Automatic Relation Extraction

Notes:

Relationship extraction typically involves using natural language processing (NLP) techniques to analyze text or XML documents and identify specific relationships between entities or concepts mentioned within them. This can include identifying semantic relationships such as “part-of,” “is-a,” or “has-a,” as well as more specific relationships such as “employee-of” or “located-in.” The goal of a relationship extraction task is to extract meaningful information from large sets of unstructured data and organize it into a structured format that can be easily analyzed and understood.

Automatic Relation Extraction (ARE) is a type of natural language processing (NLP) task that involves using algorithms and techniques to automatically identify and extract relationships between entities mentioned in text. This can include identifying semantic relationships such as “part-of,” “is-a,” or “has-a,” as well as more specific relationships such as “employee-of” or “located-in.”

ARE is used for a wide range of applications such as information extraction, knowledge base construction, and text summarization. For example, ARE can be used to extract structured information from unstructured text and create a knowledge base of facts and relationships that can be used to answer questions or make predictions. It can also be used to extract key information from large sets of text, such as news articles or scientific papers, to identify important trends or patterns. Additionally, it can be used in chatbot and virtual assistants to make them more intelligent by extracting the intent and entities from the user’s query.

The references below describe the problem of lack of fine-grained classification and corresponding relation signal words in “gene-mutation-disease” semantic types. It proposes a text-mining-assisted semantic type construction approach for automatic relation extraction from biomedical texts. It goes on to explain that automatic relation extraction is a process of semantic analysis and relationship extraction, and it can be used to extract relationships between concepts in teaching materials. The text also mentions that various computational methods have been employed for automatic relation extraction from biomedical texts, including pattern-based, feature-based and kernel-based methods. The text concludes that automatic relation extraction is a promising technique to systematically harness community knowledge and that methods for improved retrieval and automatic relation extraction are required for collecting structured information from published works.

  • Pattern-based methods: These methods rely on identifying specific patterns or templates in text that indicate the presence of a relationship between entities. These patterns can be defined manually or learned automatically from training data. They typically use regular expressions or other string matching techniques to identify the patterns in text. They are simple and efficient but can be limited in their ability to capture more complex or nuanced relationships.
  • Feature-based methods: These methods use a set of predefined features or attributes to represent the entities and relationships in text. These features can include things like word forms, part-of-speech tags, semantic roles, and syntactic dependencies. The relationships are then identified based on the presence or absence of specific features. They are more expressive than pattern-based methods, but require more manual feature engineering and may not be able to capture all possible relationships.
  • Kernel-based methods: These methods use kernel functions to compute the similarity between entities and relationships in text. Kernel functions map the input data into a higher-dimensional space where the relationships can be more easily identified. These methods are particularly well suited for capturing complex and non-linear relationships, but can be computationally expensive and may require large amounts of training data.

Resources:

Wikipedia:

References:

See also:

100 Best GitHub: Relation Extraction | 100 Best Relation Extraction Videos | Relation Extraction & Dialog Systems

Automatic relationship extraction from agricultural text for ontology construction
N Kaushik, N Chatterjee – Information processing in agriculture, 2018 – Elsevier
In the present era of Big Data the demand for developing efficient information processing techniques for different applications is expanding steadily. One such possible application is automatic creation of ontology. Such an ontology is often found to be helpful for answering …

Chemical-protein relation extraction with ensembles of SVM, CNN, and RNN models
Y Peng, A Rios, R Kavuluru, Z Lu – arXiv preprint arXiv:1802.01255, 2018 – arxiv.org
… Our CHEMPROT system obtained 0.7266 in precision and 0.5735 in recall for an f-score of 0.6410, demonstrating the effectiveness of machine learning-based approaches for automatic relation extraction from biomedical literature …

Automatic Relation Extraction for Building Smart City Ecosystems using Dependency Parsing.
D Braun, A Faber, A Hernandez-Mendez… – NL4AI@ AI …, 2018 – pdfs.semanticscholar.org
Understanding and analysing rapidly changing and growing business ecosystems, like smart city and mobility ecosystems, becomes increasingly difficult. However, the understanding of these ecosystems is the key to being successful for all involved parties, like …

Relation extraction of medical concepts using categorization and sentiment analysis
A Mondal, E Cambria, D Das, A Hussain… – Cognitive …, 2018 – Springer
… from the unstructured corpus. The relation assists in extracting similar meaning oriented links between concepts, which is considered as an automatic relation extraction system with visualization effects. To identify the semantic …

