Semantic Information Extraction on Domain Specific Data Sheets

2014 ◽  
pp. 864-873 ◽  
Author(s):  
Kai Barkschat
Keyword(s):  
Information Extraction ◽  
Semantic Information ◽  
Specific Data ◽  
Domain Specific

scholarly journals ENRICHMENT AND POPULATION OF A GEOSPATIAL ONTOLOGY FOR SEMANTIC INFORMATION EXTRACTION

2018 ◽  
Vol XLII-4 ◽  
pp. 309-314 ◽  
Author(s):  
M. Kokla ◽  
V. Papadias ◽  
E. Tomai
Keyword(s):  
Information Extraction ◽  
Semantic Information ◽  
Extraction Process ◽  
User Generated Content ◽  
Web Content ◽  
Top Down ◽  
Bottom Up ◽  
Great Opportunity ◽  
Domain Specific ◽  
Geospatial Ontology

<p><strong>Abstract.</strong> The massive amount of user-generated content available today presents a new challenge for the geospatial domain and a great opportunity to delve into linguistic, semantic, and cognitive aspects of geographic information. Ontology-based information extraction is a new, prominent field in which a domain ontology guides the extraction process and the identification of pre-defined concepts, properties, and instances from natural language texts. The paper describes an approach for enriching and populating a geospatial ontology using both a top-down and a bottom-up approach in order to enable semantic information extraction. The top-down approach is applied in order to incorporate knowledge from existing ontologies. The bottom-up approach is applied in order to enrich and populate the geospatial ontology with semantic information (concepts, relations, and instances) extracted from domain-specific web content.</p>

scholarly journals TaxoNERD: deep neural models for the recognition of taxonomic entities in the ecological and evolutionary literature

2021 ◽  
Author(s):  
Nicolas Le Guillarme ◽  
Wilfried Thuiller
Keyword(s):  
Information Extraction ◽  
High Performance ◽  
Named Entity Recognition ◽  
Open Data ◽  
Entity Recognition ◽  
Data Repositories ◽  
Biodiversity Crisis ◽  
Domain Specific ◽  
Ecological Information ◽  
Gold Standard Corpus

1. Given the biodiversity crisis, we more than ever need to access information on multiple taxa (e.g. distribution, traits, diet) in the scientific literature to understand, map and predict all-inclusive biodiversity. Tools are needed to automatically extract useful information from the ever-growing corpus of ecological texts and feed this information to open data repositories. A prerequisite is the ability to recognise mentions of taxa in text, a special case of named entity recognition (NER). In recent years, deep learning-based NER systems have become ubiqutous, yielding state-of-the-art results in the general and biomedical domains. However, no such tool is available to ecologists wishing to extract information from the biodiversity literature. 2. We propose a new tool called TaxoNERD that provides two deep neural network (DNN) models to recognise taxon mentions in ecological documents. To achieve high performance, DNN-based NER models usually need to be trained on a large corpus of manually annotated text. Creating such a gold standard corpus (GSC) is a laborious and costly process, with the result that GSCs in the ecological domain tend to be too small to learn an accurate DNN model from scratch. To address this issue, we leverage existing DNN models pretrained on large biomedical corpora using transfer learning. The performance of our models is evaluated on four GSCs and compared to the most popular taxonomic NER tools. 3. Our experiments suggest that existing taxonomic NER tools are not suited to the extraction of ecological information from text as they performed poorly on ecologically-oriented corpora, either because they do not take account of the variability of taxon naming practices, or because they do not generalise well to the ecological domain. Conversely, a domain-specific DNN-based tool like TaxoNERD outperformed the other approaches on an ecological information extraction task. 4. Efforts are needed in order to raise ecological information extraction to the same level of performance as its biomedical counterpart. One promising direction is to leverage the huge corpus of unlabelled ecological texts to learn a language representation model that could benefit downstream tasks. These efforts could be highly beneficial to ecologists on the long term.

scholarly journals DISSECT: DISentangle SharablE ConTent for Multimodal Integration and Crosswise-mapping

2020 ◽  
Author(s):  
Geoffrey Schau ◽  
Erik Burlingame ◽  
Young Hwan Chang
Keyword(s):  
Deep Learning ◽  
Complete Information ◽  
Specific Information ◽  
Multimodal Integration ◽  
Specific Data ◽  
Domain Specific ◽  
Cross Domain ◽  
Input Feature ◽  
Novel Approach ◽  
Latent Representations

AbstractDeep learning systems have emerged as powerful mechanisms for learning domain translation models. However, in many cases, complete information in one domain is assumed to be necessary for sufficient cross-domain prediction. In this work, we motivate a formal justification for domain-specific information separation in a simple linear case and illustrate that a self-supervised approach enables domain translation between data domains while filtering out domain-specific data features. We introduce a novel approach to identify domainspecific information from sets of unpaired measurements in complementary data domains by considering a deep learning cross-domain autoencoder architecture designed to learn shared latent representations of data while enabling domain translation. We introduce an orthogonal gate block designed to enforce orthogonality of input feature sets by explicitly removing non-sharable information specific to each domain and illustrate separability of domain-specific information on a toy dataset.

scholarly journals Semantic Information Generation from Classification and Information Extraction

2004 ◽  
pp. 573-574
Author(s):  
Tércio de Morais Sampaio Silva ◽  
Frederico Luiz Gonçalves de Freitas ◽  
Rafael Cobra Teske ◽  
Guilherme Bittencourt
Keyword(s):  
Information Extraction ◽  
Semantic Information ◽  
Information Generation

Domain-specific data mining for residents' transit pattern retrieval from incomplete information

2019 ◽  
Vol 134 ◽  
pp. 62-71 ◽  
Author(s):  
Yongxin Liu ◽  
Jianqiang Li ◽  
Zhong Ming ◽  
Houbing Song ◽  
Xiaoxiong Weng ◽  
...  
Keyword(s):  
Data Mining ◽  
Incomplete Information ◽  
Specific Data ◽  
Domain Specific ◽  
Pattern Retrieval
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