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Ontology Learning
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The taxonomic structure of microbial community sample is highly habitat-specific, making source tracking possible, allowing identification of the niches where samples originate. However, current methods face challenges when source tracking is scaled up.
17p
viellison
28-03-2024
2
2
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Concept normalization, the task of linking phrases in text to concepts in an ontology, is useful for many downstream tasks including relation extraction, information retrieval, etc. We present a generate-andrank concept normalization system based on our participation in the 2019 National NLP Clinical Challenges Shared Task Track 3 Concept Normalization.
10p
vighostrider
25-05-2023
4
2
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SNOMED CT is the largest clinical terminology worldwide. Quality assurance of SNOMED CT is of utmost importance to ensure that it provides accurate domain knowledge to various SNOMED CT-based applications. In this work, we introduce a deep learning-based approach to uncover missing is-a relations in SNOMED CT.
10p
vighostrider
25-05-2023
3
2
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Ebook Methods and applications for advancing distance education technologies: international issues and solutions – Part 1 presents the following content: Automatic digital content generation system for real-time distance lectures; E-World: A platform for the management of adaptive e-learning processes; An efficient and effective approach to developing engineering e-training courses; A SCORM compliant courseware authoring tool for supporting pervasive learning; An ontology-based e-learning scenario;…
193p
runthenight05
30-01-2023
3
2
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Bài viết Xây dựng hệ thống web ngữ nghĩa hỗ trợ tra cứu pháp luật Việt Nam đề xuất xây dựng một hệ thống tra cứu và tìm hiểu Pháp luật Việt Nam hoàn chỉnh. Hệ thống mà chúng tôi hướng đến không chỉ dừng lại ở mức độ tìm kiếm và tra cứu mà còn cho phép làm việc như một hệ thống e-learning hỗ trợ sinh viên nghiên cứu, học tập và thi trắc nghiệm Pháp luật Trực tuyến.
6p
vikoenigsegg
29-09-2022
41
7
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A bottleneck in high-throughput functional genomics experiments is identifying the most important genes and their relevant functions from a list of gene hits. Gene Ontology (GO) enrichment methods provide insight at the gene set level.
35p
viarchimedes
26-01-2022
6
0
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Semantic matching has received much attention in the database, AI, KDD, Web, and Semantic Web communities. Recently, many works have also applied deep learning (DL) to semantic matching. In this paper we survey this fast growing topic. We define the semantic matching problem, categorize its variations into a taxonomy, and describe important applications. We describe DL solutions for important variations of semantic matching. Finally, we discuss future R&D directions.
38p
spiritedaway36
25-11-2021
12
0
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This paper experiments with the deep learning model convolution neural network (CNN), long short-term memory (LSTM), and the combined model of CNN and LSTM. The training data set comprise reviews of cars in Vietnamese that are pre-processed according to the method of aspect analysis based on an ontology of semantic and sentimental approaches.
7p
viaespa2711
31-07-2021
17
1
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In this paper, we introduce a hybrid mechanism between ontology-based and unsupervised machine learning strategies in creating activity models used for activity recognition in the context of multi-resident homes. Comparing to related data-driven approaches, the proposed technique is technically and practically scalable to real-world scenarios due to fast training time and easy implementation. An average activity recognition rate of 95.83% on CASAS Spring dataset was achieved and the average recognition run time per operation was measured as 12.86 mili-seconds.
16p
cothumenhmong11
05-05-2021
9
1
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In this paper, we have surveyed some typical facial attribute learning methods. Five major categories of the state-of-the-art methods are identified: (1) Traditional learning, (2) Deep Single Task Learning, (3) Deep Multitask Learning, (4) Imbalanced Data Solver, and (5) Facial Attribute Ontology. They included from traditional learning algorithm to deep learning, along with methods that assist in solving semantic gaps based on ontology and solving data imbalances. For each algorithm of category, basic theories as well as their strengths, weaknesses, and differences are discussed.
20p
angicungduoc11
18-04-2021
27
1
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Gene Ontology (GO) is a popular standard in the annotation of gene products and provides information related to genes across all species. The structure of GO is dynamic and is updated on a daily basis.
7p
viwyoming2711
16-12-2020
11
1
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Ontologies and catalogs of gene functions, such as the Gene Ontology (GO) and MIPS-FUN, assume that functional classes are organized hierarchically, that is, general functions include more specific ones. This has recently motivated the development of several machine learning algorithms for gene function prediction that leverages on this hierarchical organization where instances may belong to multiple classes.
18p
viwyoming2711
16-12-2020
18
0
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Biological data has grown explosively with the advance of nextgeneration sequencing. However, annotating protein function with wet lab experiments is time-consuming. Fortunately, computational function prediction can help wet labs formulate biological hypotheses and prioritize experiments.
16p
vikentucky2711
24-11-2020
12
2
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Circular RNAs (circRNAs) are special noncoding RNA molecules with closed loop structures. Compared with the traditional linear RNA, circRNA is more stable and not easily degraded.
18p
vikentucky2711
24-11-2020
14
1
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Identification of gene expression profiles that differentiate experimental groups is critical for discovery and analysis of key molecular pathways and also for selection of robust diagnostic or prognostic biomarkers.
12p
vioklahoma2711
19-11-2020
24
2
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Deciphering complete networks of interactions between proteins is the key to comprehend cellular regulatory mechanisms. A significant effort has been devoted to expanding the coverage of the proteome-wide interaction space at molecular level.
14p
viflorida2711
30-10-2020
19
1
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Recent studies have proposed deep learning techniques, namely recurrent neural networks, to improve biomedical text mining tasks. However, these techniques rarely take advantage of existing domain-specific resources, such as ontologies. In Life and Health Sciences there is a vast and valuable set of such resources publicly available, which are continuously being updated.
12p
vicoachella2711
27-10-2020
16
1
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In the era of precision oncology and publicly available datasets, the amount of information available for each patient case has dramatically increased. From clinical variables and PET-CT radiomics measures to DNAvariant and RNA expression profiles, such a wide variety of data presents a multitude of challenges.
9p
vijisoo2711
27-10-2020
15
1
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In recent years, biomedical ontologies have become important for describing existing biological knowledge in the form of knowledge graphs. Data mining approaches that work with knowledge graphs have been proposed, but they are based on vector representations that do not capture the full underlying semantics. An alternative is to use machine learning approaches that explore semantic similarity.
19p
vicolorado2711
23-10-2020
15
2
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n this research work we have inducted the taxonomy from USDA soil taxonomy, as well as extracted the property. More importantly we have done these tasks which are based on regular expression and with the help of connectives. Connective based taxonomy and property extraction helps us not to use huge corpus behind the text processing. In future the framework can be used for other domains in taxonomic text. It may also be extended for the other aspects of ontology learning like identification of axioms and constraints.
11p
trinhthamhodang1213
30-05-2020
23
0
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