Semantic annotation tools survey

Oliveira, Pedro; Rocha, João

doi:10.1109/cidm.2013.6597251

Cited by 15 publications

(22 citation statements)

References 18 publications

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“…This intervention often takes the form of choosing the best option from a list of candidate annotations, or removing some of the proposed annotations that the user considers incorrect or irrelevant. While considerable research and development efforts were put into the design and development of semi-automated annotation tools (Uren et al, 2005), (Oliveira & Rocha, 2013), their reliance on human active participation impacts their efficiency, and thus they have been largely superseded by fully automated tools. Still, there are some usage scenarios, e.g., scholarly reading, where, due to their mixed-initiative annotation approach, semi-automated annotators are preferred.…”

Section: Defaultmentioning

confidence: 99%

Evolutionary fine-tuning of automated semantic annotation systems

Cuzzola¹,

Jovanović²,

Bagheri³

et al. 2015

Expert Systems with Applications

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a b s t r a c tConsidering the ever-increasing speed at which new textual content is generated, an efficient and effective use of large text corpora requires automated natural language processing and text analysis tools. A subset of such tools, namely automated semantic annotation tools, are capable of interlinking syntactical forms of text with their underlying semantic concepts. The optimal performance of automated semantic annotation tools often depends on tuning the values of the tools' adjustable parameters to the specificities of the annotation task, and particularly to the characteristics of the text to be annotated. Such characteristics include the text domain, terseness or verbosity level, text length, structure and style. Since the default configuration of annotation tools is not suitable for the large variety of input texts that different combinations of these attributes can produce, users often need to adjust the annotators' tunable parameters in order to get the best results. However, the configuration of semantic annotators is presently a tedious and time consuming task as it is primarily based on a manual trial-and-error process. In this paper, we propose a Parameter Tuning Architecture (PTA) for automating the task of configuring parameter values of semantic annotation tools. We describe the core fitness functions of PTA that operate on the quality of the annotations produced, and offer a solution, based on a genetic algorithm, for searching the space of possible parameter values. Our experiments demonstrate that PTA enables effective configuration of parameter values of many semantic annotation tools.

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Section: Defaultmentioning

confidence: 99%

Evolutionary fine-tuning of automated semantic annotation systems

Cuzzola¹,

Jovanović²,

Bagheri³

et al. 2015

Expert Systems with Applications

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“…Existem diversas técnicas para produzir anotação semântica (Oliveira & Rocha, 2013;Slimani, 2013). Para anotação manual, é possível produzir anotações com a ajuda de editores, profissionais que são contratados para indexar informações aos conteúdos que possuem pouco ou nenhum texto prévio associado, como é o caso dos editores da BBC que são citados por Raimond e Lowis (2012).…”

Section: Anotação Semânticaunclassified

Uma Abordagem para Identificação de Similaridade Entre Recursos Educacionais Utilizando Bases de Conhecimento Externas

Dias

Barbosa

Barrére

et al. 2017

RBIE

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“…Semantic annotation have various definitions from various authors in various papers but then they all similar with one clear purpose. To define this, from [1], semantic annotation is the process in which semantic concepts are linked to natural language. [2] defines semantic annotation as methods of describing resources (texts, images, etc.)…”

Section: Introductionmentioning

confidence: 99%

“…with meta-data where the meaning has been specified in an ontology. According to [1] semantic annotation can be seen as a methodology of inputting meta-data which are classes, properties, relations and instances (i.e concepts of an ontology) in web resources to be able to give or allocate semantics. Summarizing all definitions we can simply say that semantic annotation is a way of matching resources to ontologies.…”

Section: Introductionmentioning

confidence: 99%

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Enabling Fast Annotation Process With Table2Annotation Tool

Larmande

Jibril

2020

Preprint

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Semantic annotation is the process in which semantic concepts are linked to natural language. It helps in boosting the search and access of resources and can be used in information retrieval systems to increase the queries from the user. In this paper, we are interested in identifying ontological concepts in scientific text contained in spreadsheet. We developed a tool which is able to handle various types of spreadsheet. Furthermore, we used the benefits of NCBO Annotator API provided by BioPortal to enhance the semantic annotation functionalities covering spreadsheet data. Table2Annotation developed strengths in certain criteria like speed, error handling and complex concept matching.

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Semantic annotation tools survey

Cited by 15 publications

References 18 publications

Evolutionary fine-tuning of automated semantic annotation systems

Evolutionary fine-tuning of automated semantic annotation systems

Uma Abordagem para Identificação de Similaridade Entre Recursos Educacionais Utilizando Bases de Conhecimento Externas

Enabling Fast Annotation Process With Table2Annotation Tool

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