Indirect supervision for relation extraction using question-answer pairs
Z Wu, X Ren, FF Xu, J Li, J Han – … Conference on Web Search and Data …, 2018 – dl.acm.org
… 2xiangren@usc.edu 3frankxu@sjtu.edu.cn ABSTRACT Automatic relation extraction (RE) for types of interest is of great importance for interpreting massive text corpora in an efficient man- ner. For example, we want to identify …

Weakly-Supervised Relation Extraction in Legal Knowledge Bases
H Huang, RK Wong, B Du, HJ Han – International Conference on Asian …, 2019 – Springer
… relational data from natural language text. Apart from the traditional rule-based methods, supervised learning is the most popular method to enable automatic relation extraction [13, 19]. Although these methods can achieve …

Research on relation extraction of named entity on social media in smart cities
Z Liu, X Chen – Soft Computing, 2020 – Springer
Page 1. FOCUS Research on relation extraction of named entity on social media in smart cities Zuoguo Liu1 · Xiaorong Chen1 © Springer-Verlag GmbH Germany, part of Springer Nature 2020 Abstract Social media make significant contribution to the evolution of smart cities …

A hybrid model based on neural networks for biomedical relation extraction
Y Zhang, H Lin, Z Yang, J Wang, S Zhang, Y Sun… – Journal of biomedical …, 2018 – Elsevier
… Some computational methods have been successfully employed for automatic relation extraction from biomedical texts, including pattern-based methods [4], [5], [6], feature-based methods [7], [8], [9], [10] and kernel-based methods [11], [12], [13] …

Inter-subdomain relation extraction for agriculture domain
N Chatterjee, N Kaushik, B Bansal – IETE Technical Review, 2019 – Taylor & Francis
… 5. CONCLUDING REMARKS The work presented here proposes a knowledge-based scheme for automatic relation extraction. Accuracy and efficiency are the strengths of knowledge-based relation extraction methods [7] and same is true for the proposed scheme …

Biomedical event extraction using convolutional neural networks and dependency parsing
J Björne, T Salakoski – Proceedings of the BioNLP 2018 workshop, 2018 – aclweb.org
… Relations are usually described as typed, some- times directed, pairwise links between defined named entities. Automated relation extraction aims to develop computational methods for their detection. Event extraction is a proposed alternative for relation extraction …

A bag-of-concepts model improves relation extraction in a narrow knowledge domain with limited data
J Chen, K Verspoor, Z Zhai – arXiv preprint arXiv:1904.10743, 2019 – arxiv.org
… 1 Introduction Applying automatic relation extraction on small data sets in a narrow knowledge domain is chal- lenging. Here, we consider the specific context of a small clinical corpus, in which we have a va- riety of relation types of interest but limited ex- amples of each …

Relation extraction using distant supervision: A survey
A Smirnova, P Cudré-Mauroux – ACM Computing Surveys (CSUR), 2018 – dl.acm.org
… refinement. In this survey, we do not discuss unsupervised approaches because of those limitations. In recent years, an additional paradigm for automatic relation extraction was proposed, named distant supervision. Distant …

Grammar checking and relation extraction in text: approaches, techniques and open challenges
N Madi, R Al-Matham, H Al-Khalifa – Data Technologies and …, 2019 – emerald.com
… errors, writing error correction, grammar error detection, grammar error correction, grammar checking, grammar checking systems.” As for RE, the used terms were: “Relation extraction, relation detection, relation classification, automatic relation extraction, synonyms extraction …

Neural network based relation extraction of enterprises in credit risk management
C Yan, X Fu, W Wu, S Lu, J Wu – 2019 IEEE International …, 2019 – ieeexplore.ieee.org
… recurrent units (GRU) to extract the information of text data, takes the lexical features and syntactic features of the sentence as input, mines the relation between entities by means of dependency syntax information, and finally realizes the automatic relation extraction from the text …

Relation Extraction for Knowledge Base Completion: A Supervised Approach
H Cerezo-Costas, M Martín-Vicente – Semantic Web Evaluation Challenge, 2018 – Springer
… The aim of this challenge is interesting because automatic relation extraction could help to add new information to linked data services or create new ones. It is also the basis of many question-answering solutions [2] and machine comprehension tasks [21] …

Automatic detection of latent software component relationships from online Q&A sites
S Karthik, N Medvidovic – 2019 IEEE/ACM 7th International …, 2019 – ieeexplore.ieee.org
… Our work demonstrates that identifying such relations is valuable for the design of component-based systems and that automatic relation extraction is a promising technique to systematically harness such community knowledge …

