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Ontology Learning from Text

Abel Browarnik, Oded Maimon
2015 International Journal of Signs and Semiotic Systems  
Each of the tasks may use diverse methods, ranging from uses of Linguistic knowledge to Machine Learning.  ...  The goal of Ontology Learning from Text is to learn ontologies that represent domains or applications that change often. Manually learning and updating such ontologies is too expensive.  ...  In this paper we refer specifically to Ontology Learning from text. Surveys conducted since the early days of Ontology Learning show the different methods used to tackle the problem.  ... 
doi:10.4018/ijsss.2015070101 fatcat:exxt5piigjh63bnl55pfv4idcq

Ontology learning from text

Wilson Wong, Wei Liu, Mohammed Bennamoun
2012 ACM Computing Surveys  
The explosion of textual information on the Read/Write Web coupled with the increasing demand for ontologies to power the Semantic Web have made (semi-)automatic ontology learning from text a very promising  ...  This survey looks at how far we have come since the turn of the millennium and discusses the remaining challenges that will define the research directions in this area in the near future.  ...  This survey listed 36 approaches for ontology learning from text.  ... 
doi:10.1145/2333112.2333115 fatcat:4uo5bazvivh3fpbxj7yf4yoe2u

Learning ontologies from natural language texts

Mehrnoush Shamsfard, Ahmad Abdollahzadeh Barforoush
2004 International Journal of Human-Computer Studies  
and applying a symbolic, hybrid ontology learning approach consisting of logical, linguistic based, template driven and semantic analysis methods.  ...  Hasti is an ongoing project to implement and test the automatic ontology building approach. It extracts lexical and ontological knowledge from Persian (Farsi) texts.  ...  to learn ontologies from natural language texts.  ... 
doi:10.1016/j.ijhcs.2003.08.001 fatcat:65xzwnfwdnedfl6hnzbepl2qda

Issues in learning an ontology from text

Christopher Brewster, Simon Jupp, Joanne Luciano, David Shotton, Robert D Stevens, Ziqi Zhang
2009 BMC Bioinformatics  
The filtering of recognised terms from a heterogeneous corpus to focus upon those that are the topic of the ontology is identified to be one of the main challenges for research in ontology learning.  ...  This paper describes an experiment in ontology construction from text for the animal behaviour domain.  ...  Acknowledgements We would like to thank Anita de Waard of Elsevier for making the corpus available from the Journal Animal Behaviour.  ... 
doi:10.1186/1471-2105-10-s5-s1 pmid:19426458 pmcid:PMC2679401 fatcat:gvcpzt2ryjf3hi23vasfqz3woy

Joint learning of ontology and semantic parser from text [article]

Janez Starc, Dunja Mladenić
2016 arXiv   pre-print
In this work, we present a novel approach to joint learning of ontology and semantic parser from text.  ...  The method is based on semi-automatic induction of a context-free grammar from semantically annotated text. The grammar parses the text into semantic trees.  ...  Acknowledgements This work was supported by Slovenian Research Agency and the ICT Programme of the EC under XLike (FP7-ICT-288342-STREP) and XLime (FP7-ICT-611346).  ... 
arXiv:1601.00901v1 fatcat:q2cxb4wvpvdn5kk6llzkkmkfsq

Ontology Learning from Text Using Relational Concept Analysis

Mohamed Rouane Hacene, Amedeo Napoli, Petko Valtchev, Yannick Toussaint, Rokia Bendaoud
2008 2008 International MCETECH Conference on e-Technologies (mcetech 2008)  
We discuss as well the results of an application of the method to astronomy texts.  ...  We propose an approach for semi-automated construction of ontologies from text whose core component is a Relational Concept Analysis (RCA) framework which extends Formal Concept Analysis (FCA), a lattice-theory  ...  Related work A comprehensive survey of state-of-the-art in ontology learning from text was proposed by Buitlaar et al. in [2] .  ... 
doi:10.1109/mcetech.2008.29 fatcat:c4revy3prve3xcbpfmurcyldl4

Automatic Ontology Learning from Domain-Specific Short Unstructured Text Data [article]

Yiming Xu, Dnyanesh Rajpathak, Ian Gibbs, Diego Klabjan
2019 arXiv   pre-print
Ontology learning is non-trivial due to several reasons with limited amount of prior research work that automatically learns a domain specific ontology from data.  ...  In our work, we propose a two-stage classification system to automatically learn an ontology from unstructured text data.  ...  Conclusion We propose a two-stage classification system for automatically learning an ontology from unstructured text data.  ... 
arXiv:1903.04360v1 fatcat:iasak4nnz5ddldh7xjqz2tnkzm

An Overview of Ontology Learning Process from Arabic Text

Mariam Muhammed, Nesrine Azim, Mervat Gheith
2020 The Egyptian Journal of Language Engineering  
There is a lot of research work interested in Ontology Learning for Arabic texts.  ...  Most of these works focused on three main issues: extracting the terms, extracting the semantic relations, and building the ontology from the Arabic text.  ...  Table 4 shows a summary of some researches on Automatic Ontology Learning from Arabic Text.  ... 
doi:10.21608/ejle.2020.19841.1000 fatcat:s3fe6obmnjdixlr4zqvhtcepsu

