Web Community Knowledge Extraction for myCBR 3

2011 ◽  
pp. 239-252 ◽  
Author(s):  
Christian Sauer ◽  
Thomas Roth-Berghofer
Keyword(s):  
Knowledge Extraction ◽  
Community Knowledge ◽  
Web Community

Web article quality ranking based on web community knowledge

Computing ◽  
2014 ◽  
Vol 97 (5) ◽  
pp. 509-537 ◽  
Author(s):  
Jingyu Han ◽  
Kejia Chen ◽  
Jianing Wang
Keyword(s):  
Community Knowledge ◽  
Web Community ◽  
Quality Ranking ◽  
Article Quality

A Multi-Language System For Knowledge Extraction In E-Learning Videos

2006 ◽  
Vol 3 (1) ◽  
pp. 48-55
Author(s):  
Aparesh Sood ◽  
◽  
Ankush Mittal ◽  
Divya Sarthi ◽  
◽  
...  
Keyword(s):  
Knowledge Extraction ◽  
Language System ◽  
E Learning

scholarly journals Context-Aware Holographic Communication Based on Semantic Knowledge Extraction

2021 ◽  
Author(s):  
Agata Manolova ◽  
Krasimir Tonchev ◽  
Vladimir Poulkov ◽  
Sudhir Dixir ◽  
Peter Lindgren
Keyword(s):  
Deep Neural Networks ◽  
Data Gathering ◽  
Communication Technologies ◽  
Knowledge Extraction ◽  
Semantic Knowledge ◽  
Business Model Innovation ◽  
Context Aware ◽  
Human Body Model ◽  
Network Resource ◽  
Body Model

AbstractAugmented, mixed and virtual reality are changing the way people interact and communicate. Five dimensional communications and services, integrating information from all human senses are expected to emerge, together with holographic communications (HC), providing a truly immersive experience. HC presents a lot of challenges in terms of data gathering and transmission, demanding Artificial Intelligence empowered communication technologies such as 5G. The goal of the paper is to present a model of a context-aware holographic architecture for real time communication based on semantic knowledge extraction. This architecture will require analyzing, combining and developing methods and algorithms for: 3D human body model acquisition; semantic knowledge extraction with deep neural networks to predict human behaviour; analysis of biometric modalities; context-aware optimization of network resource allocation for the purpose of creating a multi-party, from-capturing-to-rendering HC framework. We illustrate its practical deployment in a scenario that can open new opportunities in user experience and business model innovation.

scholarly journals Knowledge extraction for assisted curation of summaries of bacterial transcription factor properties

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Carlos-Francisco Méndez-Cruz ◽  
Antonio Blanchet ◽  
Alan Godínez ◽  
Ignacio Arroyo-Fernández ◽  
Socorro Gama-Castro ◽  
...  
Keyword(s):  
Transcriptional Regulation ◽  
Knowledge Extraction ◽  
Biomedical Literature ◽  
Main Role ◽  
E Coli ◽  
Bacterial Transcription ◽  
Manual Curation ◽  
New Knowledge ◽  
Serovar Typhimurium ◽  
K 12

Abstract Transcription factors (TFs) play a main role in transcriptional regulation of bacteria, as they regulate transcription of the genetic information encoded in DNA. Thus, the curation of the properties of these regulatory proteins is essential for a better understanding of transcriptional regulation. However, traditional manual curation of article collections to compile descriptions of TF properties takes significant time and effort due to the overwhelming amount of biomedical literature, which increases every day. The development of automatic approaches for knowledge extraction to assist curation is therefore critical. Here, we show an effective approach for knowledge extraction to assist curation of summaries describing bacterial TF properties based on an automatic text summarization strategy. We were able to recover automatically a median 77% of the knowledge contained in manual summaries describing properties of 177 TFs of Escherichia coli K-12 by processing 5961 scientific articles. For 71% of the TFs, our approach extracted new knowledge that can be used to expand manual descriptions. Furthermore, as we trained our predictive model with manual summaries of E. coli, we also generated summaries for 185 TFs of Salmonella enterica serovar Typhimurium from 3498 articles. According to the manual curation of 10 of these Salmonella typhimurium summaries, 96% of their sentences contained relevant knowledge. Our results demonstrate the feasibility to assist manual curation to expand manual summaries with new knowledge automatically extracted and to create new summaries of bacteria for which these curation efforts do not exist. Database URL: The automatic summaries of the TFs of E. coli and Salmonella and the automatic summarizer are available in GitHub (https://github.com/laigen-unam/tf-properties-summarizer.git).

