Journal articles: 'Knowledge Base Completion'

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Choi, Hyun-Young, Ji-Hun Hong, Wan-Gon Lee, Batselem Jagvaral, Myung-Joong Jeon, Hyun-Kyu Park, and Young-Tack Park. "Knowledge Completion Modeling using Knowledge Base Embedding." Journal of KIISE 45, no. 9 (September 30, 2018): 895–903. http://dx.doi.org/10.5626/jok.2018.45.9.895.

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Choi, Su Jeong, Hyun-Je Song, and Seong-Bae Park. "An Approach to Knowledge Base Completion by a Committee-Based Knowledge Graph Embedding." Applied Sciences 10, no. 8 (April 11, 2020): 2651. http://dx.doi.org/10.3390/app10082651.

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Knowledge bases such as Freebase, YAGO, DBPedia, and Nell contain a number of facts with various entities and relations. Since they store many facts, they are regarded as core resources for many natural language processing tasks. Nevertheless, they are not normally complete and have many missing facts. Such missing facts keep them from being used in diverse applications in spite of their usefulness. Therefore, it is significant to complete knowledge bases. Knowledge graph embedding is one of the promising approaches to completing a knowledge base and thus many variants of knowledge graph embedding have been proposed. It maps all entities and relations in knowledge base onto a low dimensional vector space. Then, candidate facts that are plausible in the space are determined as missing facts. However, any single knowledge graph embedding is insufficient to complete a knowledge base. As a solution to this problem, this paper defines knowledge base completion as a ranking task and proposes a committee-based knowledge graph embedding model for improving the performance of knowledge base completion. Since each knowledge graph embedding has its own idiosyncrasy, we make up a committee of various knowledge graph embeddings to reflect various perspectives. After ranking all candidate facts according to their plausibility computed by the committee, the top-k facts are chosen as missing facts. Our experimental results on two data sets show that the proposed model achieves higher performance than any single knowledge graph embedding and shows robust performances regardless of k. These results prove that the proposed model considers various perspectives in measuring the plausibility of candidate facts.

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Srinivasa, K., and P. Santhi Thilagam. "Clustering and Bootstrapping Based Framework for News Knowledge Base Completion." Computing and Informatics 40, no. 2 (2021): 318–40. http://dx.doi.org/10.31577/cai_2021_2_318.

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Lin, Xixun, Yanchun Liang, Limin Wang, Xu Wang, Mary Qu Yang, and Renchu Guan. "A Knowledge Base Completion Model Based on Path Feature Learning." International Journal of Computers Communications & Control 13, no. 1 (February 12, 2018): 71. http://dx.doi.org/10.15837/ijccc.2018.1.3104.

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Large-scale knowledge bases, as the foundations for promoting the development of artificial intelligence, have attracted increasing attention in recent years. These knowledge bases contain billions of facts in triple format; yet, they suffer from sparse relations between entities. Researchers proposed the path ranking algorithm (PRA) to solve this fatal problem. To improve the scalability of knowledge inference, PRA exploits random walks to find Horn clauses with chain structures to predict new relations given existing facts. This method can be regarded as a statistical classification issue for statistical relational learning (SRL). However, large-scale knowledge base completion demands superior accuracy and scalability. In this paper, we propose the path feature learning model (PFLM) to achieve this urgent task. More precisely, we define a two-stage model: the first stage aims to learn path features from the existing knowledge base and extra parsed corpus; the second stage uses these path features to predict new relations. The experimental results demonstrate that the PFLM can learn meaningful features and can achieve significant and consistent improvements compared with previous work.

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Hamaguchi, Takuo, Hidekazu Oiwa, Masashi Shimbo, and Yuji Matsumoto. "Knowledge Base Completion with Out-of-Knowledge-Base Entities: A Graph Neural Network Approach." Transactions of the Japanese Society for Artificial Intelligence 33, no. 2 (2018): F—H72_1–10. http://dx.doi.org/10.1527/tjsai.f-h72.

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Kong, Fanshuang, Richong Zhang, Yongyi Mao, and Ting Deng. "LENA: Locality-Expanded Neural Embedding for Knowledge Base Completion." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2895–902. http://dx.doi.org/10.1609/aaai.v33i01.33012895.

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Embedding based models for knowledge base completion have demonstrated great successes and attracted significant research interest. In this work, we observe that existing embedding models all have their loss functions decomposed into atomic loss functions, each on a triple or an postulated edge in the knowledge graph. Such an approach essentially implies that conditioned on the embeddings of the triple, whether the triple is factual is independent of the structure of the knowledge graph. Although arguably the embeddings of the entities and relation in the triple contain certain structural information of the knowledge base, we believe that the global information contained in the embeddings of the triple can be insufficient and such an assumption is overly optimistic in heterogeneous knowledge bases. Motivated by this understanding, in this work we propose a new embedding model in which we discard the assumption that the embeddings of the entities and relation in a triple is a sufficient statistic for the triple’s factual existence. More specifically, the proposed model assumes that whether a triple is factual depends not only on the embedding of the triple but also on the embeddings of the entities and relations in a larger graph neighbourhood. In this model, attention mechanisms are constructed to select the relevant information in the graph neighbourhood so that irrelevant signals in the neighbourhood are suppressed. Termed locality-expanded neural embedding with attention (LENA), this model is tested on four standard datasets and compared with several stateof-the-art models for knowledge base completion. Extensive experiments suggest that LENA outperforms the existing models in virtually every metric.

