Linking named entities in Tweets with knowledge base via user interest modeling

2013 ◽  
Author(s):  
Wei Shen ◽  
Jianyong Wang ◽  
Ping Luo ◽  
Min Wang
Keyword(s):  
Knowledge Base ◽  
User Interest ◽  
Named Entities

Effective Entity Linking and Disambiguation Algorithms for User-Generated Content (UGC)

2018 ◽  
pp. 416-433
Author(s):  
Senthil Kumar Narayanasamy ◽  
Dinakaran Muruganantham
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Information Extraction ◽  
Knowledge Base ◽  
Exponential Growth ◽  
Extraction Process ◽  
Unstructured Data ◽  
User Generated Content ◽  
Entity Linking ◽  
Named Entities

The exponential growth of data emerging out of social media is causing challenges in decision-making systems and poses a critical hindrance in searching for the potential information. The major objective of this chapter is to convert the unstructured data in social media into the meaningful structure format, which in return brings the robustness to the information extraction process. Further, it has the inherent capability to prune for named entities from the unstructured data and store the entities into the knowledge base for important facts. In this chapter, the authors explain the methods to identify all the critical interpretations taken over to find the named entities from Twitter streams and the techniques to proportionally link it with appropriate knowledge sources such as DBpedia.

WikiIdRank++: EXTENSIONS AND IMPROVEMENTS OF THE WikiIdRank SYSTEM FOR ENTITY LINKING

2013 ◽  
Vol 22 (03) ◽  
pp. 1350018 ◽  
Author(s):  
M. D. JIMÉNEZ ◽  
N. FERNÁNDEZ ◽  
J. ARIAS FISTEUS ◽  
L. SÁNCHEZ
Keyword(s):  
Knowledge Base ◽  
Text Analysis ◽  
Base Population ◽  
Entity Linking ◽  
Named Entities ◽  
Named Entity ◽  
Global Comparison ◽  
Knowledge Base Population ◽  
Reference Knowledge ◽  
Unstructured Information

The amount of information available on the Web has grown considerably in recent years, leading to the need to structure it in order to access it in a quick and accurate way. In order to develop techniques to automate the structuring process, the Knowledge Base Population (KBP) track of the Text Analysis Conference (TAC) was created. This forum aims to encourage research in automated systems capable of capturing knowledge in unstructured information. One of the tasks proposed in the context of the KBP track is named entity linking, and its goal is to link named entities mentioned in a document to instances in a reference knowledge base built from Wikipedia. This paper focuses on the entity linking task in the context of KBP 2010, where two different varieties of this task were considered, depending on whether the use of the text from Wikipedia was allowed or not. Specifically, the paper proposes a set of modifications to a system that participated in KBP 2010, named WikiIdRank, in order to improve its performance. The different modifications were evaluated in the official KBP 2010 corpus, showing that the best combination increases the accuracy of the initial system in a 7.04%. Though the resultant system, named WikiIdRank++, is unsupervised and does not take advantage of Wikipedia text, a comparison with other approaches in KBP indicates that the system would rank as 4th (out of 16) in the global comparison, outperforming other approaches that use human supervision and take advantage of Wikipedia textual contents. Furthermore, the system would rank as 1st in the category of systems that do not use Wikipedia text.

scholarly journals Improving Named Entity Recognition in Vietnamese Texts by a Character-Level Deep Lifelong Learning Model

2019 ◽  
Vol 06 (04) ◽  
pp. 471-487 ◽  
Author(s):  
Ngoc-Vu Nguyen ◽  
Thi-Lan Nguyen ◽  
Cam-Van Nguyen Thi ◽  
Mai-Vu Tran ◽  
Tri-Thanh Nguyen ◽  
...  
Keyword(s):  
Knowledge Base ◽  
Lifelong Learning ◽  
Named Entity Recognition ◽  
Parameter Tuning ◽  
Learning Model ◽  
Entity Recognition ◽  
Named Entities ◽  
Named Entity ◽  
Cross Domain ◽  
Learning Tasks

Named entity recognition (NER) is a fundamental task which affects the performance of its dependent task, e.g. machine translation. Lifelong machine learning (LML) is a continuous learning process, in which the knowledge base accumulated from previous tasks will be used to improve future learning tasks having few samples. Since there are a few studies on LML based on deep neural networks for NER, especially in Vietnamese, we propose a lifelong learning model based on deep learning with a CRFs layer, named DeepLML–NER, for NER in Vietnamese texts. DeepLML–NER includes an algorithm to extract the knowledge of “prefix-features” of named entities in previous domains. Then the model uses the knowledge in the knowledge base to solve the current NER task. Preprocessing and model parameter tuning are also investigated to improve the performance. The effect of the model was demonstrated by in-domain and cross-domain experiments, achieving promising results.