Long distance entity relation extraction with article structure embedding and applied to mining medical knowledge
Y Lin, C Ma, D Gaoz, Z Fan, Z Cheng… – 2019 IEEE …, 2019 – ieeexplore.ieee.org
… magnitude. As a result, relation extraction has been given a lot of attention in the machine learning and natural language processing communities. Automatic relation extraction sometimes can be done simply by pattern matching. For …

Is automatic detection of hidden knowledge an anomaly?
J Preiss – BMC bioinformatics, 2019 – bmcbioinformatics.biomedcentral …
… These allow an investigation of a potential upper bound and the detection of limitations yielded by automatic relation extraction … These allow an investigation of a potential upper bound and the detection of limitations yielded by automatic relation extraction. Conclusion …

Semi-Automatic Corpus Expansion for Uyghur Named Entity Relation based on a Hybrid Method
K Abiderexiti, A Halike, M Maimaiti… – Belt & Road …, 2018 – pdfs.semanticscholar.org
… 7. Conclusion In this study, we described our work on expending Uyghur Named Entity Relation, the main purpose of this article is to provide the extension annotated corpus which is needed in the study of automatic relation extraction …

Domain Adapted Distant Supervision for Pedagogically Motivated Relation Extraction
O Sainz, OL de Lacalle, I Aldabe… – Proceedings of The 12th …, 2020 – aclweb.org
Page 1. Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 2213–2222 Marseille, 11–16 May 2020 c European Language Resources Association (ELRA), licensed under CC-BY-NC 2213 …

Automatic approach for constructing a knowledge graph of knee osteoarthritis in Chinese
X Li, H Liu, X Zhao, G Zhang, C Xing – Health Information Science and …, 2020 – Springer
… sampling is 91.7%. Summary and outlook. Research summary. In this study, a medical knowledge graph of knee osteoarthritis was established by combining manual labeling and automatic relationship extraction model. From the …

Utilizing soft constraints to enhance medical relation extraction from the history of present illness in electronic medical records
L Chen, Y Li, W Chen, X Liu, Z Yu, S Zhang – Journal of biomedical …, 2018 – Elsevier
Skip to main content Skip to article …

Automatic Identification of Relations in Quebec Heritage Data
F Ferry, A Zouaq, M Gagnon – Euro-Mediterranean Conference, 2018 – Springer
… languages. Our objective is to examine whether it is possible to use an approach based solely on word representations and supervised learning algorithms for automatic relation extraction. 3 Methodology. 3.1 Dataset Description. For …

Construct Semantic Type of “Gene-mutation-disease” Relation by Computer-aided Curation from Biomedical Literature
D Zhao, F Tong, Z Luo – 2019 – pdfs.semanticscholar.org
… focusing on the problem that current “gene-mutation-disease” semantic types lack fine- grained classification and corresponding relation signal words, we propose a text-mining-assisted semantic type construction approach for automatic relation extraction from biomedical …

Semantic relation extraction using sequential and tree-structured LSTM with attention
ZQ Geng, GF Chen, YM Han, G Lu, F Li – Information Sciences, 2020 – Elsevier
… distant supervision method. Lockard et al. [17] presented CERES (Ceres is the Roman goddess of the harvest) for automatic relation extraction from semistructured websites based on distant supervision. Qin et al. [23] presented …

A Graph Based Clustering Approach for Relation Extraction From Crime Data
P Das, AK Das, J Nayak, D Pelusi, W Ding – IEEE Access, 2019 – ieeexplore.ieee.org
… Basili et al. [17] introduced a system called ‘REVEAL’ that employed variants of support vector machine (SVM) for automatic relation extraction for crime investigation. Arulanandam et al. [18] extracted crime information from online newspapers …

Domain Analysis of Information Extraction Techniques
TM Alam, MJ Awan – Int J Multidiscip Sci Eng, 2018 – researchgate.net
… Entities are grouped according to their categorical information. With the goal of using social media networks for the Semantic Web, a few reviews have analyzed automatic relation extraction of social media. Junichiro Mori et al …

EoANN: Lexical Semantic Relation Classification Using an Ensemble of Artificial Neural Networks
RH Pour, M Shamsfard – Proceedings of the International Conference on …, 2019 – aclweb.org
… more attention. Manual construction and extension of lexical semantic relations for WordNets or knowledge graphs are very time consuming. Using automatic relation extraction methods can speedup this process. In this study …