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Chen, Samuel, Shengyi Xie, and Qingqiang Chen. "Integrated Embedding Approach for Knowledge Base Completion with CNN." Information Technology And Control 49, no. 4 (December 19, 2020): 622–42. http://dx.doi.org/10.5755/j01.itc.49.4.25366.

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To tackle specific problems in knowledge base completion such as computational complexity and complex relations or nodes with high indegree or outdegree, an algorithm called IEAKBC(short for Integrated Embedding Approach for Knowledge Base Completion) is proposed, in which entities and relations from triplets are first mapped into low-dimensional vector spaces, each original triplet represented in the form of 3-column, k dimensional matrix; then features from different relations are integrated into head and tail entities thus forming fused triplet matrices used as another input channel for convolution. In CNN feature maps are extracted by filters, concatenated and weighted for output scores to discern whether the original triplet holds or not. Experiments show that IEAKBC holds certain advantages over other models; when scaling up to relatively larger datasets, signs of superiority of IEAKBC stand out especially on relations with high cardinalities. At last we apply IEAKBC to a personalized search application, comparing its performance with strong baselines to verify its practicality in real environments.

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Malaviya, Chaitanya, Chandra Bhagavatula, Antoine Bosselut, and Yejin Choi. "Commonsense Knowledge Base Completion with Structural and Semantic Context." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (April 3, 2020): 2925–33. http://dx.doi.org/10.1609/aaai.v34i03.5684.

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Automatic KB completion for commonsense knowledge graphs (e.g., ATOMIC and ConceptNet) poses unique challenges compared to the much studied conventional knowledge bases (e.g., Freebase). Commonsense knowledge graphs use free-form text to represent nodes, resulting in orders of magnitude more nodes compared to conventional KBs ( ∼18x more nodes in ATOMIC compared to Freebase (FB15K-237)). Importantly, this implies significantly sparser graph structures — a major challenge for existing KB completion methods that assume densely connected graphs over a relatively smaller set of nodes.In this paper, we present novel KB completion models that can address these challenges by exploiting the structural and semantic context of nodes. Specifically, we investigate two key ideas: (1) learning from local graph structure, using graph convolutional networks and automatic graph densification and (2) transfer learning from pre-trained language models to knowledge graphs for enhanced contextual representation of knowledge. We describe our method to incorporate information from both these sources in a joint model and provide the first empirical results for KB completion on ATOMIC and evaluation with ranking metrics on ConceptNet. Our results demonstrate the effectiveness of language model representations in boosting link prediction performance and the advantages of learning from local graph structure (+1.5 points in MRR for ConceptNet) when training on subgraphs for computational efficiency. Further analysis on model predictions shines light on the types of commonsense knowledge that language models capture well.

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He, Lirong, Bin Liu, Guangxi Li, Yongpan Sheng, Yafang Wang, and Zenglin Xu. "Knowledge Base Completion by Variational Bayesian Neural Tensor Decomposition." Cognitive Computation 10, no. 6 (June 26, 2018): 1075–84. http://dx.doi.org/10.1007/s12559-018-9565-x.

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Zhao, Yu, Sheng Gao, Patrick Gallinari, and Jun Guo. "Knowledge base completion by learning pairwise-interaction differentiated embeddings." Data Mining and Knowledge Discovery 29, no. 5 (July 19, 2015): 1486–504. http://dx.doi.org/10.1007/s10618-015-0430-1.

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Wang, Hongbin, Shengchen Jiang, and Zhengtao Yu. "Modeling of complex internal logic for knowledge base completion." Applied Intelligence 50, no. 10 (June 5, 2020): 3336–49. http://dx.doi.org/10.1007/s10489-020-01734-z.

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Wang, Yun-Cheng, Xiou Ge, Bin Wang, and C. C. Jay Kuo. "KGBoost: A classification-based knowledge base completion method with negative sampling." Pattern Recognition Letters 157 (May 2022): 104–11. http://dx.doi.org/10.1016/j.patrec.2022.04.001.

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Zhou, Zili, Shaowu Liu, Guandong Xu, and Wu Zhang. "On Completing Sparse Knowledge Base with Transitive Relation Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3125–32. http://dx.doi.org/10.1609/aaai.v33i01.33013125.

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Multi-relation embedding is a popular approach to knowledge base completion that learns embedding representations of entities and relations to compute the plausibility of missing triplet. The effectiveness of embedding approach depends on the sparsity of KB and falls for infrequent entities that only appeared a few times. This paper addresses this issue by proposing a new model exploiting the entity-independent transitive relation patterns, namely Transitive Relation Embedding (TRE). The TRE model alleviates the sparsity problem for predicting on infrequent entities while enjoys the generalisation power of embedding. Experiments on three public datasets against seven baselines showed the merits of TRE in terms of knowledge base completion accuracy as well as computational complexity.

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Bouraoui, Zied, and Steven Schockaert. "Automated Rule Base Completion as Bayesian Concept Induction." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6228–35. http://dx.doi.org/10.1609/aaai.v33i01.33016228.

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Considerable attention has recently been devoted to the problem of automatically extending knowledge bases by applying some form of inductive reasoning. While the vast majority of existing work is centred around so-called knowledge graphs, in this paper we consider a setting where the input consists of a set of (existential) rules. To this end, we exploit a vector space representation of the considered concepts, which is partly induced from the rule base itself and partly from a pre-trained word embedding. Inspired by recent approaches to concept induction, we then model rule templates in this vector space embedding using Gaussian distributions. Unlike many existing approaches, we learn rules by directly exploiting regularities in the given rule base, and do not require that a database with concept and relation instances is given. As a result, our method can be applied to a wide variety of ontologies. We present experimental results that demonstrate the effectiveness of our method.

