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Source publication
This paper presents a template-based information extraction
system for Arabic descriptive text understanding. The system
depends on knowledge base. The knowledge base contains
facts and rules. The facts are derived from AL Khalil lexicon,
Al Ramous lexicon and a Stanford model. The rules represent
the designed templates. The templates are helpfu...
Similar publications
Nowadays, structured information that obtains from unstructured texts and Web context can be applied as an additional source of knowledge to create ontologies. In order to extract information from a text and represent it in the RDF-triplets format, we suggest using the Open Information Extraction model. Then we consider the adaptation of the model...
Citations
... Relational extraction methods are generally classified as template-based [8], supervised-based [9], and weakly supervised-based relationship extraction [10]. The templatebased approach uses pre-defined relationship templates by domain experts and then matches relationships from the text. ...
... The decision attribute D divides the argument domain U into N equivalence classes (X1, X2…XN), BA. The lower approximation of the decision attribute D concerning subset B is calculated by (8) and (9) ...
Acquiring welding domain relationships and forming a knowledge graph can positively impact complex engineering problem solving and intelligent manufacturing applications. However, relationships are lacking in the welding domain. The relationship extraction and processing solution are designed to handle data with different characteristics in welding fabrication. The BiLSTM+Attention and CR-CNN models are employed to extract relations in unstructured documents. The neighborhood rough set-based association rule model is proposed for project-specific documents to accomplish relationship acquisition, in which invalid attributes are removed via neighborhood rough sets and attribute values are related via association rules. In addition, the knowledge graph is built based on extracted relationships, and unique empirical relationships are handled by introducing relational nodes and databases. The results show that BiLSTM+Attention gets a good score with Macro-average metrics (0.788 for Precision, 0.846 for Recall, and 0.816 for F1-score). The relational rules obtained via the proposed model are consistent with the production experience. The constructed knowledge graph effectively handles empirical relationships while positively impacting knowledge retrieval, intelligent question and answer, and decision-making for complex engineering problems.
... Designed templates are related to the task which needs these templates [5]. Many of template based systems are built, some of them depends on previously designed templates such as [6], whereas other don't have the templates previously and depend on methods as rules to mine the text templates [7]. In [8] the templates are mined from a plain text corpus for finding paraphrases in text using part of speech tagger to extract entities. ...
In this paper, the named entity recognition system is built
using morphological, lexical and semantic analysis. Rule
based system is designed for template mining from the Arabic
text. Arabic texts are selected from oil production domain.
They are taken from Arabic BBC, RT and CNN websites. The
System is tested on these texts and the results give high
performance, less error made and good accuracy in finding the
templates from texts according to named entities extracted.