APCNN: Tackling class imbalance in relation extraction through aggregated piecewise convolutional neural networks
A Smirnova, J Audiffren… – 2019 6th Swiss …, 2019 – ieeexplore.ieee.org
… not encountered by APCNN. II. RELATED WORK a) Distant Supervision: Distant supervision was origi- nally introduced by [1] to overcome the lack of training data in automatic relation extraction. Many improvements of the original …

Russianlanguage thesauri: automated construction and application for natural language processing tasks
NS Lagutina, KV Lagutina, AS Adrianov… – Modelirovanie i Analiz …, 2018 – mathnet.ru
… possible to determine directions for future study. Keywords: thesaurus, semantic relationships, automatic thesaurus construction, automatic relationship extraction, keyword extraction. DOI: https://doi.org/10.18255/1818-1015 …

Automatic extraction of access control policies from natural language documents
M Narouei, H Takabi, R Nielsen – IEEE Transactions on …, 2018 – ieeexplore.ieee.org
Page 1. 1545-5971 (c) 2018 IEEE. Personal use is permitted, but republication/ redistribution requires IEEE permission. See http://www.ieee.org/ publications_standards/publications/rights/index.html for more information. This …

Extraction of chemical–protein interactions from the literature using neural networks and narrow instance representation
R Antunes, S Matos – Database, 2019 – academic.oup.com
… appropriate treatments. Methods for improved retrieval and automatic relation extraction from biomedical literature are therefore required for collecting structured information from the growing number of published works. In this …

Reducing Feature Embedding Data for Discovering Relations in Big Text Data
H Huang, R Wong – 2019 IEEE International Congress on Big …, 2019 – ieeexplore.ieee.org
… unstructured text documents. Most works in automatic relation extraction have applied deep learning techniques such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) in large text corpora. However, they …

Automatic synonym and quasi-synonym extraction from user reviews on mobile devices
A Nugumanova, Y Baiburin, K Apayev… – ????????? …, 2019 – dbpia.co.kr
… IEEE, 2009. – Vol. 1. – P. 58-62. [14] Nugumanova A., Baiburin Y., Bessmertny I. Enhancement of binary text classification using automatic relation extraction //Communications in Computer and Information Science. – 2015. – Vol. 535. – Pp. 686-693.

Crime pattern analysis by identifying named entities and relation among entities
P Das, AK Das – Advanced computational and communication …, 2018 – Springer
… Initially, the project called ‘ASTREA’ [5] developed a relation extraction system for crime investigation. Basili et al. proposed a method called ‘REVEAL’ [6] that employs variants of support vector machine for automatic relation extraction in case of crime investigation. Yao et al …

Towards Automatic Generation of Peer-Targeted Science Talk in Curiosity-Evoking Virtual Agent
B Paranjape, Y Ge, Z Bai, J Hammer… – Proceedings of the 18th …, 2018 – dl.acm.org
… Some works have looked at automatic relation extraction. Automated biological hypothesis generation OpenCog extracts relations automatically from free text [16]. Most automatic relation extraction methods like these use dependency parsing to extract underlying relations …

The extraction of complex relationships and their conversion to biological expression language (BEL) overview of the BioCreative VI (2017) BEL track
S Madan, J Szostak, R Komandur Elayavilli… – Database, 2019 – academic.oup.com
Abstract. Knowledge of the molecular interactions of biological and chemical entities and their involvement in biological processes or clinical phenotypes is i.

Russian-Language Thesauri: Automatic Construction and Application for Natural Language Processing Tasks
NS Lagutina, KV Lagutina, AS Adrianov… – Automatic Control and …, 2019 – Springer
The paper overviews the existing digital Russian-language thesauri and the methods of their automatic construction and application. The authors have analyz.

Two Ways for the Automatic Generation of Application Ontologies by Using BalkaNet
M Mladenovi?, SV Stankovi?, V Paji? – International Journal on …, 2020 – igi-global.com
Page 1. DOI: 10.4018/IJSWIS.2020040102 International Journal on Semantic Web and Information Systems Volume 16 • Issue 2 • April-June 2020 Copyright©2020,IGIGlobal.Copyingordistributing inprintorelectronicformswithoutwrittenpermissionofIGIGlobalisprohibited. 18 …

Relation Extraction With Synthetic Explanations And Neural Network
R Chahardoli – 2019 – era.library.ualberta.ca
… on the results. Page 19. 7 Chapter 2 Background Building an accurate automated relation extraction system often requires developing or employing accurate tools for sentence parsing and feature extraction. The developed system …

