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.

/pdf/named-entity-extraction-for-knowledge-graphs-a-literature-2k1xjwodjd.pdf

Named Entity Extraction for Knowledge Graphs: A Literature Overview

Conventional knowledge graph embedding (KGE) often suffers from limited knowledge representation, leading to performance degradation especially on the low-resource problem. To remedy this, we propose to enrich knowledge representation via pretrained language models by leveraging world knowledge from pretrained models. Specifically, we present a universal training framework named Pretrain-KGE consisting of three phases: semantic-based fine-tuning phase, knowledge extracting phase and KGE training phase. Extensive experiments show that our proposed Pretrain-KGE can improve results over KGE models, especially on solving the low-resource problem.

/pdf/pretrain-kge-learning-knowledge-representation-from-4ysj2myhsy.pdf

Pretrain-KGE: Learning Knowledge Representation from Pretrained Language Models

Entity linking involves mapping ambiguous mentions in documents to the correct entities in a given knowledge base. Most of the current methods are a combination of local and global models. The local model uses the local context information around the entity mention to independently resolve the ambiguity of each entity mention. The global model encourages thematic consistency across the target entities of all mentions in the document. However, the known global models calculate the correlation between entities from a semantic perspective, ignoring the correlation information between entities in nature. In this paper, we introduce knowledge graphs to enrich the correlation information between entities and propose an entity linking model that introduces the structural information of the knowledge graph (KGEL). The model can fully consider the relations between entities. To prove the importance of the knowledge graph structure, extensive experiments are conducted on multiple public datasets. Results illustrate that our model outperforms the baseline and achieves superior performance.

/pdf/improving-entity-linking-by-introducing-knowledge-graph-1hc2brp4.pdf

Improving Entity Linking by Introducing Knowledge Graph Structure Information

A massive amount of news is being shared online by individuals and news agencies, making it difficult to take advantage of these news and analyse them in traditional ways. In view of this, there is an urgent need to use recent technologies to analyse all news relevant information that is being shared in natural language and convert it into forms that can be more easily and precisely processed by computers. Knowledge Graphs (KGs) offer offer a good solution for such processing. Natural Language Processing (NLP) offers the possibility for mining and lifting natural language texts to knowledge graphs allowing to exploit its semantic capabilities, facilitating new possibilities for news analysis and understanding. However, the current available techniques are still away from perfect. Many approaches and frameworks have been proposed to track and analyse news in the last few years. The shortcomings of those systems are that they are static and not updateable, are not designed for largescale data volumes, did not support real-time processing, dealt with limited data resources, used traditional lifting pipelines and supported limited tasks, or have neglected the use of knowledge graphs to represent news into a computer-processable form. Therefore, there is a need to better support lifting natural language into a KG. With the continuous development of NLP techniques, the design of new dynamic NLP lifters that can cope with all the previous shortcomings is required. This paper introduces a general NLP lifting architecture for automatically lifting and processing news reports in real-time based on the recent development of the NLP methods.

/pdf/lifting-news-into-a-journalistic-knowledge-platform-4ghb57xqwg.pdf

Lifting news into a journalistic knowledge platform

https://www.techscience.com/csse/v37n3/41711/pdf

Chinese Relation Extraction on Forestry Knowledge Graph Construction

Deep Semantic Match Model for Entity Linking Using Knowledge Graph and Text

The invention discloses a DSMM (deep semantic match model) entity linking method based on a multi-granularity LSTM (long short term memory) network, and belongs to the field of information processing.The method is characterized by comprising steps as follows: firstly, extracting surface form feature representations of an entity reference and a candidate entity by using a character-level bidirectional LSTM network; then, encoding the sentence in which the entity reference is located by adopting a word-level bidirectional LSTM network, outputting the sentence as a context semantic feature vector of the entity reference, and learning the context semantic feature vector of the candidate entity based on information of a structured knowledge map; finally, calculating similarity scores of the surface form and the semantics for the entity reference, the surface form of the candidate entity and the context semantic feature vector respectively, and combining the similarity scores as a final score of the entity reference and candidate entity pair. The entity link effect is improved by combining the multi-granularity LSTM network and the knowledge representation learning method.

DSMM (Deep semantic match model) entity linking method based on multi-granularity LSTM (long short term memory) network

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Yajing Xu

Author Tools

Chat about Author

Papers

Deep Semantic Match Model for Entity Linking Using Knowledge Graph and Text

DSMM (Deep semantic match model) entity linking method based on multi-granularity LSTM (long short term memory) network