PaloPro: a platform for knowledge extraction from big social data and the news

Makrynioti, Nantia; Grivas, Andreas; Sardianos, Christos; Tsirakis, Nikos; Varlamis, Iraklis; Vassalos, Vasilis; Poulopoulos, Vassilis; Tsantilas, Panagiotis

doi:10.1504/ijbdi.2017.081185

Cited by 7 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…There is an abundance of proposed strategies for transforming raw text to a structured representation in order to populate a knowledge graph [1][2][3][4]. However, especially in the case of OIE approaches and due to concerns on scaling, the use of syntactic or semantic relation extraction techniques has been relatively sparse, with the exception of a few recent examples aiming at domain-specific knowledge extraction [5][6][7][8][9]. Most domain-specific information extraction approaches are focused primarily on evaluating the efficiency of different triple extraction tools on raw data, not taking useful pre-processing and post-processing strategies into account, thus resulting in a large number of potentially uninformative triples [10][11][12].…”

Section: Related Workmentioning

confidence: 99%

“…Some coronaviruses are associated with gastrointestinal disease in children. Sporadic infections of the central nervous system have also been reported, although the role of coronaviruses in infections outside the respiratory tract has not been completely clarified (8). Most coronaviruses are adapted to their hosts, whether animal or human, although cases of possible animal-to-hu-man transmission and adaptation have been described in the past two decades, causing two epidemics.…”

Section: Cord-19 Article Id: D99dbae98cc9705d9b5674bb6eb66560b4434305mentioning

confidence: 99%

“…The cause was shown to be a novel coronavirus (SARS-CoV), an animal virus that had crossed the species barrier and infected humans. The most likely reservoir was bats, with evidence that the virus was transmitted to a human through an intermediate host, probably a palm civet or raccoon dog (8,9). In less than a year, SARS-CoV infected 8098 people in 26 countries, of whom 774 died (10,11).…”

Section: Cord-19 Article Id: D99dbae98cc9705d9b5674bb6eb66560b4434305mentioning

confidence: 99%

See 2 more Smart Citations

A Methodology for Open Information Extraction and Representation from Large Scientific Corpora: The CORD-19 Data Exploration Use Case

2020

View full text Add to dashboard Cite

The usefulness of automated information extraction tools in generating structured knowledge from unstructured and semi-structured machine-readable documents is limited by challenges related to the variety and intricacy of the targeted entities, the complex linguistic features of heterogeneous corpora, and the computational availability for readily scaling to large amounts of text. In this paper, we argue that the redundancy and ambiguity of subject–predicate–object (SPO) triples in open information extraction systems has to be treated as an equally important step in order to ensure the quality and preciseness of generated triples. To this end, we propose a pipeline approach for information extraction from large corpora, encompassing a series of natural language processing tasks. Our methodology consists of four steps: i. in-place coreference resolution, ii. extractive text summarization, iii. parallel triple extraction, and iv. entity enrichment and graph representation. We manifest our methodology on a large medical dataset (CORD-19), relying on state-of-the-art tools to fulfil the aforementioned steps and extract triples that are subsequently mapped to a comprehensive ontology of biomedical concepts. We evaluate the effectiveness of our information extraction method by comparing it in terms of precision, recall, and F1-score with state-of-the-art OIE engines and demonstrate its capabilities on a set of data exploration tasks.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Cord-19 Article Id: D99dbae98cc9705d9b5674bb6eb66560b4434305mentioning

confidence: 99%

Section: Cord-19 Article Id: D99dbae98cc9705d9b5674bb6eb66560b4434305mentioning

confidence: 99%

See 1 more Smart Citation

A Methodology for Open Information Extraction and Representation from Large Scientific Corpora: The CORD-19 Data Exploration Use Case

2020

View full text Add to dashboard Cite

show abstract

“…Tables 1 and 2 identify some studies that use data analysis techniques and which data types are used. [4], [15], [16] Natural Language Processing text [4] HTML Trees (DOM) text [5], [6] Link Prediction Collaboration Networks; Social Networks; Infrastructure Networks; Sports Networks; Biology Networks [7] Probabilistic Three-way Entity Model (TEM) text [8] Entity Linking Entities (text) [9], [10], [11] Naive Bayes Classifier Text and numeric values [12] Multilayer Perceptron, (MLP) Time series [13] Entity Recognition text [13], [14] Sentiment Analysis text [4], [15], [16] Natural Language Processing text [17], [18] Topic Modeling Probabilistic text [19] Latent Semantic Visualization Time-stamps [20] First Story Detection text [21] Event Extraction Entities (text) [22] Data Discovery text [13] Entity Recognition text [13], [14] Sentiment Analysis text [23] Image Recognition image…”

