Linking Entities to Wikipedia Documents (original) (raw)

Supervised Learning for Linking Named Entities to Knowledge Base Entries

2011

Abstract This paper addresses the challenging information extraction problem of linking named entities in text to entries in a knowledge base. Our approach uses supervised learning to (a) rank candidate knowledge base entries for each named entity,(b) classify the top-ranked entry as the correct disambiguation or not, and (c) group together the named entities without a corresponding entry in the knowledge base.

Learning to link with wikipedia

International Conference on Information and Knowledge Management, Proceedings, 2008

This paper describes how to automatically cross-reference documents with Wikipedia: the largest knowledge base ever known. It explains how machine learning can be used to identify significant terms within unstructured text, and enrich it with links to the appropriate Wikipedia articles. The resulting link detector and disambiguator performs very well, with recall and precision of almost 75%. This performance is constant whether the system is evaluated on Wikipedia articles or "real world" documents. This work has implications far beyond enriching documents with explanatory links. It can provide structured knowledge about any unstructured fragment of text. Any task that is currently addressed with bags of words-indexing, clustering, retrieval, and summarization to name a few-could use the techniques described here to draw on a vast network of concepts and semantics.

Entity linking leveraging: automatically generated annotation

2010

Entity linking refers entity mentions in a document to their representations in a knowledge base (KB). In this paper, we propose to use additional information sources from Wikipedia to find more name variations for entity linking task. In addition, as manually creating a training corpus for entity linking is laborintensive and costly, we present a novel method to automatically generate a large scale corpus annotation for ambiguous mentions leveraging on their unambiguous synonyms in the document collection. Then, a binary classifier is trained to filter out KB entities that are not similar to current mentions. This classifier not only can effectively reduce the ambiguities to the existing entities in KB, but also be very useful to highlight the new entities to KB for the further population. Furthermore, we also leverage on the Wikipedia documents to provide additional information which is not available in our generated corpus through a domain adaption approach which provides further performance improvements. The experiment results show that our proposed method outperforms the state-of-the-art approaches.

Nus-i2r: Learning a combined system for entity linking

2010

In this paper, we report the joint participation of NUS and I2R team in Knowledge Base Population at Text analysis conference 2010. For Entity Linking, we analyze IR approaches and SVM classification in the disambiguation stage and develop a supervised learner for combining these approaches. The combined system performs better than the individual components and achieves results much better than the median. Furthermore, according to our error analysis, quite some errors are caused due to the different Wikipedia version is used, which hinder our system to show significant better performance.

Dexter: an open source framework for entity linking

We introduce Dexter, an open source framework for entity linking. The entity linking task aims at identifying all the small text fragments in a document referring to an entity contained in a given knowledge base, e.g., Wikipedia. The annotation is usually organized in three tasks. Given an input document the first task consists in discovering the fragments that could refer to an entity. Since a mention could refer to multiple entities, it is necessary to perform a disambiguation step, where the correct entity is selected among the candidates. Finally, discovered entities are ranked by some measure of relevance. Many entity linking algorithms have been proposed, but unfortunately only a few authors have released the source code or some APIs. As a result, evaluating today the performance of a method on a single subtask, or comparing different techniques is difficult. In this work we present a new open framework, called Dexter, which implements some popular algorithms and provides all the tools needed to develop any entity linking technique. We believe that a shared framework is fundamental to perform fair comparisons and improve the state of the art.

Survey on English Entity Linking on Wikidata: Datasets and approaches

Semantic Web, 2022

Wikidata is a frequently updated, community-driven, and multilingual knowledge graph. Hence, Wikidata is an attractive basis for Entity Linking, which is evident by the recent increase in published papers. This survey focuses on four subjects: (1) Which Wikidata Entity Linking datasets exist, how widely used are they and how are they constructed? (2) Do the characteristics of Wikidata matter for the design of Entity Linking datasets and if so, how? (3) How do current Entity Linking approaches exploit the specific characteristics of Wikidata? (4) Which Wikidata characteristics are unexploited by existing Entity Linking approaches? This survey reveals that current Wikidata-specific Entity Linking datasets do not differ in their annotation scheme from schemes for other knowledge graphs like DBpedia. Thus, the potential for multilingual and time-dependent datasets, naturally suited for Wikidata, is not lifted. Furthermore, we show that most Entity Linking approaches use Wikidata in the ...

Mining and Leveraging Background Knowledge for Improving Named Entity Linking

Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics, 2018

Knowledge-rich Information Extraction (IE) methods aspire towards combining classical IE with background knowledge obtained from third-party resources. Linked Open Data repositories that encode billions of machine readable facts from sources such as Wikipedia play a pivotal role in this development. The recent growth of Linked Data adoption for Information Extraction tasks has shed light on many data quality issues in these data sources that seriously challenge their usefulness such as completeness, timeliness and semantic correctness. Information Extraction methods are, therefore, faced with problems such as name variance and type confusability. If multiple linked data sources are used in parallel, additional concerns regarding link stability and entity mappings emerge. This paper develops methods for integrating Linked Data into Named Entity Linking methods and addresses challenges in regard to mining knowledge from Linked Data, mitigating data quality issues, and adapting algorithms to leverage this knowledge. Finally, we apply these methods to Recognyze, a graph-based Named Entity Linking (NEL) system, and provide a comprehensive evaluation which compares its performance to other well-known NEL systems, demonstrating the impact of the suggested methods on its own entity linking performance.

Entity Disambiguation and Linking over Queries using Encyclopedic Knowledge

Literature has seen a large amount of work on entity recognition and semantic disambiguation in text but very limited on the effect in noisy text data. In this paper, we present an approach for recognizing and disambiguating entities in text based on the high coverage and rich structure of an online encyclopedia. This work was carried out on a collection of query logs from the Bridgeman Art Library. As queries are noisy unstructured text, pure natural language processing as well as computational techniques can create problems, we need to contend with the impact noise and the demands it places on query analysis. In order to cope with the noisy input, we use machine learning method with statistical measures derived from Wikipedia. It provides a huge electronic text from the Internet, which is also noisy. Our approach is an unsupervised approach and do not need any manual annotation made by human experts. We show that data collection from Wikipedia can be used statistically to derive good performance for entity recognition and semantic disambiguation over noisy unstructured text. Also, as no natural language specific tool is needed, the method can be applied to other languages in a similar manner with little adaptation.

BUAP_1: A Naïve Approach to the Entity Linking Task

In these notes we are reporting the obtained results by applying the Naïve Bayes classifier to the Entity Linking task of the Knowledge Base Population track at the Text Analysis Conference. Three different runs were submitted to the challenge, each with different ways of approaching the application of the above mentioned classifier. The obtained results were very low, and recent analyses showed that this issue was derived from errors at the pre-processing stage.

High-Throughput and Language-Agnostic Entity Disambiguation and Linking on User Generated Data

ArXiv, 2017

The Entity Disambiguation and Linking (EDL) task matches entity mentions in text to a unique Knowledge Base (KB) identifier such as a Wikipedia or Freebase id. It plays a critical role in the construction of a high quality information network, and can be further leveraged for a variety of information retrieval and NLP tasks such as text categorization and document tagging. EDL is a complex and challenging problem due to ambiguity of the mentions and real world text being multi-lingual. Moreover, EDL systems need to have high throughput and should be lightweight in order to scale to large datasets and run on off-the-shelf machines. More importantly, these systems need to be able to extract and disambiguate dense annotations from the data in order to enable an Information Retrieval or Extraction task running on the data to be more efficient and accurate. In order to address all these challenges, we present the Lithium EDL system and algorithm - a high-throughput, lightweight, language...