Name Disambiguation via Context Term Clustering
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Solution Overview
Problem
Users face challenges in searching for information on person names due to ambiguity, as most names are not unique, leading to multiple search results referencing different individuals, and often require multiple query revisions to find relevant information.
Innovation Solution
A system generates context term lists for person names, clusters them, selects representative terms, and provides query suggestions by combining the person name with these terms to disambiguate the search, allowing users to quickly focus on appropriate contexts without manual determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a search is performed on a person name, then search results are provided, but multiple search results reference different persons causing ambiguity
Solution Approach 1:
The system performs preliminary actions by generating context term lists from multiple resources before the user submits a search query. These context terms are clustered and representative terms are selected in advance, so when a name search is performed, the disambiguation information is already prepared and can be immediately presented to the user as query suggestions, eliminating the need for multiple query revisions.
Solution Approach 2:
Context terms serve as intermediaries between the person name and the actual search results. By introducing these context terms (such as occupation, location, or associated entities) as intermediary elements, the system bridges the gap between ambiguous names and specific individuals, allowing users to refine their searches without manually determining the various contexts of each name.
2Measurement precision
If users manually revise queries to focus on specific contexts, then search accuracy improves, but time and effort are consumed
Solution Approach 1:
The system performs self-service by automatically generating context term lists, clustering them, and selecting representative terms without user intervention. The search system autonomously prepares disambiguation information and presents query suggestions to users, eliminating the need for users to manually revise queries multiple times while still achieving precise search results.
Solution Approach 2:
The system performs the time-consuming tasks of context analysis, term extraction, and clustering in advance, before the user needs to search. This preliminary processing allows the system to present ready-made query suggestions immediately when a name is searched, saving users the time and effort of manual query revision while maintaining high search precision.
3Adaptability or versatility
If context term lists are generated and clustered for all person names, then name disambiguation improves, but system complexity increases
Solution Approach 1:
The system segments the complex task of name disambiguation into distinct stages: generating context term lists from resources, clustering the context terms, and selecting representative terms. This segmentation allows each component to be handled separately and efficiently, managing system complexity while maintaining high disambiguation capability. The segmentation also enables the system to process only relevant context terms for each name rather than all possible terms.
4Reliability
If multiple query revisions are required to find relevant information, then comprehensive search coverage is achieved, but user productivity decreases
Solution Approach 1:
The system performs preliminary generation of context term lists and clustering before the user needs to search, so that when a name search is performed, multiple disambiguated query suggestions are already prepared. This allows users to immediately select the appropriate context without requiring multiple query revisions, maintaining comprehensive search coverage while dramatically improving search efficiency and user productivity.
Data Source
AI summary
Methods, systems and apparatus, including computer programs encoded on a computer storage medium, for disambiguating names in a document corpus. In an aspect, a method includes generating context term lists for a person name, each context term list being a list of context terms from a resource for the person name; clustering the context term lists into a plurality of clusters, each of the clusters of context term lists including context term lists that are most similar to the cluster relative to other clusters; for each of the clusters, selecting a representative term for the cluster; receiving the person name as a search query; and generating a plurality of query suggestions from the search query and the representative terms for the clusters, each query suggesting being a combination of the person name and one representative term.