A cascaded framework for identification and extraction of antonym for Turkish language
T Y?ld?z, S Y?ld?r?m – Soft Computing, 2019 – Springer
Identification and extraction of semantic relations are challenging tasks in Natural Language Processing. In this paper, we design and propose three different models for the two separate tasks of…

RICH-CPL: Fact Extraction from Wikipedia-sized Corpora for Morphologically Rich Languages
S Budkov, K Buraya, A Filchenkov… – … 23rd Conference of …, 2018 – ieeexplore.ieee.org
Page 1. RICH-CPL: Fact Extraction from Wikipedia-sized Corpora for Morphologically Rich Languages Sergei Budkov, Kseniya Buraya, Andrey Filchenkov, Ivan Smetannikov, Antonina Puchkovskaia ITMO University Saint-Petersburg …

Automatic text summarisation of case law using gate with annie and summa plug-ins
CT Aghaunor, GO Ekuobase – Nigerian Journal of Technology, 2019 – ajol.info
… Extraction (OBIE) system. OBIE supported automatic relation extraction; but its output lacks typographic structures (ie it is still an unstructured text) and is not amenable to machine comprehension. Ekuobase and Ebietomere …

Automatically extracting relations between clinical findingand treatment from clinical texts
Y Dou – 2019 – upcommons.upc.edu
Page 1. Automatically Extracting Relations between Clinical Finding and Treatment from Clinical Texts Yunxuan Dou Universitat Politecnica de Catalunya Facultat d’Informatica de Barcelona Director: Horacio Rodriguez Hontoria A thesis submitted for the degree of …

A statistical approach to knowledge discovery: Bootstrap analysis of language models for knowledge base population from unstructured text
S Momtazi, O Moradiannasab – Scientia Iranica, 2019 – scientiairanica.sharif.edu
… Previous research has shown that automatic relation extraction is feasible for question answering tasks with decent results [9]. Availability of a huge amount of unstructured texts on the web provides us with an opportunity to extract relations between named entities …

Construction of an ontology-based nursing knowledge system
SF Han, RF Zhu, J Xue, Q Yu, YB Su… – Frontiers of …, 2018 – content.sciendo.com
… Ontology learning is a process of semantic analysis and relationship extraction, and using automatic relationship-extraction techniques, it is possible to extract the relationships between concepts in teaching materials and establish triplet relationships, thereby forming …

Exploiting sequence labeling framework to extract document-level relations from biomedical texts
Z Li, Z Yang, Y Xiang, L Luo, Y Sun, H Lin – BMC bioinformatics, 2020 – Springer
Page 1. METHODOLOGY ARTICLE Open Access Exploiting sequence labeling framework to extract document-level relations from biomedical texts Zhiheng Li1, Zhihao Yang1*, Yang Xiang2, Ling Luo1, Yuanyuan Sun1 and Hongfei Lin1 * Correspondence: yangzh@dlut …

A framework for crime data analysis using relationship among named entities
P Das, AK Das, J Nayak, D Pelusi – Neural Computing and Applications, 2019 – Springer
… 123 Page 4. and Communication for Justice’ [21], and it helped by providing all the information resources. Basili et al. [5] designed ‘REVEAL’ that employed variants of support vector machine for automatic relation extraction in crime investigation. Das et al …

A self-attention based deep learning method for lesion attribute detection from CT reports
Y Peng, K Yan, V Sandfort… – 2019 IEEE …, 2019 – ieeexplore.ieee.org
… Our method obtained 0.848 in preci- sion and 0.788 in recall for an F-score of 0.815, demonstrating the effectiveness of machine learning-based approaches for automatic relation extraction from the clinical text in this task. II. RELATED WORK …

Deep learning of mutation-gene-drug relations from the literature
K Lee, B Kim, Y Choi, S Kim… – BMC …, 2018 – bmcbioinformatics.biomedcentral …
Molecular biomarkers that can predict drug efficacy in cancer patients are crucial components for the advancement of precision medicine. However, identifying these molecular biomarkers remains a laborious and challenging task. Next-generation sequencing of patients and preclinical …

A survey on supervised convolutional neural network and its major applications
DT Mane, UV Kulkarni – Deep Learning and Neural Networks …, 2020 – igi-global.com
… 2 Semantic Role Labeling for Automated Relation Extraction from Biomedical Texts (Barnickel, Weston, Collobert, Mewes, & Stumpflen, 2009) … Large scale application of neural network based semantic role labeling for automated relation extraction from biomedical texts …

