Improving the classification of events in tweets using semantic enrichment

Romero, Simone Aparecida Pinto; Becker, Karin

doi:10.1145/3106426.3106435

Cited by 8 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additional information about features (properties and relations) can be used as context to improve the generalisation of a specific dataset [47]. However, we must be aware of the noise that may result from the inclusion of uninformative features [71]. Another method is to extract patterns from the graph, e.g., to capture spurious model correlations that are based on sensitive information [61], or properties that enable mining less popular items in a RS [65].…”

Section: Opportunitiesmentioning

confidence: 99%

Semantic Web technologies and bias in artificial intelligence: A systematic literature review

Lobo

Daga

Alani

et al. 2023

View full text Add to dashboard Cite

Bias in Artificial Intelligence (AI) is a critical and timely issue due to its sociological, economic and legal impact, as decisions made by biased algorithms could lead to unfair treatment of specific individuals or groups. Multiple surveys have emerged to provide a multidisciplinary view of bias or to review bias in specific areas such as social sciences, business research, criminal justice, or data mining. Given the ability of Semantic Web (SW) technologies to support multiple AI systems, we review the extent to which semantics can be a “tool” to address bias in different algorithmic scenarios. We provide an in-depth categorisation and analysis of bias assessment, representation, and mitigation approaches that use SW technologies. We discuss their potential in dealing with issues such as representing disparities of specific demographics or reducing data drifts, sparsity, and missing values. We find research works on AI bias that apply semantics mainly in information retrieval, recommendation and natural language processing applications and argue through multiple use cases that semantics can help deal with technical, sociological, and psychological challenges.

show abstract

Section: Opportunitiesmentioning

confidence: 99%

Semantic Web technologies and bias in artificial intelligence: A systematic literature review

Lobo

Daga

Alani

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The method uses a combination of geometric and linguistic techniques to classify the tags and then select those that would most likely semantically enrich feature descriptions. In order to improve the classification of events in tweets, Romero and Becker [138] use semantic enrichment to identify entities and relevant vocabulary from tweets and related web pages and associate these features with concepts extracted from the LoD cloud. A pruning technique is then applied in order to discard too generic or too specific semantic features and select those with the most discriminative power for event classification.…”

Section: Geospatial Semantic Information Extraction and Enrichmentmentioning

confidence: 99%

A Review of Geospatial Semantic Information Modeling and Elicitation Approaches

Kokla

Guilbert

2020

IJGI

View full text Add to dashboard Cite

The present paper provides a review of two research topics that are central to geospatial semantics: information modeling and elicitation. The first topic deals with the development of ontologies at different levels of generality and formality, tailored to various needs and uses. The second topic involves a set of processes that aim to draw out latent knowledge from unstructured or semi-structured content: semantic-based extraction, enrichment, search, and analysis. These processes focus on eliciting a structured representation of information in various forms such as: semantic metadata, links to ontology concepts, a collection of topics, etc. The paper reviews the progress made over the last five years in these two very active areas of research. It discusses the problems and the challenges faced, highlights the types of semantic information formalized and extracted, as well as the methodologies and tools used, and identifies directions for future research.

show abstract

“…DBpedia Spotlight g serves purpose to find mapping between domain ontology and DBpedia classes. Romero and Becker (2017) describe a classification framework, taking advantage of DBpedia for enriching semantic features. DBpedia spotlight connects terms to their respective URI for semantic enrichment.…”

Section: Literature Surveymentioning

confidence: 99%

A Hierarchical Clustering Approach for DBpedia based Contextual Information of Tweets

Maravanthe¹,

Rao²,

Kanavalli³

et al. 2020

Journal of Computer Science

View full text Add to dashboard Cite

The past decade has seen a tremendous increase in the adoption of Social Web leading to the generation of enormous amount of user data every day. The constant stream of tweets with an innate complex sentimental and contextual nature makes searching for relevant information a herculean task. Multiple applications use Twitter for various domain sensitive and analytical use-cases. This paper proposes a scalable context modeling framework for a set of tweets for finding two forms of metadata termed as primary and extended contexts. Further, our work presents a hierarchical clustering approach to find hidden patterns by using generated primary and extended contexts. Ontologies from DBpedia are used for generating primary contexts and subsequently to find relevant extended contexts. DBpedia Spotlight in conjunction with DBpedia Ontology forms the backbone for this proposed model. We consider both twitter trend and stream data to demonstrate the application of these contextual parts of information appropriate in clustering. We also discuss the advantages of using hierarchical clustering and information obtained from cutting dendrograms.

show abstract

Improving the classification of events in tweets using semantic enrichment

Cited by 8 publications

References 12 publications

Semantic Web technologies and bias in artificial intelligence: A systematic literature review

Semantic Web technologies and bias in artificial intelligence: A systematic literature review

A Review of Geospatial Semantic Information Modeling and Elicitation Approaches

A Hierarchical Clustering Approach for DBpedia based Contextual Information of Tweets

Contact Info

Product

Resources

About