Many Entity Linking systems use collective graph-based methods to disambiguate the entity mentions within a document. Most of them have focused on graph construction and initial weighting of the candidate entities, less attention has been devoted to compare the graph ranking algorithms. In this work, we focus on the graph-based ranking algorithms, therefore we propose to apply five centrality measures: Degree, HITS, PageRank, Betweenness and Closeness. A disambiguation graph of candidate entities is constructed for each document using the popularity method, then centrality measures are applied to choose the most relevant candidate to boost the results of entity popularity method. We investigate the effectiveness of each centrality measure on the performance across different domains and datasets. Our experiments show that a simple and fast centrality measure such as Degree centrality can outperform other more time-consuming measures.
The collective approach disambiguates jointly all entity mentions, it tries to model the interdependencies between the candidate entities for all mentions. This approach reformulates the problem as a global optimisation problem which is NP-hard for which many approximations have been proposed. @cite_3 proposed a local and global methods to address entity linking problem, while the local methods take into account the similarity between the mention and the candidate entity, the global one is based on the entire document and all mentions were disambiguated concurrently in order to produce coherent results. @cite_10 built a weighted graph of mentions and candidate entities, and computes a dense subgraph that approximates the best joint mention-entity mapping. A probabilistic method that makes use of graphical models to perform collective disambiguation @cite_14 . @cite_8 used PageRank to rank the candidate entity, @cite_6 proposed Personalized PageRank to filter out the noise introduced by incorrect candidate entities. AGISTICS @cite_16 used HITS algorithm to rank the graph nodes, while DOSER @cite_12 found that PageRank performs better on their disambiguation graph. @cite_11 proposed different centrality measures to collectively disambiguate the authors's names in a French corpus.
@cite_14 @cite_8 @cite_6 @cite_3 @cite_16 @cite_10 @cite_12 @cite_11
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Disambiguating entity references by annotating them with unique ids from a catalog is a critical step in the enrichment of unstructured content. In this paper, we show that topic models, such as Latent Dirichlet Allocation (LDA) and its hierarchical variants, form a natural class of models for learning accurate entity disambiguation models from crowd-sourced knowledge bases such as Wikipedia. Our main contribution is a semi-supervised hierarchical model called Wikipedia-based Pachinko Allocation Model (WPAM) that exploits: (1) All words in the Wikipedia corpus to learn word-entity associations (unlike existing approaches that only use words in a small fixed window around annotated entity references in Wikipedia pages), (2) Wikipedia annotations to appropriately bias the assignment of entity labels to annotated (and co-occurring unannotated) words during model learning, and (3) Wikipedia's category hierarchy to capture co-occurrence patterns among entities. We also propose a scheme for pruning spurious nodes from Wikipedia's crowd-sourced category hierarchy. In our experiments with multiple real-life datasets, we show that WPAM outperforms state-of-the-art baselines by as much as 16 in terms of disambiguation accuracy. The task of Named Entity Disambiguation is to map entity mentions in the document to their correct entries in some knowledge base. We present a novel graph-based disambiguation approach based on Personalized PageRank (PPR) that combines local and global evidence for disambiguation and effectively filters out noise introduced by incorrect candidates. Experiments show that our method outperforms state-of-the-art approaches by achieving 91.7 in microand 89.9 in macroaccuracy on a dataset of 27.8K named entity mentions. Disambiguating named entities in natural-language text maps mentions of ambiguous names onto canonical entities like people or places, registered in a knowledge base such as DBpedia or YAGO. This paper presents a robust method for collective disambiguation, by harnessing context from knowledge bases and using a new form of coherence graph. It unifies prior approaches into a comprehensive framework that combines three measures: the prior probability of an entity being mentioned, the similarity between the contexts of a mention and a candidate entity, as well as the coherence among candidate entities for all mentions together. The method builds a weighted graph of mentions and candidate entities, and computes a dense subgraph that approximates the best joint mention-entity mapping. Experiments show that the new method significantly outperforms prior methods in terms of accuracy, with robust behavior across a variety of inputs. Named Entity Disambiguation (NED) refers to the task of mapping different named entity mentions in running text to their correct interpretations in a specific knowledge base (KB). This paper presents a collective disambiguation approach using a graph model. All possible NE candidates are represented as nodes in the graph and associations between different candidates are represented by edges between the nodes. Each node has an initial confidence score, e.g. entity popularity. Page-Rank is used to rank nodes and the final rank is combined with the initial confidence for candidate selection. Experiments on 27,819 NE textual mentions show the effectiveness of using Page-Rank in conjunction with initial confidence: 87 accuracy is achieved, outperforming both baseline and state-of-the-art approaches.