Evolving Meaning for Supervised Learning in Complex Biomedical Domains Using Knowledge Graphs

2020 ◽  
pp. 280-290
Author(s):  
Rita T. Sousa
Keyword(s):  
Supervised Learning ◽  
Knowledge Graphs

OAG_know: Self-supervised Learning for Linking Knowledge Graphs

2021 ◽  
pp. 1-1
Author(s):  
Xiao Liu ◽  
Li Mian ◽  
Yuxiao Dong ◽  
Fanjin Zhang ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Knowledge Graphs

scholarly journals Evolving knowledge graph similarity for supervised learning in complex biomedical domains

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Rita T. Sousa ◽  
Sara Silva ◽  
Catia Pesquita
Keyword(s):  
Supervised Learning ◽  
Semantic Similarity ◽  
Protein Interaction ◽  
Expert Knowledge ◽  
Learning Task ◽  
Knowledge Graph ◽  
Interaction Prediction ◽  
Protein Protein Interaction ◽  
Protein Interaction Prediction ◽  
Knowledge Graphs

Abstract Background 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. However, since ontologies can model multiple perspectives, semantic similarity computations for a given learning task need to be fine-tuned to account for this. Obtaining the best combination of semantic similarity aspects for each learning task is not trivial and typically depends on expert knowledge. Results We have developed a novel approach, evoKGsim, that applies Genetic Programming over a set of semantic similarity features, each based on a semantic aspect of the data, to obtain the best combination for a given supervised learning task. The approach was evaluated on several benchmark datasets for protein-protein interaction prediction using the Gene Ontology as the knowledge graph to support semantic similarity, and it outperformed competing strategies, including manually selected combinations of semantic aspects emulating expert knowledge. evoKGsim was also able to learn species-agnostic models with different combinations of species for training and testing, effectively addressing the limitations of predicting protein-protein interactions for species with fewer known interactions. Conclusions evoKGsim can overcome one of the limitations in knowledge graph-based semantic similarity applications: the need to expertly select which aspects should be taken into account for a given application. Applying this methodology to protein-protein interaction prediction proved successful, paving the way to broader applications.

scholarly journals Exploring knowledge graphs for the identification of concept prerequisites

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Rubén Manrique ◽  
Bernardo Pereira ◽  
Olga Mariño
Keyword(s):  
Supervised Learning ◽  
Ground Truth ◽  
Learning Model ◽  
Instructional Designers ◽  
Semantic Web Technologies ◽  
Natural Form ◽  
Web Technologies ◽  
Knowledge Graphs ◽  
Context Of Learning ◽  
Specific Challenge

AbstractLearning basic concepts before complex ones is a natural form of learning. Automated systems and instructional designers evaluate and order concepts’ complexity to successfully generate and recommend or adapt learning paths. This paper addresses the specific challenge of accurately and adequately identifying concept prerequisites using semantic web technologies for a basic understanding of a particular concept within the context of learning: given a target concept c, the goals are to (a) find candidate concepts that serve as possible prerequisite for c; and, (b) evaluate the prerequisite relation between the target and candidates concepts via a supervised learning model. Our four step approach consists of (i) an exploration of Knowledge Graphs in order to identify possible candidate concepts; (ii) the creation of a set of potential concepts; (iii) deployment of supervised learning model to evaluate a proposed list of prerequisite relationships regarding the target set; and, (iv) validation of our approaching using a ground truth of 80 concepts from different domains (with a precision varying between 76% and 96%).

scholarly journals To hop or not, that is the question: Towards effective multi-hop reasoning over knowledge graphs

2021 ◽  
Author(s):  
Jinzhi Liao ◽  
Xiang Zhao ◽  
Jiuyang Tang ◽  
Weixin Zeng ◽  
Zhen Tan
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Supervised Learning ◽  
Large Scale ◽  
State Of The Art ◽  
False Negative ◽  
Stop Signal ◽  
Knowledge Graph ◽  
Overall Performance ◽  
Knowledge Graphs

AbstractWith the proliferation of large-scale knowledge graphs (KGs), multi-hop knowledge graph reasoning has been a capstone that enables machines to be able to handle intelligent tasks, especially where some explicit reasoning path is appreciated for decision making. To train a KG reasoner, supervised learning-based methods suffer from false-negative issues, i.e., unseen paths during training are not to be found in prediction; in contrast, reinforcement learning (RL)-based methods do not require labeled paths, and can explore to cover many appropriate reasoning paths. In this connection, efforts have been dedicated to investigating several RL formulations for multi-hop KG reasoning. Particularly, current RL-based methods generate rewards at the very end of the reasoning process, due to which short paths of hops less than a given threshold are likely to be overlooked, and the overall performance is impaired. To address the problem, we propose , a revised RL formulation of multi-hop KG reasoning that is characterized by two novel designs—the stop signal and the worth-trying signal. The stop signal instructs the agent of RL to stay at the entity after finding the answer, preventing from hopping further even if the threshold is not reached; meanwhile, the worth-trying signal encourages the agent to try to learn some partial patterns from the paths that fail to lead to the answer. To validate the design of our model , comprehensive experiments are carried out on three benchmark knowledge graphs, and the results and analysis suggest the superiority of over state-of-the-art methods.

scholarly journals Validation of Growing Knowledge Graphs by Abductive Text Evidences

2019 ◽  
Vol 33 ◽  
pp. 2784-2791
Author(s):  
Jianfeng Du ◽  
Jeff Z. Pan ◽  
Sylvia Wang ◽  
Kunxun Qi ◽  
Yuming Shen ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Special Class ◽  
Chinese Literature ◽  
Knowledge Graph ◽  
Logical Theory ◽  
Text Corpus ◽  
Efficiency And Effectiveness ◽  
Complete Set ◽  
Knowledge Graphs ◽  
The Right

This paper proposes a validation mechanism for newly added triples in a growing knowledge graph. Given a logical theory, a knowledge graph, a text corpus, and a new triple to be validated, this mechanism computes a sorted list of explanations for the new triple to facilitate the validation of it, where an explanation, called an abductive text evidence, is a set of pairs of the form (triple, window) where appending the set of triples on the left to the knowledge graph enforces entailment of the new triple under the logical theory, while every sentence window on the right which is contained in the text corpus explains to some degree why the triple on the left is true. From the angle of practice, a special class of abductive text evidences called TEP-based abductive text evidence is proposed, which is constructed from explanation patterns seen before in the knowledge graph. Accordingly, a method for computing the complete set of TEP-based abductive text evidences is proposed. Moreover, a method for sorting abductive text evidences based on distantly supervised learning is proposed. To evaluate the proposed validation mechanism, four knowledge graphs with logical theories are constructed from the four great classical masterpieces of Chinese literature. Experimental results on these datasets demonstrate the efficiency and effectiveness of the proposed mechanism.

A Supervised Learning Approach for Dynamic Sampling (SLADS) in Raman Hyperspectral Imaging

2018 ◽  
Vol 2018 (15) ◽  
pp. 132-1-1323
Author(s):  
Shijie Zhang ◽  
Zhengtian Song ◽  
G. M. Dilshan P. Godaliyadda ◽  
Dong Hye Ye ◽  
Atanu Sengupta ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Hyperspectral Imaging ◽  
Learning Approach ◽  
Dynamic Sampling

An IoT Device Identification Method based on Semi-supervised Learning

2020 ◽  
Author(s):  
Linna Fan ◽  
Shize Zhang ◽  
Yichao Wu ◽  
Zhiliang Wang ◽  
Chenxin Duan ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Device Identification ◽  
Identification Method
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