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Sedghi, Hanie, and Ashish Sabharwal. "Knowledge Completion for Generics using Guided Tensor Factorization." Transactions of the Association for Computational Linguistics 6 (December 2018): 197–210. http://dx.doi.org/10.1162/tacl_a_00015.

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Given a knowledge base or KB containing (noisy) facts about common nouns or generics, such as “all trees produce oxygen” or “some animals live in forests”, we consider the problem of inferring additional such facts at a precision similar to that of the starting KB. Such KBs capture general knowledge about the world, and are crucial for various applications such as question answering. Different from commonly studied named entity KBs such as Freebase, generics KBs involve quantification, have more complex underlying regularities, tend to be more incomplete, and violate the commonly used locally closed world assumption (LCWA). We show that existing KB completion methods struggle with this new task, and present the first approach that is successful. Our results demonstrate that external information, such as relation schemas and entity taxonomies, if used appropriately, can be a surprisingly powerful tool in this setting. First, our simple yet effective knowledge guided tensor factorization approach achieves state-of-the-art results on two generics KBs (80% precise) for science, doubling their size at 74%–86% precision. Second, our novel taxonomy guided, submodular, active learning method for collecting annotations about rare entities (e.g., oriole, a bird) is 6x more effective at inferring further new facts about them than multiple active learning baselines.

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Schouterden, Jonas, Jessa Bekker, Jesse Davis, and Hendrik Blockeel. "Unifying Knowledge Base Completion with PU Learning to Mitigate the Observation Bias." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 2022): 4137–45. http://dx.doi.org/10.1609/aaai.v36i4.20332.

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Methods for Knowledge Base Completion (KBC) reason about a knowledge base (KB) in order to derive new facts that should be included in the KB. This is challenging for two reasons. First, KBs only contain positive examples. This complicates model evaluation which needs both positive and negative examples. Second, those facts that were selected to be included in the knowledge base, are most likely not an i.i.d. sample of the true facts, due to the way knowledge bases are constructed. In this paper, we focus on rule-based approaches, which traditionally address the first challenge by making assumptions that enable identifying negative examples, which in turn makes it possible to compute a rule's confidence or precision. However, they largely ignore the second challenge, which means that their estimates of a rule's confidence can be biased. This paper approaches rule-based KBC through the lens of PU-learning, which can cope with both challenges. We make three contributions.: (1) We provide a unifying view that formalizes the relationship between multiple existing confidences measures based on (i) what assumption they make about and (ii) how their accuracy depends on the selection mechanism. (2) We introduce two new confidence measures that can mitigate known biases by using propensity scores that quantify how likely a fact is to be included the KB. (3) We show through theoretical and empirical analysis that taking the bias into account improves the confidence estimates, even when the propensity scores are not known exactly.

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Shang, Chao, Yun Tang, Jing Huang, Jinbo Bi, Xiaodong He, and Bowen Zhou. "End-to-End Structure-Aware Convolutional Networks for Knowledge Base Completion." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3060–67. http://dx.doi.org/10.1609/aaai.v33i01.33013060.

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Knowledge graph embedding has been an active research topic for knowledge base completion, with progressive improvement from the initial TransE, TransH, DistMult et al to the current state-of-the-art ConvE. ConvE uses 2D convolution over embeddings and multiple layers of nonlinear features to model knowledge graphs. The model can be efficiently trained and scalable to large knowledge graphs. However, there is no structure enforcement in the embedding space of ConvE. The recent graph convolutional network (GCN) provides another way of learning graph node embedding by successfully utilizing graph connectivity structure. In this work, we propose a novel end-to-end StructureAware Convolutional Network (SACN) that takes the benefit of GCN and ConvE together. SACN consists of an encoder of a weighted graph convolutional network (WGCN), and a decoder of a convolutional network called Conv-TransE. WGCN utilizes knowledge graph node structure, node attributes and edge relation types. It has learnable weights that adapt the amount of information from neighbors used in local aggregation, leading to more accurate embeddings of graph nodes. Node attributes in the graph are represented as additional nodes in the WGCN. The decoder Conv-TransE enables the state-of-the-art ConvE to be translational between entities and relations while keeps the same link prediction performance as ConvE. We demonstrate the effectiveness of the proposed SACN on standard FB15k-237 and WN18RR datasets, and it gives about 10% relative improvement over the state-of-theart ConvE in terms of HITS@1, HITS@3 and HITS@10.

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Li, Chen, Xutan Peng, Shanghang Zhang, Hao Peng, Philip S. Yu, Min He, Linfeng Du, and Lihong Wang. "Modeling relation paths for knowledge base completion via joint adversarial training." Knowledge-Based Systems 201-202 (August 2020): 105865. http://dx.doi.org/10.1016/j.knosys.2020.105865.

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Mežnar, Sebastian, Matej Bevec, Nada Lavrač, and Blaž Škrlj. "Ontology Completion with Graph-Based Machine Learning: A Comprehensive Evaluation." Machine Learning and Knowledge Extraction 4, no. 4 (December 1, 2022): 1107–23. http://dx.doi.org/10.3390/make4040056.

