Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/106420
Title: Pair-linking for collective entity disambiguation : two could be better than all
Authors: Phan, Minh C.
Sun, Aixin
Tay, Yi
Han, Jialong
Li, Chenliang
Keywords: Collective Entity Disambiguation
MINTREE
Engineering::Computer science and engineering
Issue Date: 2018
Source: Phan, M. C., Sun, A., Tay, Y., Han, J., & Li, C. (2019). Pair-linking for collective entity disambiguation : two could be better than all. IEEE Transactions on Knowledge and Data Engineering, 31(7), 1383-1396. doi:10.1109/TKDE.2018.2857493
Series/Report no.: IEEE Transactions on Knowledge and Data Engineering
Abstract: Collective entity disambiguation, or collective entity linking aims to jointly resolve multiple mentions by linking them to their associated entities in a knowledge base. Previous works are primarily based on the underlying assumption that entities within the same document are highly related. However, the extent to which these entities are actually connected in reality is rarely studied and therefore raises interesting research questions. For the first time, this paper shows that the semantic relationships between mentioned entities within a document are in fact less dense than expected. This could be attributed to several reasons such as noise, data sparsity, and knowledge base incompleteness. As a remedy, we introduce MINTREE, a new tree-based objective for the problem of entity disambiguation. The key intuition behind MINTREE is the concept of coherence relaxation which utilizes the weight of a minimum spanning tree to measure the coherence between entities. Based on this new objective, we design Pair-Linking, a novel iterative solution for the MINTREE optimization problem. The idea of Pair-Linking is simple: instead of considering all the given mentions, Pair-Linking iteratively selects a pair with the highest confidence at each step for decision making. Via extensive experiments on eight benchmark datasets, we show that our approach is not only more accurate but also surprisingly faster than many state-of-the-art collective linking algorithms
URI: https://hdl.handle.net/10356/106420
http://hdl.handle.net/10220/50043
ISSN: 1041-4347
DOI: http://dx.doi.org/10.1109/TKDE.2018.2857493
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TKDE.2018.2857493.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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