Section: Related Workmentioning

confidence: 99%

Exploring Data Analytics of Data Variety

Cruz

Sá

Pereira

2018

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

The Internet allows organizations managers access to large amounts of data, and this data are presented in different formats, i.e., data variety, namely structured, semi-structured and unstructured. Based on the Internet, this data variety is partly derived from social networks, but not only, machines are also capable of sharing information among themselves, or even machines with people. The objective of this paper is to understand how to retrieve information from data analysis with data variety. An experiment was carried out, based on a dataset with two distinct data types, images and comments on cars. Techniques of data analysis were used, namely Natural Language Processing to identify patterns, and Sentimental and Emotional Analysis. The image recognition technique was used to associate a car model with a category. Next, OLAP cubes and their visualization through dashboards were created. This paper concludes that it is possible to extract a set of relevant information, namely identifying which cars people like more/less, among other information.

show abstract

“…O universo digital apresenta uma enorme diversidade de dados gerados através das redes sociais, onde os utilizadores geram conteúdos diversificados como imagens, vídeos, textos, sites, entre outros (Fan e Gordon, 2014), mas não só, também a Internet of Things (Uckelmann et al, 2011), tornou possível que máquinas pudessem comunicar entre si automaticamente, utilizando sistemas de endereçamento exclusivos, e desta forma, consigam trabalhar em conjunto para atingir um fim comum. Esta diversidade de dados é acompanhada por uma variedade de tipo dados, estruturados, semiestruturados e não estruturados, tanto gerados por HTML trees (DOM) (Li et al2016), (Zhou, Liu, & Buttler, 2015) Link prediction (Bi, et al, 2015) probabilistic Three-way Entity Model (TEM) (Blanco, Ottaviano, & Meij, 2015) Entity linking (Caballero Barajas & Akella, 2015) Naive Bayes classifier (Oliveira, Barbar, & Soares, 2016) multilayer perceptron, (MLP) (Makrynioti, et al, 2017) entity recognition (Makrynioti, et al, 2017) Association Rule Learning (Makrynioti, et al, 2017) entity recognition (Makrynioti, et al, 2017) sentiment analysis A técnica de análise de textos através do processamento de linguagem natural possibilita processar textos não estruturados e obter um conjunto de descritores, como entidades, conceitos gerais, palavras-chave, categorias, relações e análises de sentimento.…”

Section: Descrição Do Trabalho (Póster)unclassified

Data Analytics Para Variedade De Dados

Cruz

Sá

2017

Atas Da 17ª Conferência Da Associação Portuguesa De Sistemas De Informação

View full text Add to dashboard Cite

ResumoA internet fez com que os gestores das organizações tivessem acesso a grandes quantidades de dados e esses dados são apresentados em diferentes formatos, em concreto, estruturados, semiestruturados e não estruturados. Esta variedade de dados é essencialmente proveniente das redes sociais, mas não só, também são provenientes da Internet of Things. Verifica-se para os dados estruturados que existem técnicas validadas, estudadas e maduras, mas para os outros tipos de dados, ou seja, semiestruturados e não estruturados tal já não se verifica. Neste poster, é apresentado um conjunto de técnicas de análise de dados para os dados semiestruturados e não estruturados, utilizando como principal bibliografia conferências de investigação na área de análise de dados. Palavras-chave: Análise de dados, Variedade de dados, Tipo de dados AbstractThrough the Internet, the organizations managers had access to massive amounts of data and these data are presented in different formats, namely, structured, semi-structured and unstructured. These variety of data is essentially generated from social networks, but not only, they also are generated from the Internet of Things, from machines, sensors, among others. While the structured data has techniques well studied, mature and validated, otherwise the other types of techniques, semi-structured and unstructured, this is no longer true. In this poster, a set of data analysis techniques is presented for the semi-structured and unstructured data by using as main bibliography data analytics conferences.

show abstract

PaloPro: a platform for knowledge extraction from big social data and the news

Cited by 7 publications

References 0 publications

A Methodology for Open Information Extraction and Representation from Large Scientific Corpora: The CORD-19 Data Exploration Use Case

A Methodology for Open Information Extraction and Representation from Large Scientific Corpora: The CORD-19 Data Exploration Use Case

Exploring Data Analytics of Data Variety

Data Analytics Para Variedade De Dados

Contact Info

Product

Resources

About