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Increasing quantities of semantic resources offer a wealth of human knowledge, but their growth also increases the probability of wrong knowledge base entries. The development of approaches that identify potentially spurious parts of a given knowledge base is therefore highly relevant. We propose an approach for ontology completion that transforms an ontology into a graph and recommends missing edges using structure-only link analysis methods. By systematically evaluating thirteen methods (some for knowledge graphs) on eight different semantic resources, including Gene Ontology, Food Ontology, Marine Ontology, and similar ontologies, we demonstrate that a structure-only link analysis can offer a scalable and computationally efficient ontology completion approach for a subset of analyzed data sets. To the best of our knowledge, this is currently the most extensive systematic study of the applicability of different types of link analysis methods across semantic resources from different domains. It demonstrates that by considering symbolic node embeddings, explanations of the predictions (links) can be obtained, making this branch of methods potentially more valuable than black-box methods.

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Zhang, Xiaolin, and Chao Che. "Drug Repurposing for Parkinson’s Disease by Integrating Knowledge Graph Completion Model and Knowledge Fusion of Medical Literature." Future Internet 13, no. 1 (January 8, 2021): 14. http://dx.doi.org/10.3390/fi13010014.

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The prevalence of Parkinson’s disease increases a tremendous medical and economic burden to society. Therefore, the effective drugs are urgently required. However, the traditional development of effective drugs is costly and risky. Drug repurposing, which identifies new applications for existing drugs, is a feasible strategy for discovering new drugs for Parkinson’s disease. Drug repurposing is based on sufficient medical knowledge. The local medical knowledge base with manually labeled data contains a large number of accurate, but not novel, medical knowledge, while the medical literature containing the latest knowledge is difficult to utilize, because of unstructured data. This paper proposes a framework, named Drug Repurposing for Parkinson’s disease by integrating Knowledge Graph Completion method and Knowledge Fusion of medical literature data (DRKF) in order to make full use of a local medical knowledge base containing accurate knowledge and medical literature with novel knowledge. DRKF first extracts the relations that are related to Parkinson’s disease from medical literature and builds a medical literature knowledge graph. After that, the literature knowledge graph is fused with a local medical knowledge base that integrates several specific medical knowledge sources in order to construct a fused medical knowledge graph. Subsequently, knowledge graph completion methods are leveraged to predict the drug candidates for Parkinson’s disease by using the fused knowledge graph. Finally, we employ classic machine learning methods to repurpose the drug for Parkinson’s disease and compare the results with the method only using the literature-based knowledge graph in order to confirm the effectiveness of knowledge fusion. The experiment results demonstrate that our framework can achieve competitive performance, which confirms the effectiveness of our proposed DRKF for drug repurposing against Parkinson’s disease. It could be a supplement to traditional drug discovery methods.

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Zhang, Xiaolin, and Chao Che. "Drug Repurposing for Parkinson’s Disease by Integrating Knowledge Graph Completion Model and Knowledge Fusion of Medical Literature." Future Internet 13, no. 1 (January 8, 2021): 14. http://dx.doi.org/10.3390/fi13010014.

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The prevalence of Parkinson’s disease increases a tremendous medical and economic burden to society. Therefore, the effective drugs are urgently required. However, the traditional development of effective drugs is costly and risky. Drug repurposing, which identifies new applications for existing drugs, is a feasible strategy for discovering new drugs for Parkinson’s disease. Drug repurposing is based on sufficient medical knowledge. The local medical knowledge base with manually labeled data contains a large number of accurate, but not novel, medical knowledge, while the medical literature containing the latest knowledge is difficult to utilize, because of unstructured data. This paper proposes a framework, named Drug Repurposing for Parkinson’s disease by integrating Knowledge Graph Completion method and Knowledge Fusion of medical literature data (DRKF) in order to make full use of a local medical knowledge base containing accurate knowledge and medical literature with novel knowledge. DRKF first extracts the relations that are related to Parkinson’s disease from medical literature and builds a medical literature knowledge graph. After that, the literature knowledge graph is fused with a local medical knowledge base that integrates several specific medical knowledge sources in order to construct a fused medical knowledge graph. Subsequently, knowledge graph completion methods are leveraged to predict the drug candidates for Parkinson’s disease by using the fused knowledge graph. Finally, we employ classic machine learning methods to repurpose the drug for Parkinson’s disease and compare the results with the method only using the literature-based knowledge graph in order to confirm the effectiveness of knowledge fusion. The experiment results demonstrate that our framework can achieve competitive performance, which confirms the effectiveness of our proposed DRKF for drug repurposing against Parkinson’s disease. It could be a supplement to traditional drug discovery methods.

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Yoshikawa, Masashi, Koji Mineshima, Hiroshi Noji, and Daisuke Bekki. "Combining Axiom Injection and Knowledge Base Completion for Efficient Natural Language Inference." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7410–17. http://dx.doi.org/10.1609/aaai.v33i01.33017410.

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In logic-based approaches to reasoning tasks such as Recognizing Textual Entailment (RTE), it is important for a system to have a large amount of knowledge data. However, there is a tradeoff between adding more knowledge data for improved RTE performance and maintaining an efficient RTE system, as such a big database is problematic in terms of the memory usage and computational complexity. In this work, we show the processing time of a state-of-the-art logic-based RTE system can be significantly reduced by replacing its search-based axiom injection (abduction) mechanism by that based on Knowledge Base Completion (KBC). We integrate this mechanism in a Coq plugin that provides a proof automation tactic for natural language inference. Additionally, we show empirically that adding new knowledge data contributes to better RTE performance while not harming the processing speed in this framework.