Towards the Integration of Agricultural Data from Heterogeneous Sources: Perspectives for the French Agricultural Context Using Semantic Technologies
S Jiang, R Angarita, R Chiky, S Cormier… – International Conference …, 2020 – Springer
… prediction in agriculture. Agric. Syst. 70(2–3), 515–553 (2001)CrossRefGoogle Scholar. 8. Kaushik, N., Chatterjee, N.: Automatic relationship extraction from agricultural text for ontology construction. Inf. Process. Agric. 5(1), 60 …

OC-2-KB: integrating crowdsourcing into an obesity and cancer knowledge base curation system
JA Lossio-Ventura, W Hogan… – BMC medical …, 2018 – bmcmedinformdecismak …
There is strong scientific evidence linking obesity and overweight to the risk of various cancers and to cancer survivorship. Nevertheless, the existing online information about the relationship between obesity and cancer is poorly organized, not evidenced-based, of poor quality, and …

GOWDA: Goal-oriented Web Documents Querying tool
B Zarei, M Gaedke – Proceedings of the ACM Symposium on Document …, 2018 – dl.acm.org
… https://doi.org/10.1016/j.knosys.2014. 07.007 arXiv:1207.0246 [7] Neha Kaushik and Niladri Chatterjee. 2017. Automatic relationship extraction from agricultural text for ontology construction. Information Processing in Agri- culture 5, 1 (mar 2017), 60–73 …

Learning domain ontologies from engineering documents for manufacturing knowledge reuse by a biologically inspired approach
C Zhang, G Zhou, F Chang, X Yang – The International Journal of …, 2020 – Springer
The evolution of any given product family in manufacturing enterprises follows the epicycles in product lifecycle development, thus generating many useful.

Scenarios: a new representation for complex scene understanding
ZA Daniels, DN Metaxas – arXiv preprint arXiv:1802.06117, 2018 – arxiv.org
… systems that address fundamental scene understanding tasks such as scene classification, object de- tection, and semantic segmentation as well as more complex scene understanding tasks such as visual question-answering, automatic relationship extraction, scene graph …

Extracting chemical–protein relations with ensembles of SVM and deep learning models
Y Peng, A Rios, R Kavuluru, Z Lu – Database, 2018 – academic.oup.com
… Our CHEMPROT system obtained 0.7266 in precision and 0.5735 in recall for an F-score of 0.6410 during the challenge, demonstrating the effectiveness of machine learning-based approaches for automatic relation extraction from biomedical literature and achieving the …

Review and trend analysis of knowledge graphs for crop pest and diseases
L Xiaoxue, B Xuesong, W Longhe, R Bingyuan… – IEEE …, 2019 – ieeexplore.ieee.org
Page 1. Received March 29, 2019, accepted May 5, 2019, date of publication May 9, 2019, date of current version May 24, 2019. Digital Object Identifier 10.1109/ACCESS.2019.2915987 Review and Trend Analysis of Knowledge Graphs for Crop Pest and Diseases …

Ontology Based Reasoning Rules for Target Tracking in WSNs
J Zhang, Q Xiong – 2019 International Conference on Computer …, 2019 – atlantis-press.com
… New York: Academic, 1963, pp. 271-350. [3]. Kaushik N, Chatterjee N. “Automatic relationship extraction from agricultural text for ontology construction,” Information Processing in Agriculture, 2017, 5(1): 60-73. [4]. Maroua Masmoudi, Sana Ben Abdallah Ben Lamine. et al …

Transforming a Specialized Q&A System to a Chatbot System: A Case of a Simplified Taxation in Korea
J Jang, K Lee – International Conference on Human-Computer …, 2019 – Springer
… Q&A into conversational Q&A on special knowledge. In this method, we build an ontology of special knowledge using accumulated human knowledge and automated relation extraction. We use the traditional category system …

Re-curation and rational enrichment of knowledge graphs in Biological Expression Language
CT Hoyt, D Domingo-Fernández, R Aldisi, L Xu… – Database, 2019 – academic.oup.com
Abstract. The rapid accumulation of new biomedical literature not only causes curated knowledge graphs (KGs) to become outdated and incomplete, but also makes.

A survey of ontology learning techniques and applications
MN Asim, M Wasim, MUG Khan, W Mahmood… – Database, 2018 – academic.oup.com
Abstract. Ontologies have gained a lot of popularity and recognition in the semantic web because of their extensive use in Internet-based applications. Ontolog.

