Linking Entities to a Knowledge Base with Query Expansion

Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University)Published 27 July 2011

Swapna Gottipati, Jing Jiang

Source URL

Citations108

TL;DR

A novel approach to entity linking based on a statistical language model-based information retrieval with query expansion with a strong emphasis on named entities in the local contexts and a positional language model to weigh them differently based on their distances to the query.

Abstract

In this paper we present a novel approach to entity linking based on a statistical language model-based information retrieval with query expansion. We use both local contexts and global world knowledge to expand query language models. We place a strong emphasis on named entities in the local contexts and explore a positional language model to weigh them differently based on their distances to the query. Our experiments on the TAC-KBP 2010 data show that incorporating such contextual information indeed aids in disambiguating the named entities and consistently improves the entity linking performance. Compared with the official results from KBP 2010 participants, our system shows competitive performance. 1

Keywords

Computer Science

ACM SIGIR ForumRelevance-Based Language Models

1,449 Citations2017Victor Lavrenko, W. Bruce Croft

This work proposes a novel technique for estimating a relevance model with no training data and demonstrates that it can produce highly accurate relevance models, addressing important notions of synonymy and polysemy.

ACM Transactions on Information SystemsA study of smoothing methods for language models applied to information retrieval

1,212 Citations2004ChengXiang Zhai, John Lafferty

Evaluation on five different databases and four types of queries indicates that the two-stage smoothing method with the proposed parameter estimation methods consistently gives retrieval performance that is close to or better than the best results achieved using a single smoothing methods and exhaustive parameter search on the test data.

Large-Scale Named Entity Disambiguation Based on Wikipedia Data

1,072 Citations2007Silviu Cucerzan

Through a process of maximizing the agreement between the contextual information extracted from Wikipedia and the context of a document, as well as the agreement among the category tags associated with the candidate entities, the implemented system shows high disambiguation accuracy on both news stories and Wikipedia articles.

Using Encyclopedic Knowledge for Named Entity Disambiguation

812 Citations2006Răzvan Bunescu, Marius Paşca

A disambiguation SVM kernel is trained to exploit the high coverage and rich structure of the knowledge encoded in an online encyclopedia and significantly outperforms a less informed baseline.

Model-based feedback in the language modeling approach to information retrieval

799 Citations2001ChengXiang Zhai, John Lafferty

This paper proposes and evaluates two different approaches to updating a query language model based on feedback documents, one based on a generative probabilistic model of feedback documents and onebased on minimization of the KL-divergence over feedback documents.

ACM SIGIR ForumDocument Language Models, Query Models, and Risk Minimization for Information Retrieval

774 Citations2017John Lafferty, ChengXiang Zhai

A framework for information retrieval that combines document models and query models using a probabilistic ranking function based on Bayesian decision theory is presented and an operational retrieval model that extends recent developments in the language modeling approach to information retrieval is suggested.

Overview of the TAC 2010 Knowledge Base Population Track

419 Citations2010Heng Ji, Ralph Grishman +3 more

An overview of the task definition and annotation challenges associated with KBP2010 is provided and the evaluation results and lessons that are learned are discussed based on detailed analysis.

Entity Disambiguation for Knowledge Base Population

343 Citations2010Mark Dredze, Paul McNamee +3 more

This work presents a state of the art system for entity disambiguation that not only addresses challenges but also scales to knowledge bases with several million entries using very little resources.

View PDF

Positional relevance model for pseudo-relevance feedback

214 Citations2010Yuanhua Lv, ChengXiang Zhai

The proposed positional relevance model (PRM) is an extension of the relevance model to exploit term positions and proximity so as to assign more weights to words closer to query words based on the intuition that words closerto query words are more likely to be related to the query topic.

A comparative study of methods for estimating query language models with pseudo feedback

207 Citations2009Yuanhua Lv, ChengXiang Zhai

This work systematically compares five representative state-of-the-art methods for estimating query language models with pseudo feedback in ad hoc information retrieval, and proposes several heuristics that are intuitively related to the good retrieval performance of an estimation method.