Semantic grounding of social annotations for enhancing resource classification in folksonomies

Tommasel, Antonela; Godoy, Daniela

doi:10.1007/s10844-014-0339-y

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…Seifollahi and Shajari (2019) investigated sentiment entities of news headlines. Tommasel and Godoy (2015) investigated social annotations in Flickr and Delicious. Hunter (2001) indexed documents using entities and keywords for information retrieval of disambiguating words via database queries.…”

Section: Theory and Calculationmentioning

confidence: 99%

Longitudinal Study of a Website for Assessing American Presidential Candidates and Decision Making of Potential Election Irregularities Detection

Piper

Rodger

2022

International Journal on Semantic Web and Information Systems

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We employ the concept of word sense disambiguation to determine the inherent meaning of voter intentions regarding possible political candidates from the 2016 Presidential election. We present our findings based on a website (www.presidentselect.com) that we developed, where candidates can be examined and their true assets and competencies in three major areas of eligibility, education, and experience inputs can be deciphered. Data envelope analysis is used to determine underlying word instances for elected and successful outputs. We also utilize our web site results to longitudinally extend these findings for decision making of potential election fraud detection in the 2020 Presidential election, utilizing Benford’s Law. Our results shed light on these phenomenon and provide new insights into the word sense disambiguation literature.

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Section: Theory and Calculationmentioning

confidence: 99%

Longitudinal Study of a Website for Assessing American Presidential Candidates and Decision Making of Potential Election Irregularities Detection

Piper

Rodger

2022

International Journal on Semantic Web and Information Systems

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“…Much work has been done to introduce semantics in folksonomy [16][17][18][19], and to investigate methods of deploying this semantics for tasks such as information retrieval [20][21][22], recommender systems [23][24][25][26], and ontologies development [27][28][29]. As well, quite a number of works has been done to extract structured knowledge and develop ontologies from social tagging systems.…”

Section: Related Workmentioning

confidence: 99%

Enabling social WEB for IoT inducing ontologies from social tagging

Alruqimi¹,

Aknin²

2019

IJ-ICT

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<span>Semantic domain ontologies are increasingly seen as the key for enabling interoperability across heterogeneous systems and sensor-based applications. The ontologies deployed in these systems and applications are developed by restricted groups of domain experts and not by semantic web experts. Lately, folksonomies are increasingly exploited in developing ontologies. The “collective intelligence”, which emerge from collaborative tagging can be seen as an alternative for the current effort at semantic web ontologies. However, the uncontrolled nature of social tagging systems leads to many kinds of noisy annotations, such as misspellings, imprecision and ambiguity. Thus, the construction of formal ontologies from social tagging data remains a real challenge. Most of researches have focused on how to discover relatedness between tags rather than producing ontologies, much less domain ontologies. This paper proposed an algorithm that utilises tags in social tagging systems to automatically generate up-to-date specific-domain ontologies. The evaluation of the algorithm, using a dataset extracted from BibSonomy, demonstrated that the algorithm could effectively learn a domain terminology, and identify more meaningful semantic information for the domain terminology. Furthermore, the proposed algorithm introduced a simple and effective method for disambiguating tags.</span><span style="font-size: 9pt; font-family: 'Times New Roman', serif;">Semantic domain ontologies are increasingly seen as the key for enabling interoperability across heterogeneous systems and sensor-based applications. The ontologies deployed in these systems and applications are developed by restricted groups of domain experts and not by semantic web experts. Lately, folksonomies are increasingly exploited in developing ontologies. The “collective intelligence”, which emerge from collaborative tagging can be seen as an alternative for the current effort at semantic web ontologies. However, the uncontrolled nature of social tagging systems leads to many kinds of noisy annotations, such as misspellings, imprecision and ambiguity. Thus, the construction of formal ontologies from social tagging data remains a real challenge. Most of researches have focused on how to discover relatedness between tags rather than producing ontologies, much less domain ontologies. This paper proposed an algorithm that utilises tags in social tagging systems to automatically generate up-to-date specific-domain ontologies. The evaluation of the algorithm, using a dataset extracted from BibSonomy, demonstrated that the algorithm could effectively learn a domain terminology, and identify more meaningful semantic information for the domain terminology. Furthermore, the proposed algorithm introduced a simple and effective method for disambiguating tags.</span>

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The role of collaborative tagging and ontologies in emerging semantic of web resources

Qassimi

Abdelwahed

2019

Computing

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Semantic grounding of social annotations for enhancing resource classification in folksonomies

Cited by 7 publications

References 29 publications

Longitudinal Study of a Website for Assessing American Presidential Candidates and Decision Making of Potential Election Irregularities Detection

Longitudinal Study of a Website for Assessing American Presidential Candidates and Decision Making of Potential Election Irregularities Detection

Enabling social WEB for IoT inducing ontologies from social tagging

The role of collaborative tagging and ontologies in emerging semantic of web resources

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