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Kouda, Ryoichi, Yutaka Murakami, Tomoji Sanga, and Yoneji Matano. "Completion of normalized infrared images of Japanese land area as knowledge base." Geoinformatics 11, no. 2 (2000): 88–89. http://dx.doi.org/10.6010/geoinformatics1990.11.2_88.

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Peng, Haoliang, and Yue Wu. "A Dynamic Convolutional Network-Based Model for Knowledge Graph Completion." Information 13, no. 3 (March 4, 2022): 133. http://dx.doi.org/10.3390/info13030133.

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Knowledge graph embedding can learn low-rank vector representations for knowledge graph entities and relations, and has been a main research topic for knowledge graph completion. Several recent works suggest that convolutional neural network (CNN)-based models can capture interactions between head and relation embeddings, and hence perform well on knowledge graph completion. However, previous convolutional network models have ignored the different contributions of different interaction features to the experimental results. In this paper, we propose a novel embedding model named DyConvNE for knowledge base completion. Our model DyConvNE uses a dynamic convolution kernel because the dynamic convolutional kernel can assign weights of varying importance to interaction features. We also propose a new method of negative sampling, which mines hard negative samples as additional negative samples for training. We have performed experiments on the data sets WN18RR and FB15k-237, and the results show that our method is better than several other benchmark algorithms for knowledge graph completion. In addition, we used a new test method when predicting the Hits@1 values of WN18RR and FB15k-237, named specific-relationship testing. This method gives about a 2% relative improvement over models that do not use this method in terms of Hits@1.

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Wang, Jianmin, Yukun Xia, Wenbin Zhao, Yuhang Zhang, and Feng Wu. "A Novel Framework for Authority Management Based on Knowledge Base Completion of the Graph Neural Network." Wireless Communications and Mobile Computing 2021 (November 26, 2021): 1–8. http://dx.doi.org/10.1155/2021/1735349.

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Big data is massive and heterogeneous, along with the rapid increase in data quantity, and the diversification of user access, traditional database, and access control methods can no longer meet the requirements of big data storage and flexible access control. To solve this problem, an entity relationship completion and authority management method is proposed. By combining the weighted graph convolutional neural network and the attention mechanism, a knowledge base completion model is given. On this basis, the authority management model is formally defined and the process of multilevel trust access control is designed. The effectiveness of the proposed method is verified by experiments, and the authority management of knowledge base is more fine-grained and more secure.

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Cen, Yu, and Wei Jia Zhou. "Knowledge Interchange in Task-Oriented Architecture: For Space Robot Application." Applied Mechanics and Materials 303-306 (February 2013): 1774–81. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.1774.

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Behaviors of multi-robot system based on task-oriented architecture are intuitional according to the flow of task processing that is obvious to plan and monitor. This paper tables a novel task-oriented architecture for space robot application, which consists of task description, task completion analysis, task compromise. For this architecture, author designed a knowledge interchange mechanism base on KIF (Knowledge Interchange Format) and OKBC (Open Knowledge Base Connectivity). Using this knowledge interchange mechanism, knowledge bases designed by different languages comprehend information transmitted form each other.

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Liu, Weiyu, Angel Daruna, Zsolt Kira, and Sonia Chernova. "Path Ranking with Attention to Type Hierarchies." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (April 3, 2020): 2893–900. http://dx.doi.org/10.1609/aaai.v34i03.5680.

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The objective of the knowledge base completion problem is to infer missing information from existing facts in a knowledge base. Prior work has demonstrated the effectiveness of path-ranking based methods, which solve the problem by discovering observable patterns in knowledge graphs, consisting of nodes representing entities and edges representing relations. However, these patterns either lack accuracy because they rely solely on relations or cannot easily generalize due to the direct use of specific entity information. We introduce Attentive Path Ranking, a novel path pattern representation that leverages type hierarchies of entities to both avoid ambiguity and maintain generalization. Then, we present an end-to-end trained attention-based RNN model to discover the new path patterns from data. Experiments conducted on benchmark knowledge base completion datasets WN18RR and FB15k-237 demonstrate that the proposed model outperforms existing methods on the fact prediction task by statistically significant margins of 26% and 10%, respectively. Furthermore, quantitative and qualitative analyses show that the path patterns balance between generalization and discrimination.

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Sanders-Dewey, Neva E. J., and Stephanie A. Zaleski. "The Utility of a College Major: Do Students of Psychology Learn Discipline-Specific Knowledge?" Journal of General Education 58, no. 1 (January 1, 2009): 19–27. http://dx.doi.org/10.2307/27798119.

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Abstract The research focuses on the assumption that the completion of courses subsumed within a college major results in a heightened knowledge base of information and ultimately increases the likelihood of successful employment within that field. Results support the primary principle underlying the college major model currently espoused by higher education.

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Nguyen, Dai Quoc, Dat Quoc Nguyen, Tu Dinh Nguyen, and Dinh Phung. "A convolutional neural network-based model for knowledge base completion and its application to search personalization." Semantic Web 10, no. 5 (September 26, 2019): 947–60. http://dx.doi.org/10.3233/sw-180318.

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Bing, Lidong, Zhiming Zhang, Wai Lam, and William W. Cohen. "Towards a language-independent solution: Knowledge base completion by searching the Web and deriving language pattern." Knowledge-Based Systems 115 (January 2017): 80–86. http://dx.doi.org/10.1016/j.knosys.2016.10.014.

