on a graph with multiple node and edge types, then diverse path types can be incorporated for query expansion, but the device complexity increases due to the need to manage labeled and directed graphs with multiple relationship types
Solution Approach 1:
The patent segments the complex graph management into distinct components: query nodes, document nodes, and term nodes, each with specific properties and relationships. The edges are segmented into different types representing specific relationships (e.g., query-clicked-document, document-containing-term). This segmentation allows the system to manage complexity by treating each segment independently while maintaining their interconnected relationships through the graph structure, making the system more manageable and maintainable.
Data Source
AI summary
Various technologies described herein pertain to use of path-constrained random walks for query expansion and/or query document matching. Clickthrough data from search logs is represented as a labeled and directed graph. Path-constrained random walks are executed over the graph based upon an input query. The graph includes a first set of nodes that represent queries included in the clickthrough data from search logs, a second set of nodes that represent documents included in the clickthrough data from the search logs, a third set of nodes that represent words from the queries and the documents, and edges between nodes that represent relationships between queries, documents, and words. The path-constrained random walks include traversals over edges of the graph between nodes. Further, a score for a relationship between a target node and a source node representative of the input query is computed based at least in part upon the path-constrained random walks.