Improving efficiency and accuracy in multilingual entity extraction

Daiber, Joachim; Jakob, Max; Hokamp, Chris; Mendes, Pablo N.

doi:10.1145/2506182.2506198

Cited by 431 publications

(318 citation statements)

References 3 publications

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“…Several approaches and APIs have been proposed for extracting named entities from text documents and linking them to LOD. One of the most used APIs is DBpedia Spotlight [54,55], which allows for automatically annotating text documents with DBpedia URIs. This tool is used in several LOD enabled data mining approaches, e.g., [56][57][58][59].…”

Section: Using Lod To Interpret Unstructured Datamentioning

confidence: 99%

Semantic Web in Data Mining and Knowledge Discovery: A Comprehensive Survey

Ristoski

Paulheim

2016

SSRN Journal

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a b s t r a c tData Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in that field are knowledge intensive and can often benefit from using additional knowledge from various sources. Therefore, many approaches have been proposed in this area that combine Semantic Web data with the data mining and knowledge discovery process. This survey article gives a comprehensive overview of those approaches in different stages of the knowledge discovery process. As an example, we show how Linked Open Data can be used at various stages for building content-based recommender systems. The survey shows that, while there are numerous interesting research works performed, the full potential of the Semantic Web and Linked Open Data for data mining and KDD is still to be unlocked.

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Section: Using Lod To Interpret Unstructured Datamentioning

confidence: 99%

Semantic Web in Data Mining and Knowledge Discovery: A Comprehensive Survey

Ristoski

Paulheim

2016

SSRN Journal

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“…Textual concepts: The textual concepts used in evaluation of the multimedia retrieval task are extracted using the DBpedia Spotlight annotation tool, which is an open source project for automatic annotation of DBpedia entities in natural language text [6].…”

Section: Feature Extractionmentioning

confidence: 99%

Multimedia retrieval based on non-linear graph-based fusion and partial least squares regression

Gialampoukidis

Moumtzidou

Liparas

et al. 2017

Multimed Tools Appl

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Heterogeneous sources of information, such as images, videos, text and metadata are often used to describe different or complementary views of the same multimedia object, especially in the online news domain and in large annotated image collections. The retrieval of multimedia objects, given a multimodal query, requires the combination of several sources of information in an efficient and scalable way. Towards this direction, we provide a novel unsupervised framework for multimodal fusion of visual and textual similarities, which are based on visual features, visual concepts and textual metadata, integrating non-linear graph-based fusion and Partial Least Squares Regression. The fusion strategy is based on the construction of a multimodal contextual similarity matrix and the non-linear combination of relevance scores from query-based similarity vectors. Our framework can employ more than two modalities and high-level information, without increase in memory complexity, when compared to state-of-the-art baseline methods. The experimental comparison is done in three public multimedia collections in the multimedia retrieval task. The results have shown that the proposed method outperforms the baseline methods, in terms of Mean Average Precision and Precision@20.

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“…With the development of multilingual Wikipedia, researchers have been employing it in many multilingual applications [3,16,17,20,23,24]. Similar to the English-only contexts, each dimension in a multilingual context representation vector represented the relatedness of the target entity with a set of entities/words in the corresponding language.…”

Section: Related Workmentioning

confidence: 99%