Web Services Ontology Population through Text Classification

Reyes‐Ortiz, José A.; Bravo, Maricela; Pablo, Hugo

doi:10.15439/2016f332

Cited by 9 publications

(5 citation statements)

References 18 publications

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“…Data cleaning is performed and each word in a sentence is converted into a vector form to be modelled by assigning a Term frequency -inverse document frequency (Tf-IDF) score [9].…”

Section: Ic Preprocessing News Headlines For Sentiment Analysismentioning

confidence: 99%

Analysis of the Effect of News Sentiment on Stock Market Prices through Event Embedding

Sridhar¹,

Sanagavarapu²

2021

Annals of Computer Science and Information Systems

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Stock market price prediction models have remained a prominent challenge for the investors owing to their volatile nature. The impact of macroeconomic events such as news headlines is studied here using a standard dataset with closing stock price rates for a chosen period by performing sentiment analysis using a Random Forest classifier. A Bi-LSTM time-series forecasting model is constructed to predict the stock prices by using the polarity of the news headlines. It is observed that Random Forest Classifiers predict the polarity of news articles with an accuracy of 84.92%.

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“…Data cleaning is performed and each word in a sentence is converted into a vector form to be modelled by assigning a Term frequency -inverse document frequency (Tf-IDF) score [9].…”

Section: Ic Preprocessing News Headlines For Sentiment Analysismentioning

confidence: 99%

Analysis of the Effect of News Sentiment on Stock Market Prices through Event Embedding

Sridhar¹,

Sanagavarapu²

2021

Annals of Computer Science and Information Systems

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“…A recent approach for Web services ontology population from text has been exposed by Reyes-Ortiz et al [22], oriented towards Web services classification rather than capacities description. The goal of [22] is to obtain an ontology that classifies Web services following their topic (application domain). Features (meaningful terms in the WS descriptions) are extracted using term frequency (TF) and inverse document frequency (IDF) measures.…”

Section: A Ontology Construction Techniquesmentioning

confidence: 99%

Investigating a Method for Automatic Construction and Population of Ontologies for Services: Performances and Limitations

Louge

Karray

Archimède

2018

2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA)

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Ontological engineering is a complex process, involving multidisciplinary skills. The Semantic Web, and more specifically Semantic Web Services spreading suffer from the difficulty of producing an ontology sufficiently detailed to be able to correctly describe the data flows exchanged between services. These data are often described using sector-specific vocabulary. Linking these descriptions to external knowledge sources capable of unifying them is often a complex process, requiring adequate sources to be found and properly used. In this paper, we investigate a method combining existing string distance measurement, NLPanalysis and clustering algorithms for automatic construction and population of an ontology. This method takes services capacities descriptions as only input, without external sources of knowledge. It is tested on a set of more than 10,000 services for 106,000 different measures to classify in an ontology, performances and limitations are exposed.

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“…Ontology population has already been deployed in various domains, like e.g. etourism [12], web services [13] and clinical data [14], amongst others. Another recent work deploys ontology population in a Big Data setting [15], indicating a potentially emerging interest in the area.…”

Section: Related Workmentioning

confidence: 99%

“…Bob then registers the sources that serve the desired data. Two suitable candidates are ENVO 12 and LinkedGeoData 13 . Specifically, ENVO's class City (ENVO_00000856) and LinkedGeoData's classes City and Town contain relevant instances.…”

Section: Use Case 2: Ontology Population In a Data-intensive Domainmentioning

confidence: 99%

PROPheT – Ontology Population and Semantic Enrichment from Linked Data Sources

Riga

Mitzias

Kontopoulos

et al. 2018

Communications in Computer and Information Science

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Ontologies are a rapidly emerging paradigm for knowledge representation, with a growing number of applications in various domains. However, populating ontologies with massive volumes of data is an extremely challenging task. The field of ontology population offers a wide array of approaches for populating ontologies in an automated or semi-automated way. Nevertheless, most of the related tools typically analyse natural language text, while sources of more structured information like Linked Open Data would arguably be more appropriate. The paper presents PROPheT, a novel software tool for ontology population and enrichment. PROPheT can populate a local ontology model with instances retrieved from diverse Linked Data sources served by SPARQL endpoints. To the best of our knowledge, no existing tool can offer PROPheT's diverse extent of functionality.

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Web Services Ontology Population through Text Classification

Cited by 9 publications

References 18 publications

Analysis of the Effect of News Sentiment on Stock Market Prices through Event Embedding

Analysis of the Effect of News Sentiment on Stock Market Prices through Event Embedding

Investigating a Method for Automatic Construction and Population of Ontologies for Services: Performances and Limitations

PROPheT – Ontology Population and Semantic Enrichment from Linked Data Sources

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