From Text to Facts: Recognizing Ontological Facts for a New Application

Farhad Abedini, Seyedeh Masoumeh Mirhashem
2012 International Journal of Machine Learning and Computing  
Fact extraction methods are used for various aims. Here, a new method is introduced for a new applicationcomputing semantic relatedness of texts.  ...  Ontological facts are the facts about real world that are available in a knowledgebase called ontology. There are many systems to extract ontological facts from a text.  ...  CONCLUSION In this paper, the approach of extracting ontological facts from a text using YAGO ontology was presented.  ... 
doi:10.7763/ijmlc.2012.v2.110 fatcat:lrg2ig5yezb5nkov3qcfbuaydu

Ontological Learning from Weak Labels [article]

Larry Tang, Po Hao Chou, Yi Yu Zheng, Ziqian Ge, Ankit Shah, Bhiksha Raj
2022 arXiv   pre-print
Ontologies encompass a formal representation of knowledge through the definition of concepts or properties of a domain, and the relationships between those concepts.  ...  ontology information to learn the concepts.  ...  Feature Extraction using Ontologies In a recent survey paper [3] , they discuss how the use of ontologies for feature selection have focused on applications in text classification.  ... 
arXiv:2203.02483v1 fatcat:jjaaruwyjvatljjwpypnjhuezq

A Concise Survey on Datasets, Tools and Methods for Biomedical Text

R. Johnsi, G. Bharadwaja Kumar, Tulasi Prasad Sariki
2022 International Journal of Applied Engineering Research  
In today's advancement of the digital world, a huge amount of Biomedical information in the form of text data is accessible from research articles as well as blogs.  ...  Biomedical text mining aims to retrieve useful information from large data efficiently and convert it into practical usage in a way of diagnosing symptoms, prevention, and treatment of diseases.  ...  Building a gazetteer that incorporates all of the new terminology is really tough. 3 Relation Extraction Learning based methods used in Clinical Text Data Table: 4 Relation Extractions in Biomedical  ... 
doi:10.37622/ijaer/17.3.2022.200-217 fatcat:2cw7f572rjfqvbnbrpq7gc75ue

Ontology Learning Part One — on Discovering Taxonomic Relations from the Web [chapter]

Alexander Maedche, Viktor Pekar, Steffen Staab
2003 Web Intelligence  
The purpose of the chapter is to give a survey of existing work on learning taxonomic relations from texts and an example of how such learning may be performed and evaluated.  ...  Conclusion In this section we have surveyed symbolic and statistical means for extracting taxonomic relations from text.  ... 
doi:10.1007/978-3-662-05320-1_14 fatcat:xlwrtgwwlvhy5mzmwhui4wdmja

Learning a Large Scale of Ontology from Japanese Wikipedia

Susumu Tamagawa, Shinya Sakurai, Takuya Tejima, Takeshi Morita, Noriaki Izumi, Takahira Yamaguchi
2010 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology  
Here is discussed how to learn a large scale of ontology from Japanese Wikipedia.  ...  Experimental case studies show us that the learned Japanese Wikipedia Ontology goes better than already existing general linguistic ontologies, such as EDR and Japanese WordNet, from the points of building  ...  Thus, ontology learning from Wikipedia becomes popular.This paper proposes and evaluates a large-scale, general-purpose ontology (called Wikipedia Ontology below) learning method using the Japanese Wikipedia  ... 
doi:10.1109/wi-iat.2010.177 dblp:conf/webi/TamagawaSTMIY10 fatcat:663ocy7nqrhh5fs7da3h6mkgpa

Learning to discover complex mappings from web forms to ontologies

Yuan An, Xiaohua Hu, Il-Yeol Song
2012 Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12  
We present a machine learning-based automatic approach for discovering complex mappings from Web forms to ontologies.  ...  A complex mapping associates a set of semantically related elements on a form to a set of semantically related elements in an ontology.  ...  Specifically, a single text box under a text label is removed from the tree, because the text box is an input field which is used to collect the values of the object represented by the text label.  ... 
doi:10.1145/2396761.2398427 dblp:conf/cikm/AnHS12 fatcat:6esinx7kpbdr7g53hccl52ek6u

Protein function prediction with gene ontology: from traditional to deep learning models

Thi Thuy Duong Vu, Jaehee Jung
2021 PeerJ  
Herein, we reviewed the currently available computational GO annotation methods for proteins, ranging from conventional to deep learning approach.  ...  Further, we selected some suitable predictors from among the reviewed tools and conducted a mini comparison of their performance using a worldwide challenge dataset.  ...  SURVEY METHODOLOGY Based on previous studies (Jung & Thon, 2006; Jung et al., 2010) and the aforementioned surveys, our review is divided in two main parts offering an overview of the field (from its  ... 
doi:10.7717/peerj.12019 pmid:34513334 pmcid:PMC8395570 fatcat:fs7h6imkyvfw3bemq4he6miqce
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