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Nicola, George, Irina Maria Gheorghiu, Sanziana Scarlatescu, and Paula Perlea. "THE IMPORTANCE OF INTRODUCING MEDICAL LEGISLATION IN THE UNIVERSITY TRAINING PROGRAM FOR STUDENTS." Romanian Journal of Stomatology 67, no. 2 (June 30, 2021): 76–79. http://dx.doi.org/10.37897/rjs.2021.2.2.

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The place of medical legislation in the university curriculum remains somehow uncertain and should be identified more clearly. A more robust knowledge base on the law is needed to enable medical students to develop sufficient legal competence to manage the challenges of future practice. Further research is needed to identify the most effective methods of teaching, learning and assessing legal knowledge and skills, during and after the completion of initial medical education. An in-depth analysis of resources shows that there is no robust evidence base that evaluates the impact in different curricular structures of the efficient methods in developing the knowledge, skills, attitudes, and behaviors needed in medical practice of student. Moreover, only a limited number of studies refer to the results and effectiveness of teaching and learning the elements of medical legislation.

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Rogers, Leland, Igor Barani, Marc Chamberlain, Thomas J. Kaley, Michael McDermott, Jeffrey Raizer, David Schiff, Damien C. Weber, Patrick Y. Wen, and Michael A. Vogelbaum. "Meningiomas: knowledge base, treatment outcomes, and uncertainties. A RANO review." Journal of Neurosurgery 122, no. 1 (January 2015): 4–23. http://dx.doi.org/10.3171/2014.7.jns131644.

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Evolving interest in meningioma, the most common primary brain tumor, has refined contemporary management of these tumors. Problematic, however, is the paucity of prospective clinical trials that provide an evidence-based algorithm for managing meningioma. This review summarizes the published literature regarding the treatment of newly diagnosed and recurrent meningioma, with an emphasis on outcomes stratified by WHO tumor grade. Specifically, this review focuses on patient outcomes following treatment (either adjuvant or at recurrence) with surgery or radiation therapy inclusive of radiosurgery and fractionated radiation therapy. Phase II trials for patients with meningioma have recently completed accrual within the Radiation Therapy Oncology Group and the European Organisation for Research and Treatment of Cancer consortia, and Phase III studies are being developed. However, at present, there are no completed prospective, randomized trials assessing the role of either surgery or radiation therapy. Successful completion of future studies will require a multidisciplinary effort, dissemination of the current knowledge base, improved implementation of WHO grading criteria, standardization of response criteria and other outcome end points, and concerted efforts to address weaknesses in present treatment paradigms, particularly for patients with progressive or recurrent low-grade meningioma or with high-grade meningioma. In parallel efforts, Response Assessment in Neuro-Oncology (RANO) subcommittees are developing a paper on systemic therapies for meningioma and a separate article proposing standardized end point and response criteria for meningioma.

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Marra, Giuseppe. "Bridging symbolic and subsymbolic reasoning with minimax entropy models." Intelligenza Artificiale 15, no. 2 (February 4, 2022): 71–90. http://dx.doi.org/10.3233/ia-210088.

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In this paper, we investigate MiniMax Entropy models, a class of neural symbolic models where symbolic and subsymbolic features are seamlessly integrated. We show how these models recover classical algorithms from both the deep learning and statistical relational learning scenarios. Novel hybrid settings are defined and experimentally explored, showing state-of-the-art performance in collective classification, knowledge base completion and graph (molecular) data generation.

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Garcia-Duran, Alberto, Antoine Bordes, Nicolas Usunier, and Yves Grandvalet. "Combining Two and Three-Way Embedding Models for Link Prediction in Knowledge Bases." Journal of Artificial Intelligence Research 55 (March 28, 2016): 715–42. http://dx.doi.org/10.1613/jair.5013.

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This paper tackles the problem of endogenous link prediction for knowledge base completion. Knowledge bases can be represented as directed graphs whose nodes correspond to entities and edges to relationships. Previous attempts either consist of powerful systems with high capacity to model complex connectivity patterns, which unfortunately usually end up overfitting on rare relationships, or in approaches that trade capacity for simplicity in order to fairly model all relationships, frequent or not. In this paper, we propose Tatec, a happy medium obtained by complementing a high-capacity model with a simpler one, both pre-trained separately and then combined. We present several variants of this model with different kinds of regularization and combination strategies and show that this approach outperforms existing methods on different types of relationships by achieving state-of-the-art results on four benchmarks of the literature.

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Gair, Susan. "Pursing Indigenous-Inclusive Curriculum in Social Work Tertiary Education: Feeling my Way as a Non-Indigenous Educator." Australian Journal of Indigenous Education 36, no. 1 (2007): 49–55. http://dx.doi.org/10.1017/s1326011100004415.

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AbstractThe retention and completion rates of Indigenous students undertaking tertiary studies continue to be disappointing. The contribution of Eurocentric curricula to such an outcome has been proposed in the Australian and international literature. Remaining very conscious of my status as a white, female, social work educator teaching at a regional university, over the last six years I have attempted to pursue the development of a more Indigenous-inclusive curricula and thus contribute to increasing Indigenous graduates from our degree programmes. This article documents some of my actions to rectify gaps in my own non-Indigenous knowledge base as a reflective learner under Indigenous supervision within the academy and in the community. Action to advance the development of accurate, useful curriculum and teaching practices respectful of Indigenous knowledges is recommended.

