Abstract The airfreight industry of shipping goods with special handling needs, also known as cargo, often deals non-transparent data and outdated technology, resulting in significant inefficiency. A cargo ontology is a means extracting, structuring, storing domain knowledge representing the concepts relationships that can be processed by computers. This used base semantic retrieval many artifi...

Relation ties, defined as the correlation and mutual exclusion between different relations, are critical for distant supervised relation extraction. Previous studies usually obtain this property by greedily learning local connections relations. However, they essentially limited because of failing to capture global topology structure ties may easily fall into a locally optimal solution. To addre...

Cooccurrence relation extraction of abundant tags is very significant for information retrieve and tag recommendation. This paper presents a parallel extraction method of tag cooccurrence relation based on cloud computing. At last, two experiments show that the parallel extraction method can improve the performance of retrievals greatly and have good scalability. The research may play a role in...

Relation extraction with accurate precision is still a challenge when processing full text databases. We propose an approach based on cooccurrence analysis in each document for which we used document organization to improve accuracy of relation extraction. This approach is implemented in a R package called x.ent. Another facet of extraction relies on use of extracted relation into a querying sy...

This paper reports on our work to automatically construct and populate an ontology of wayang (Indonesian shadow puppet) mythology from free text using relation extraction and relation clustering. A reference ontology is used to evaluate the generated ontology. The reference ontology contains concepts and properties within the wayang character domain. We examined the influence of corpus data var...

Relation extraction is the task of finding semantic relations between entities from text. The state-of-the-art methods for relation extraction are mostly based on statistical learning, and thus all have to deal with feature selection, which can significantly affect the classification performance. In this paper, we systematically explore a large space of features for relation extraction and eval...

BACKGROUND The Variome corpus, a small collection of published articles about inherited colorectal cancer, includes annotations of 11 entity types and 13 relation types related to the curation of the relationship between genetic variation and disease. Due to the richness of these annotations, the corpus provides a good testbed for evaluation of biomedical literature information extraction syste...

Distantly supervised relation extraction, which can automatically generate training data by aligning facts in the existing knowledge bases to text, has gained much attention. Previous work used conjunction features with coarse entity types consisting of only four types to train their models. Entity types are important indicators for a specific relation, for example, if the types of two entities...

To reduce the increasing amount of time spent on literature search in the life sciences, several methods for automated knowledge extraction have been developed. Co-occurrence based approaches can deal with large text corpora like MEDLINE in an acceptable time but are not able to extract any specific type of semantic relation. Semantic relation extraction methods based on syntax trees, on the ot...