AAAI Publications, Twenty-Ninth AAAI Conference on Artificial Intelligence

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An EBMC-Based Approach to Selecting Types for Entity Filtering
Jiwei Ding, Wentao Ding, Wei Hu, Yuzhong Qu

Last modified: 2015-02-09


The quantity of entities in the Linked Data is increasing rapidly. For entity search and browsing systems, filtering is very useful for users to find entities that they are interested in. Type is a kind of widely-used facet and can be easily obtained from knowledge bases, which enables to create filters by selecting at most K types of an entity collection. However, existing approaches often fail to select high-quality type filters due to complex overlap between types. In this paper, we propose a novel type selection approach based upon Budgeted Maximum Coverage (BMC), which can achieve integral optimization for the coverage quality of type filters. Furthermore, we define a new optimization problem called Extended Budgeted Maximum Coverage (EBMC) and propose an EBMC-based approach, which enhances the BMC-based approach by incorporating the relevance between entities and types, so as to create sensible type filters. Our experimental results show that the EBMC-based approach performs best comparing with several representative approaches.


Budgeted Maximum Coverage; Type Filter; Type Hierarchy

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