Semantic Annotation of Educational Resources through Linked Data

Otero-García, Estefanía; Vidal, Juan C.; Lama, Manuel; Bugarín, Alberto; Domenech, José E.

doi:10.1007/978-3-642-20539-2_33

Cited by 3 publications

(3 citation statements)

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“…The search algorithm presented in [14] has been performed in two steps. In the first one, for each relevant term of the document to annotate, a unique DBpedia node (or equivalently, a unique URI) is retrieved through the lookup service of DBpedia.…”

Section: Querying Linked Datamentioning

confidence: 99%

“…• The second part calculates the diversity of the node x, that is, the relation of shared terms between the node and the context c. The way the similarity and diversity are calculated is out of the scope of this work, however exact formulas can be found in [14].…”

Section: B Fitness Functionmentioning

confidence: 99%

“…In [14] a context-based algorithm to annotate (educational) documents is presented. This algorithm considers that each relation of DBpedia (relation between two DBpedia concepts) has a relevance which is given by a weight.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An evolutionary approach for learning the weight of relations in linked data

Vidal

Lama

Otero-Garcia

et al. 2011

2011 11th International Conference on Intelligent Systems Design and Applications

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In this paper we present an approach for improving a specific class of semantic annotation, that relates a term of the document with a (sub)tree of the ontology, instead of linking a term with a single concept of the ontology. An important part of this class of annotation is filtering the relevant (sub)nodes and relations, because the returned graph should only contain relevant information, that is, nodes that are truly related with the topics of the document. In addition, we consider that the relevance of nodes vary depending on if the node is a branch or a leaf, that is, if the node has links to other nodes or it is a text-based description. This paper focuses on the relevance of branch nodes, which is calculated from the relevance of its links, since leaf nodes relevance is usually estimated by similarity metrics. Specifically, our approach incises in learning (through a genetic algorithm) and assigning the most appropriate weights to these links in order to reduce the precision/recall curve of the annotation process. The results show that our solution is viable and outperforms the state of the art approaches.

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Section: Querying Linked Datamentioning

confidence: 99%

Section: B Fitness Functionmentioning

confidence: 99%