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Brzychczy, Edyta, Marek Kęsek, Aneta Napieraj, and Roman Magda. "An expert system for underground coal mine planning." Gospodarka Surowcami Mineralnymi 33, no. 2 (June 27, 2017): 113–27. http://dx.doi.org/10.1515/gospo-2017-0015.

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Abstract In the current market situation, mining companies are faced with the necessity to take actions to improve the efficiency of the mining process. Some of these actions enforce a centralization of activities in the field of deposit economy and planning of mining operations in these companies. In the planning process with such scope the large knowledge of designers is required, which could be additionally supported by a knowledge base, supplied by information and data obtained during the completion of mining works, which also allows for use of the expert knowledge of other organizational units of the mine or the company. The paper presents an original expert system for mining works planning in the underground hard coal mines (MinePlanEx). The aim of the developed system is to support the designers of production planning in hard coal mines within the scope of: equipment selection, mining machinery combining into equipment sets and determining characteristic curves regarding the production results in the planned excavations. Knowledge of the system is represented by the rules selected with the chosen data mining techniques (association rules and classification trees) and obtained from experts. The first part of the paper presents a knowledge base, knowledge acquisition module and inference module which are the main components of the system. The second part contains an example of system operation.

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Dash, Sarthak, Michael R. Glass, Alfio Gliozzo, Mustafa Canim, and Gaetano Rossiello. "Populating Web-Scale Knowledge Graphs Using Distantly Supervised Relation Extraction and Validation." Information 12, no. 8 (August 6, 2021): 316. http://dx.doi.org/10.3390/info12080316.

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In this paper, we propose a fully automated system to extend knowledge graphs using external information from web-scale corpora. The designed system leverages a deep-learning-based technology for relation extraction that can be trained by a distantly supervised approach. In addition, the system uses a deep learning approach for knowledge base completion by utilizing the global structure information of the induced KG to further refine the confidence of the newly discovered relations. The designed system does not require any effort for adaptation to new languages and domains as it does not use any hand-labeled data, NLP analytics, and inference rules. Our experiments, performed on a popular academic benchmark, demonstrate that the suggested system boosts the performance of relation extraction by a wide margin, reporting error reductions of 50%, resulting in relative improvement of up to 100%. Furthermore, a web-scale experiment conducted to extend DBPedia with knowledge from Common Crawl shows that our system is not only scalable but also does not require any adaptation cost, while yielding a substantial accuracy gain.

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Kanj, M., M. Zaman, and J. C. Roegiers. "Sanding: An Expert-System Approach for Assessment and Control in Wells." Journal of Energy Resources Technology 119, no. 4 (December 1, 1997): 223–35. http://dx.doi.org/10.1115/1.2794994.

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The sand production problem is plaguing the petroleum industry by its adverse effects on thousands of oil and gas fields throughout the world. A tremendous amount of money is spent each year on attempts to predict and control sand influx and/or repair wells and equipment damaged by sand. Sand inflow into the well during production leads to casing abrasion and failure, formation damage and distortion, and frequent sand removal and cleaning. The sand control process has a major influence on the type of the well completion design and it influences the guidelines for the completion process. In addition, many wells are currently being produced below their potential in order to restrict sand influx or erosion, and/or as a result of poorly designed or installed sand control methods. Evidently, the sand prediction and control problem is exceedingly complex and suggests the use of heuristics and the appropriateness of the expert systems technology. An automated sand control consultant and expert system has been developed. The system is aimed at assisting users in predicting sand occurrence during production and in selecting and designing the proper sand-exclusion treatments. The knowledge base of the system is based on an easy upgrade, easy expand format and involves four primary modules, thus giving the end-user greater flexibility to tentatively access and evaluate different scenarios of knowledge processing. Input data can range from “not known” formation characteristics and/or well stimulation requirements, for which the system gives conservative recommendations based on the remaining known facts, completion characteristics of the hole, well history, and geological probability; to cases with detailed information available, in which case very elaborate and precise recommendations are prescribed. This paper describes the knowledge involved in various modules of the sanding system, as well as future plans and further developments.

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Abou-Hanna, Jacob J., Jonah E. Yousif, Ariane D. Kaplan, David C. Musch, and Jonathan D. Trobe. "Medical Student Ophthalmic Knowledge Proficiency after Completing a Clinical Elective or an Online Course." Journal of Academic Ophthalmology 12, no. 02 (July 2020): e255-e266. http://dx.doi.org/10.1055/s-0040-1721069.

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Abstract Background As more information is being packed into medical school curricula, mainstream medical topics legitimately receive more attention than specialty topics such as ophthalmology. However, general practitioners, as gatekeepers of specialty care, must attain competency in ophthalmology. We have investigated whether an online ophthalmology course alone would be noninferior to the same online course plus an in-person clinical elective in providing ophthalmic knowledge. Methods Students at the University of Michigan Medical School voluntarily enrolled in one of two groups: an Online Only group requiring satisfactory completion of an online course entitled “The Eyes Have It” (TEHI) or a Clinical + Online group requiring students to complete a 2-week clinical rotation and the TEHI online course. The outcome metric was the score on an independent 50-question written examination of ophthalmic knowledge. Students also completed a survey assessing confidence in managing ophthalmic problems. Results Twenty students in the Clinical + Online group and 59 students in the Online Only group completed the study. The Clinical + Online group slightly outscored the Online Only group (86.3 vs. 83.0%, p = 0.004). When the two outlier questions were removed from the analysis, there was no difference in mean scores between the two groups (85.8 vs. 85.4, p = 0.069). Students in the Clinical + Online group devoted 80 more hours to the experience than did the students in the Online Only group. The number of hours devoted to the course and interest in ophthalmology were weakly correlated with examination performance. After completion of the experiment, there was no difference in student-reported comfort in dealing with ophthalmic problems between the two groups. Conclusion The examination scores of the students who completed the in-person alone were only slightly inferior to those of the students who completed the in-person clinical elective and the online course. These results suggest that an online course alone may provide a satisfactory ophthalmic knowledge base in a more compact timeframe, an alternative that should have appeal to students who do not intend to pursue a career in ophthalmology.

