scholarly journals Ontology Learning Approaches to Provide Domain-Specific Knowledge Base

2020 ◽  
Vol 176 ◽  
pp. 3324-3334
Author(s):  
Agnieszka Konys ◽  
Zygmunt Drążek
Keyword(s):  
Knowledge Base ◽  
Learning Approaches ◽  
Ontology Learning ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Domain Specific Knowledge

scholarly journals Diffusion enables integration of heterogeneous data and user-driven learning in a desktop knowledge-base

2021 ◽  
Vol 17 (8) ◽  
pp. e1009283
Author(s):  
Tomasz Konopka ◽  
Sandra Ng ◽  
Damian Smedley
Keyword(s):  
Knowledge Base ◽  
Nearest Neighbor ◽  
Heterogeneous Data ◽  
Data Types ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Heterogeneous Datasets ◽  
Domain Specific Knowledge ◽  
High Throughput Experiments ◽  
Gene Symbols

Integrating reference datasets (e.g. from high-throughput experiments) with unstructured and manually-assembled information (e.g. notes or comments from individual researchers) has the potential to tailor bioinformatic analyses to specific needs and to lead to new insights. However, developing bespoke analysis pipelines from scratch is time-consuming, and general tools for exploring such heterogeneous data are not available. We argue that by treating all data as text, a knowledge-base can accommodate a range of bioinformatic data types and applications. We show that a database coupled to nearest-neighbor algorithms can address common tasks such as gene-set analysis as well as specific tasks such as ontology translation. We further show that a mathematical transformation motivated by diffusion can be effective for exploration across heterogeneous datasets. Diffusion enables the knowledge-base to begin with a sparse query, impute more features, and find matches that would otherwise remain hidden. This can be used, for example, to map multi-modal queries consisting of gene symbols and phenotypes to descriptions of diseases. Diffusion also enables user-driven learning: when the knowledge-base cannot provide satisfactory search results in the first instance, users can improve the results in real-time by adding domain-specific knowledge. User-driven learning has implications for data management, integration, and curation.

scholarly journals Deep Learning-Based Knowledge Graph Generation for COVID-19

2021 ◽  
Vol 13 (4) ◽  
pp. 2276
Author(s):  
Taejin Kim ◽  
Yeoil Yun ◽  
Namgyu Kim
Keyword(s):  
Knowledge Base ◽  
Language Model ◽  
Fine Tuning ◽  
Knowledge Graph ◽  
Specific Information ◽  
Specific Knowledge ◽  
Text Documents ◽  
Domain Specific ◽  
Open Information Extraction ◽  
Domain Specific Knowledge

Many attempts have been made to construct new domain-specific knowledge graphs using the existing knowledge base of various domains. However, traditional “dictionary-based” or “supervised” knowledge graph building methods rely on predefined human-annotated resources of entities and their relationships. The cost of creating human-annotated resources is high in terms of both time and effort. This means that relying on human-annotated resources will not allow rapid adaptability in describing new knowledge when domain-specific information is added or updated very frequently, such as with the recent coronavirus disease-19 (COVID-19) pandemic situation. Therefore, in this study, we propose an Open Information Extraction (OpenIE) system based on unsupervised learning without a pre-built dataset. The proposed method obtains knowledge from a vast amount of text documents about COVID-19 rather than a general knowledge base and add this to the existing knowledge graph. First, we constructed a COVID-19 entity dictionary, and then we scraped a large text dataset related to COVID-19. Next, we constructed a COVID-19 perspective language model by fine-tuning the bidirectional encoder representations from transformer (BERT) pre-trained language model. Finally, we defined a new COVID-19-specific knowledge base by extracting connecting words between COVID-19 entities using the BERT self-attention weight from COVID-19 sentences. Experimental results demonstrated that the proposed Co-BERT model outperforms the original BERT in terms of mask prediction accuracy and metric for evaluation of translation with explicit ordering (METEOR) score.

scholarly journals Software Process Ontology: A case study of software organisations software process sub domains

2019 ◽  
pp. 122-130
Author(s):  
R. O. Oveh ◽  
O. Efevberha-Ogodo ◽  
F. A. Egbokhare
Keyword(s):  
Knowledge Base ◽  
Software Process ◽  
Formal Representation ◽  
Specific Knowledge ◽  
Process Ontology ◽  
Process Knowledge ◽  
Domain Specific ◽  
Domain Specific Knowledge ◽  
Intellectual Knowledge

In a domain like software process that is intensively knowledge driven, transforming intellectual knowledge by formal representation is an invaluable requirement. An improved use of this knowledge could lead to maximum payoff in software organisations which is key. The purpose of formal representation is to help organisations achieve success by modelling successful organisations. In this paper, Software process knowledge from successful organisations was harvested and formally modeled using ontology. Domain specific knowledge base ontology was produced for core software process subdomain, with its resulting software process ontology produced.

OMIT: A Domain-Specific Knowledge Base for MicroRNA Target Prediction

2011 ◽  
Vol 28 (12) ◽  
pp. 3101-3104 ◽  
Author(s):  
Jingshan Huang ◽  
Christopher Townsend ◽  
Dejing Dou ◽  
Haishan Liu ◽  
Ming Tan
Keyword(s):  
Knowledge Base ◽  
Target Prediction ◽  
Microrna Target ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Microrna Target Prediction ◽  
Domain Specific Knowledge

From Opportunity Insight to Opportunity Intention: The Importance of Person–Situation Learning Match

2007 ◽  
Vol 31 (4) ◽  
pp. 561-583 ◽  
Author(s):  
Dimo Dimov
Keyword(s):  
Prior Knowledge ◽  
Learning Style ◽  
Learning Process ◽  
Learning Approaches ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Early Stages ◽  
Potential Entrepreneur ◽  
Domain Specific Knowledge

Within the context of opportunity development as a learning process, this paper explores the intentionality that drives the early stages of this process, from the initial occurrence of an idea to its further exploration and elaboration by a potential entrepreneur. It establishes that the specific situations that induce opportunity insights also affect the roles that individuals’ prior knowledge and learning approaches play for the formation of opportunity intentions. The likelihood of acting on their initial opportunity insights depends not only on how much prior knowledge individuals have of the opportunity domain, but also on whether their learning style matches the situation at hand. The results from an experiment show that domain–specific knowledge enables action when there is a person–situation match and impedes it when such a match is lacking.

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