HyperFoods: Machine intelligent mapping of cancer-beating molecules in foods
K Veselkov, G Gonzalez, S Aljifri, D Galea… – Scientific reports, 2019 – nature.com
… 17 . Although of great promise, the automated relation extraction systems based on natural language processing (NLP) have thus far been tested on a very small subset (<200) of somewhat subjectively annotated abstracts …

Text-mining clinically relevant cancer biomarkers for curation into the CIViC database
J Lever, MR Jones, AM Danos, K Krysiak, M Bonakdar… – Genome Medicine, 2019 – Springer
… IE methods are also used for automated knowledgebase population without a manual curation step. For example, the miRTex knowledgebase, which collates microRNAs and their targets, uses automated relation extraction methods to populate the knowledgebase [24] …

Financial knowledge instantiation from semi-structured, heterogeneous data sources
F García-Sánchez, JA García-Díaz… – Computer Science On …, 2018 – Springer
… IOS Press, Amsterdam (2008)zbMATHGoogle Scholar. 20. Kaushik, N., Chatterjee, N.: Automatic relationship extraction from agricultural text for ontology construction. Inf. Process. Agric. (2017). https://doi.org/10.1016/j.inpa.2017.11.003CrossRefGoogle Scholar. 21 …

Role Term-Based Semantic Similarity Technique for Idea Plagiarism Detection
AH Osman, HM Aljahdali – … JOURNAL OF ADVANCED …, 2018 – pdfs.semanticscholar.org
Page 1. (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 9, No. 8, 2018 475 | P age www.ijacsa.thesai.org Role Term-BasedSemantic Similarity Technique for Idea Plagiarism Detection Ahmed …

Generating Knowledge Graphs from Scientific Literature of Degenerative Diseases
A Rossanez, JC dos Reis – 2019 – pdfs.semanticscholar.org
… 1. pp. 86–90. Ass. for Computational Linguistics, Stroudsburg, PA, USA (1998) 4. Barnickel, T., Weston, J., Collobert, R., Mewes, HW, Stümpflen, V.: Large scale application of neural network based semantic role labeling for automated relation extraction from biomedical texts …

Abstractive text summarization based on improved semantic graph approach
A Khan, N Salim, H Farman, M Khan, B Jan… – International Journal of …, 2018 – Springer
The goal of abstractive summarization of multi-documents is to automatically produce a condensed version of the document text and maintain the significant information. Most of the graph-based…

Scenarionet: An interpretable data-driven model for scene understanding
ZA Daniels, D Metaxas – … on Explainable Artificial Intelligence (XAI) 2018, 2018 – par.nsf.gov
… systems that address fundamental scene understanding tasks such as scene classification, object de- tection, and semantic segmentation as well as more complex scene understanding tasks such as visual question-answering, automatic relationship extraction, scene graph …

Financial Knowledge Instantiation from Semi-structured, Heterogeneous Data Sources
JM Gómez-Berbís… – Artificial Intelligence and …, 2018 – books.google.com
… and Knowledge. IOS Press, Amsterdam (2008) Kaushik, N., Chatterjee, N.: Automatic relationship extraction from agricultural text for ontology construction. Inf. Process. Agric.(2017). https://doi. org/10.1016/j. inpa. 2017.11. 003 …

Drug knowledge bases and their applications in biomedical informatics research
Y Zhu, O Elemento, J Pathak… – Briefings in bioinformatics, 2019 – academic.oup.com
Abstract. Recent advances in biomedical research have generated a large volume of drug-related data. To effectively handle this flood of data, many initiatives.

Effect of Aerobic Exercise on Respiratory Diseases in Haze Environment
J Yang, H Wang – Ekoloji, 2019 – ekolojidergisi.com
… sugars. Information Processing in Agriculture 4 (1):83-89. Kaushik N, Chatterjee N (2018) Automatic relationship extraction from agricultural text for ontology construction. Information Processing in Agriculture 5 (1):60-73. Marcela …

A survey of semantic web technology for agriculture
B Drury, R Fernandes, MF Moura… – Information Processing …, 2019 – Elsevier
JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Skip to main content Skip to article …

Using Deep Learning Based Natural Language Processing Techniques for Clinical Decision-Making with EHRs
R Zhu, X Tu, J Huang – Deep Learning Techniques for Biomedical and …, 2020 – Springer
… Similar to Li et al.’s work, Quan et al. [54] presented a multichannel CNN to exercise automatic relation extraction in medical domain in 2016 to tackle drug-drug interaction (DDI) extraction and protein-to-protein interaction (PPI) extraction problems …