Disambiguating the Twitter Stream Entities and Enhancing the Search Operation Using DBpedia Ontology

2016 ◽  
Vol 11 (2) ◽  
pp. 51-62 ◽  
Author(s):  
N. Senthil Kumar ◽  
Dinakaran Muruganantham
Keyword(s):  
Knowledge Base ◽  
Structured Data ◽  
Unstructured Data ◽  
Social Web ◽  
Huge Amount ◽  
Named Entities ◽  
Named Entity ◽  
Semantic Classes ◽  
Search Operation ◽  
Principal Task

The web and social web is holding the huge amount of unstructured data and makes the searching processing more cumbersome. The principal task here is to migrate the unstructured data into the structured data through the appropriate utilization of named entity detections. The goal of the paper is to automatically build and store the deep knowledge base of important facts and construct the comprehensive details about the facts such as its related named entities, its semantic classes of the entities and its mutual relationship with its temporal context can be thoroughly analyzed and probed. In this paper, the authors have given and proposed the model to identify all the major interpretations of the named entities and effectively link them to the appropriate mentions of the knowledge base (DBpedia). They finally evaluate the approaches that uniquely identify the DBpedia URIs of the selected entities and eliminate the other candidate mentions of the entities based on the authority rankings of those candidate mentions.

scholarly journals Semantic based entity retrieval and disambiguation system for Twitter streams

2019 ◽  
pp. 262-280
Keyword(s):  
Social Media ◽  
Knowledge Base ◽  
Semantic Similarity ◽  
Noun Phrases ◽  
User Generated Content ◽  
Information Searching ◽  
Named Entities ◽  
Social Media Networks ◽  
Entity Retrieval ◽  
Potential Sources

Social media networks have evolved as a large repository of short documents and gives the greater challenges to effectively retrieve the content out of it. Many factors were involved in this process such as restricted length of a content, informal use of language (i.e., slangs, abbreviations, styles, etc.) and low contextualization of the user generated content. To meet out the above stated problems, latest studies on context-based information searching have been developed and built on adding semantics to the user generated content into the existing knowledge base. And also, earlier, bag-of-concepts has been used to link the potential noun phrases into existing knowledge sources. Thus, in this paper, we have effectively utilized the relationships among the concepts and equivalence prevailing in the related concepts of the selected named entities by deriving the potential meaning of entities and find the semantic similarity between the named entities with three other potential sources of references (DBpedia, Anchor Texts and Twitter Trends).

scholarly journals Semantic representation of scientific literature: bringing claims, contributions and named entities onto the Linked Open Data cloud

2015 ◽  
Vol 1 ◽  
pp. e37 ◽  
Author(s):  
Bahar Sateli ◽  
René Witte
Keyword(s):  
Text Mining ◽  
Knowledge Base ◽  
Language Processing ◽  
Digital Libraries ◽  
Scientific Literature ◽  
Open Data ◽  
Semantic Knowledge ◽  
Linked Open Data ◽  
Named Entities ◽  
Semantic Annotations

Motivation.Finding relevant scientific literature is one of the essential tasks researchers are facing on a daily basis. Digital libraries and web information retrieval techniques provide rapid access to a vast amount of scientific literature. However, no further automated support is available that would enable fine-grained access to the knowledge ‘stored’ in these documents. The emerging domain ofSemantic Publishingaims at making scientific knowledge accessible to both humans and machines, by adding semantic annotations to content, such as a publication’s contributions, methods, or application domains. However, despite the promises of better knowledge access, the manual annotation of existing research literature is prohibitively expensive for wide-spread adoption. We argue that a novel combination of three distinct methods can significantly advance this vision in a fully-automated way: (i) Natural Language Processing (NLP) forRhetorical Entity(RE) detection; (ii)Named Entity(NE) recognition based on the Linked Open Data (LOD) cloud; and (iii) automatic knowledge base construction for both NEs and REs using semantic web ontologies that interconnect entities in documents with the machine-readable LOD cloud.Results.We present a complete workflow to transform scientific literature into a semantic knowledge base, based on the W3C standards RDF and RDFS. A text mining pipeline, implemented based on the GATE framework, automatically extracts rhetorical entities of typeClaimsandContributionsfrom full-text scientific literature. These REs are further enriched with named entities, represented as URIs to the linked open data cloud, by integrating the DBpedia Spotlight tool into our workflow. Text mining results are stored in a knowledge base through a flexible export process that provides for a dynamic mapping of semantic annotations to LOD vocabularies through rules stored in the knowledge base. We created a gold standard corpus from computer science conference proceedings and journal articles, whereClaimandContributionsentences are manually annotated with their respective types using LOD URIs. The performance of the RE detection phase is evaluated against this corpus, where it achieves an averageF-measure of 0.73. We further demonstrate a number of semantic queries that show how the generated knowledge base can provide support for numerous use cases in managing scientific literature.Availability.All software presented in this paper is available under open source licenses athttp://www.semanticsoftware.info/semantic-scientific-literature-peerj-2015-supplements. Development releases of individual components are additionally available on our GitHub page athttps://github.com/SemanticSoftwareLab.

scholarly journals Knowledge Base Question Answering with Topic Units

2019 ◽  
Author(s):  
Yunshi Lan ◽  
Shuohang Wang ◽  
Jing Jiang
Keyword(s):  
Natural Language Processing ◽  
Reinforcement Learning ◽  
Knowledge Base ◽  
Language Processing ◽  
Question Answering ◽  
State Of The Art ◽  
Entity Linking ◽  
Named Entities ◽  
Benchmark Datasets ◽  
Previous State

Knowledge base question answering (KBQA) is an important task in natural language processing. Existing methods for KBQA usually start with entity linking, which considers mostly named entities found in a question as the starting points in the KB to search for answers to the question. However, relying only on entity linking to look for answer candidates may not be sufficient. In this paper, we propose to perform topic unit linking where topic units cover a wider range of units of a KB. We use a generation-and-scoring approach to gradually refine the set of topic units. Furthermore, we use reinforcement learning to jointly learn the parameters for topic unit linking and answer candidate ranking in an end-to-end manner. Experiments on three commonly used benchmark datasets show that our method consistently works well and outperforms the previous state of the art on two datasets.

Sign in / Sign up

Export Citation Format

Share Document