CohEEL: Coherent and efficient named entity linking through random walks

Gruetze, Toni; Kasneci, Gjergji; Zuo, Zhe; Naumann, Felix

doi:10.1016/j.websem.2016.03.001

Cited by 20 publications

(9 citation statements)

References 26 publications

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“…NER results impact later tasks such as NED and NEL [146], [147], which in turn are highly affected by the type and quality of the documents used for training [146]. KG researchers have been focusing more on the KG-construction side and have paid less attention to the NER task, which is usually done by off-the-shelf systems or established tools.…”

Section: F Main Findings and Open Directionsmentioning

confidence: 99%

Named Entity Extraction for Knowledge Graphs: A Literature Overview

et al. 2020

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An enormous amount of digital information is expressed as natural-language (NL) text that is not easily processable by computers. Knowledge Graphs (KG) offer a widely used format for representing information in computer-processable form. Natural Language Processing (NLP) is therefore needed for mining (or lifting) knowledge graphs from NL texts. A central part of the problem is to extract the named entities in the text. The paper presents an overview of recent advances in this area, covering: Named Entity Recognition (NER), Named Entity Disambiguation (NED), and Named Entity Linking (NEL). We comment that many approaches to NED and NEL are based on older approaches to NER and need to leverage the outputs of state-of-the-art NER systems. There is also a need for standard methods to evaluate and compare named-entity extraction approaches. We observe that NEL has recently moved from being stepwise and isolated into an integrated process along two dimensions: the first is that previously sequential steps are now being integrated into end-to-end processes, and the second is that entities that were previously analysed in isolation are now being lifted in each other's context. The current culmination of these trends are the deep-learning approaches that have recently reported promising results.

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Section: F Main Findings and Open Directionsmentioning

confidence: 99%

Named Entity Extraction for Knowledge Graphs: A Literature Overview

et al. 2020

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“…Both approaches involved constructing a map in which the nodes are entity-mention pairs. Then, all the nodes are sorted by a random walk [22] process. Modeling a wide variety of information through a simple graph is not appropriate because the simple graph contains very limited information.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach for Analyzing Entity Linking Between Words and Entities for a Knowledge Base Using an Attention-Based Bilinear Joint Learning and Weighted Summation Model

2020

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Entity linking (EL) is a task about natural language that links mentions of entities in text to corresponding entities that are in a knowledge base. Potential applications include question-answering systems, information extraction and knowledge base population (KBP). The key to structuring an EL system that has high quality involves creating careful representations of words and entities. However, a hypothesis that whole words have the same weight in their context exists in most previous methods, which causes the meanings of words to be biased. In this paper, a novel approach to analyze entity linking between words and entities for a knowledge base using attention-based bilinear joint learning is proposed. First, the approach designs a novel encoding method to model entities and words in EL. The method learns words and entities in a joint way and uses an attention mechanism to obtain different importance values in the context. Second, the approach introduces a weighted summation method to form the textual context and introduces the method with same line of reasoning to model coherence to improve ranking the features. Finally, the approach employs a pairwise boosting regression tree (PBRT) to rank the candidate entities. During the ranking, the approach takes features constructed with a weighted summation model and conventional EL features as the input. Through experiments, it demonstrates that compared with other state-of-the-art methods, the proposed model learns embeddings efficiently and improves EL performance. Our approach achieves progressive results on CoNLL and TAC 2010 datasets.

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“…Entity linking approaches [52] have been proposed to identify meaningful fragments in short posts and link them to external resources such as Wikipedia pages or DBpedia resources [22][23][24][25]. These approaches identify topics related to single posts.…”

Section: Plos Onementioning

confidence: 99%

“…Other ontologies that Topico refers to are DBpedia to express encyclopedic concepts, (FOAF) [66] to express agents (with emphasis on people), W3C basic Geo vocabulary [67,68] and Geonames [69] to express geolocations, Schema.org [70,71] to express persons and locations, and W3C time [72] to express intervals of topics. The namespace prefixes that are referred to in this paper are given in S1 Table. Entity linking is used to identify fragments within text documents (surface forms or spots) and link them to external resources that represent real-world entities (i.e., dictionaries and/or encyclopedias such as Wikipedia) [52]. Entity linking for microposts is challenging due to the use of unstructured and untidy language as well as the limited context of short texts.…”

Section: Plos Onementioning

confidence: 99%

“…Entity linking is used to identify fragments within text documents (surface forms or spots) and link them to external resources that represent real-world entities (i.e., dictionaries and/or encyclopedias such as Wikipedia) [ 52 ]. Entity linking for microposts is challenging due to the use of unstructured and untidy language as well as the limited context of short texts.…”

Section: Introductionmentioning

confidence: 99%

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Microblog topic identification using Linked Open Data

Yildirim

Uskudarli

2020

PLoS ONE

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Much valuable information is embedded in social media posts (microposts) which are contributed by a great variety of persons about subjects that of interest to others. The automated utilization of this information is challenging due to the overwhelming quantity of posts and the distributed nature of the information related to subjects across several posts. Numerous approaches have been proposed to detect topics from collections of microposts, where the topics are represented by lists of terms such as words, phrases, or word embeddings. Such topics are used in tasks like classification and recommendations. The interpretation of topics is considered a separate task in such methods, albeit they are becoming increasingly human-interpretable. This work proposes an approach for identifying machineinterpretable topics of collective interest. We define topics as a set of related elements that are associated by having posted in the same contexts. To represent topics, we introduce an ontology specified according to the W3C recommended standards. The elements of the topics are identified via linking entities to resources published on Linked Open Data (LOD). Such representation enables processing topics to provide insights that go beyond what is explicitly expressed in the microposts. The feasibility of the proposed approach is examined by generating topics from more than one million tweets collected from Twitter during various events. The utility of these topics is demonstrated with a variety of topic-related tasks along with a comparison of the effort required to perform the same tasks with words-list-based representations. Manual evaluation of randomly selected 36 sets of topics yielded 81.0% and 93.3% for the precision and F1 scores respectively.

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CohEEL: Coherent and efficient named entity linking through random walks

Cited by 20 publications

References 26 publications

Named Entity Extraction for Knowledge Graphs: A Literature Overview

Named Entity Extraction for Knowledge Graphs: A Literature Overview

A Novel Approach for Analyzing Entity Linking Between Words and Entities for a Knowledge Base Using an Attention-Based Bilinear Joint Learning and Weighted Summation Model

Microblog topic identification using Linked Open Data

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