Generation and Applications of Knowledge Graphs in Systems and Networks Biology
CT Hoyt – 2019 – d-nb.info
… topic. Therefore, researchers need the assistance of automated relation extraction systems to assist in the enrichment of previously existing knowledge available and integrated from relevant, high-quality databases. 15 Page 33 …

A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text
RM Ghoniem, N Alhelwa, K Shaalan – Algorithms, 2019 – mdpi.com
Ontologies are used to model knowledge in several domains of interest, such as the biomedical domain. Conceptualization is the basic task for ontology building. Concepts are identified, and then they are linked through their semantic relationships. Recently, ontologies have constituted …

A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19
Y Ge, T Tian, S Huang, F Wan, J Li, S Li, H Yang… – bioRxiv, 2020 – biorxiv.org
Page 1. A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19 Yiyue Ge1,2,†, Tingzhong Tian1,2,†, Suling Huang3,†, Fangping Wan1,†, Jingxin Li2,†, Shuya Li1, Hui Yang11 …

Application of Text-Analytics in Quantitative Study of Science and Technology
S Ranaei, A Suominen, A Porter, T Kässi – Springer Handbook of Science …, 2019 – Springer
The quantitative study of science, technology and innovation (ST&I ) has experienced significant growth with advancements in disciplines such as mathematics, computer science and information…

Artist migration through the biographer’s lens: A case study based on biographical data retrieved from the Austrian Biographical Dictionary
M Kaiser, K Lejtovicz, PA Rumpolt… – Journal of Historical …, 2018 – hcommons.org
Page 1. KAISER, MAXIMILIAN; SCHLÖGL, MATTHIAS; LEJTOVICZ, KATALIN AND RUMPOLT, PETER ALEXANDER Artist Migration Through the Bio- grapher’s Lens: A Case Study Based on Biographical Data Retrieved from the Austrian Biographical Dictionary …

Text Completion using a Context-Integrating Dependency Parser
AR Salama, O Alaçam, W Menzel – ACL 2018, 2018 – aclweb.org
… V1. 0 For the current study, the pictures, as the one given in Figure 1, serve illustrative purposes, be- cause the computational model does only have access to the manually annotated representations. An automatic relation extraction is not within the scope of this study …

Text Completion using Context-Integrated Dependency Parsing
AR Salama, Ö Alaçam, W Menzel – Proceedings of The Third Workshop …, 2018 – aclweb.org
… An automatic relation extraction is not within the scope of this study. The different semantic roles are distributed in the data set as follows; Agent (%13.6), Theme (%13.6), Location (%33.1), Next to (%9.8), Prop- erty (%19.5), Own (%10.3) …

Cross-domain Ontology Construction and Alignment from Online Customer Product Reviews
Q Geng, S Deng, D Jia, J Jin – Information Sciences, 2020 – Elsevier
JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Skip to main content Skip to article …

Security-Pattern Recognition and Validation
M Bunke – 2019 – elib.suub.uni-bremen.de
Page 1. Security-Pattern Recognition and Validation Dissertation Submitted by Michaela Bunke on 12th December 2018 to the Universität Bremen Faculty of Mathematics and Computer Science in partial fulfillment of the requirements for the degree of …

Ecological Evolution Characteristics of New Chinese Landscape from the Perspective of Rapid Urbanization.
T Zhang – Ekoloji Dergisi, 2019 – ekolojidergisi.com
… southern kazakhstan. Ekoloji 26 (100):1-10. Kaushik N, Chatterjee N (2018) Automatic relationship extraction from agricultural text for ontology construction. Information Processing in Agriculture 5 (1):60-73. Mroczek-Krzyzelewska …

A Distance Measure of Interval-valued Belief Structures
J Cao, X Zhang, J Feng – Sains Malaysiana, 2019 – ukm.my
Page 1. Sains Malaysiana 48(12)(2019): 2787–2796 http://dx.doi.org/10.17576/jsm-2019-4812- 20 A Distance Measure of Interval-valued Belief Structures (Suatu Jarak Pengukuran Nilai Selang Struktur Kepercayaan) JUNQIN CAO, XUEYING ZHANG* & JIAPENG FENG …

Modeling of negative protein-protein interactions: methods and experiments
A Moscatelli – arXiv preprint arXiv:1910.04709, 2019 – arxiv.org
Page 1. Modeling of negative protein-protein interactions: methods and experiments Facoltà di Ingegneria dell’Informazione, Informatica e Statistica Corso di Laurea Magistrale in Computer Science Candidate Andrea Moscatelli ID number 1667647 Thesis Advisor Prof …