scholarly journals Frequent Itemset Mining and Multi-Layer Network-Based Analysis of RDF Databases

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 450
Author(s):  
Gergely Honti ◽  
János Abonyi
Keyword(s):  
Climate Change ◽  
Extraction Process ◽  
Knowledge Extraction ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Underlying Structure ◽  
Multilayer Network ◽  
Interdisciplinary Science ◽  
Academic Knowledge ◽  
Itemset Mining

Triplestores or resource description framework (RDF) stores are purpose-built databases used to organise, store and share data with context. Knowledge extraction from a large amount of interconnected data requires effective tools and methods to address the complexity and the underlying structure of semantic information. We propose a method that generates an interpretable multilayered network from an RDF database. The method utilises frequent itemset mining (FIM) of the subjects, predicates and the objects of the RDF data, and automatically extracts informative subsets of the database for the analysis. The results are used to form layers in an analysable multidimensional network. The methodology enables a consistent, transparent, multi-aspect-oriented knowledge extraction from the linked dataset. To demonstrate the usability and effectiveness of the methodology, we analyse how the science of sustainability and climate change are structured using the Microsoft Academic Knowledge Graph. In the case study, the FIM forms networks of disciplines to reveal the significant interdisciplinary science communities in sustainability and climate change. The constructed multilayer network then enables an analysis of the significant disciplines and interdisciplinary scientific areas. To demonstrate the proposed knowledge extraction process, we search for interdisciplinary science communities and then measure and rank their multidisciplinary effects. The analysis identifies discipline similarities, pinpointing the similarity between atmospheric science and meteorology as well as between geomorphology and oceanography. The results confirm that frequent itemset mining provides an informative sampled subsets of RDF databases which can be simultaneously analysed as layers of a multilayer network.

scholarly journals Community readiness for collecting stray dogs in Aradan County: a quantitative‐qualitative study

2021 ◽  
Vol 74 (1) ◽  
Author(s):  
Tahereh Kamalikhah ◽  
Somayeh Mirrezaei ◽  
Tahereh Rahimi ◽  
Leila Sabzmakan ◽  
Safiye Ghobakhloo
Keyword(s):  
Rural Areas ◽  
Rating Scale ◽  
Public Awareness ◽  
City Council ◽  
Community Readiness ◽  
Community Knowledge ◽  
Stray Dogs ◽  
Community Readiness Model ◽  
Six Dimensions ◽  
Community Climate

Abstract Background Paying more attention to free-roaming dogs’ population control seems to be necessary because of public health and environmental problems. The present study used the community readiness model to determine the readiness of Aradan County in terms of collecting stray dogs. Methods This study is a quantitative-qualitative research study conducted in Aradan County in Semnan Province of Iran. The semi-structured questionnaire uses the six dimensions of the Community Readiness Model as guideline, with 36 items used for the interview. The interviews lasted 45 to 100 minutes with 11 key members including the governor, prefect, mayor’s assistant, city council chairman, key trustees, officials responsible for environmental health network, officials responsible for environmentalism of the city, and governors of a rural district. In quantitative part two, assessors read the interviews carefully and assigned scores based on the rating-scale form suggested by the guideline itself for scoring each dimension. A qualitative directed content analysis with deductive approach was used for analyzing the collected qualitative data. Results The study involved 11 key members of Aradan County, all of whom were male. Most of the participants were over 40 years old and with five years of work experience (73.6 %). The mean score of each six dimensions in Aradan County were: Community efforts (4.78), Community knowledge of efforts (4.28), Leadership (4.90), Community climate (4.38), Community knowledge about the issue (4.20) and Resources related to the issue (3.29) respectively. Community readiness in Aradan County and Aradan City was generally estimated to be in the preplanning stage, whereas vague public awareness was found in the rural areas. In the qualitative part, 870 initial open codes, 589 refund codes, 19 subcategories and 6 themes emerged, including (a) community efforts, (b) community knowledge of the efforts (c) leadership, (d) community climate, (e) community knowledge of the issue, and (f) resources related to the issue. Conclusions For improving the process of collecting the stray dogs, it is necessary to focus on holding educational sessions for the public to increase their partnership and justify the responsible organizations’ activities to collaborate and provide the necessary financial resources.

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