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Mendoza, Marcelo, and Naim Bro. "Predicting affinity ties in a surname network." PLOS ONE 16, no. 9 (September 2, 2021): e0256603. http://dx.doi.org/10.1371/journal.pone.0256603.

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From administrative registers of last names in Santiago, Chile, we create a surname affinity network that encodes socioeconomic data. This network is a multi-relational graph with nodes representing surnames and edges representing the prevalence of interactions between surnames by socioeconomic decile. We model the prediction of links as a knowledge base completion problem, and find that sharing neighbors is highly predictive of the formation of new links. Importantly, We distinguish between grounded neighbors and neighbors in the embedding space, and find that the latter is more predictive of tie formation. The paper discusses the implications of this finding in explaining the high levels of elite endogamy in Santiago.

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Zhou, Hanqing, Amal Zouaq, and Diana Inkpen. "A Comparison of Word Embeddings and N-gram Models for DBpedia Type and Invalid Entity Detection." Information 10, no. 1 (December 25, 2018): 6. http://dx.doi.org/10.3390/info10010006.

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This article presents and evaluates a method for the detection of DBpedia types and entities that can be used for knowledge base completion and maintenance. This method compares entity embeddings with traditional N-gram models coupled with clustering and classification. We tackle two challenges: (a) the detection of entity types, which can be used to detect invalid DBpedia types and assign DBpedia types for type-less entities; and (b) the detection of invalid entities in the resource description of a DBpedia entity. Our results show that entity embeddings outperform n-gram models for type and entity detection and can contribute to the improvement of DBpedia’s quality, maintenance, and evolution.

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McKennis, Jeffrey S., Nam Sook-Bae, Enrico J. Termine, Ken Shimamoto, and Mitsuo Kimura. "Misconceptions Regarding the Chemical Role of Completion/Packer Fluids in Annular Environmentally Assisted Cracking of Martensitic Stainless Steel Tubing." SPE Journal 15, no. 04 (June 22, 2010): 1098–103. http://dx.doi.org/10.2118/121433-pa.

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Summary The increased use of corrosion resistant alloys (CRAs) in deep high-pressure/high-temperature (HP/HT) wells has led to production-tubing cracking failures throughout the industry. Many of these failures have occurred from the outside (annulus side) and have been attributed to environmentally assisted cracking (EAC) and, hence, are best described as annular EAC (AEAC). Examination of these failures points to a serious incompatibility of the production tubing metallurgy with the packer fluid under stress. In 2003, combining expertise in fluid chemistry and metallurgy, the authors formed a research alliance to address the AEAC problem by examining the compatibility of a wide spectrum of completion fluids with various martensitic stainless steel (MSS) metallurgies. This unique research collaboration, involving extensive stress-cracking testing performed with different metallurgies and different fluids under simulated well conditions, has resulted in an extensive database (more than 4,000 test entries for 27 fluids and six MSS metallurgies) and a new body of knowledge regarding the causes behind AEAC failures. Conventional wisdom holds that chloride ion and oxygen play major causative roles. The authors’ findings, however, identify other contaminants in completion/packer fluids that play the dominant role in the chemical mechanism of the crack failures. Such contaminants include sulfur-containing species, oxidants other than oxygen, and select basic ionic species. This paper addresses the effect of the new information and identifies serious misconceptions regarding the role of completion /packer fluids in the tubular failures. The authors’ comprehensive study has advanced the industry's knowledge of the causes of AEAC by detailing the previously unrecognized importance of various contaminants present in the fluids. As a consequence, the need for quality assurance and best- practice fluid management throughout the life cycle of the fluids is now recognized. Misconceptions with respect to the chemical mechanisms and causative factors of AEAC failures are discussed. Such information should expand the industry's AEAC knowledge base and minimize the risk of tubular failure.

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Kothari, Monica, Dionne Mackison, Carolyn Hemminger, Sandrine Fimbi, Denise Lionetti, Abigail Perry, Katja Siling, and Blene Hailu. "Nutrition Embedding Evaluation Programme: An Evaluation Technical Assistance Model for Supporting Civil Society Organizations to Conduct Quality Nutrition Impact Evaluations." Evaluation Review 43, no. 6 (December 2019): 396–425. http://dx.doi.org/10.1177/0193841x19898939.

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The Nutrition Embedding Evaluation Programme (NEEP) was a global 4-year program (2013–2017) funded by the United Kingdom Department for International Development created to respond to gaps in the nutrition evidence base. The NEEP implementing agency—PATH—provided grants and evaluation technical assistance (ETA) to civil society organizations (CSOs) from 12 countries to conduct robust nutrition-related impact evaluations. The programmatic approach of having an intermediary agent to manage the funding and ETA mechanisms for nutrition impact evaluations is rare and therefore provides a unique opportunity to understand its effectiveness. Over the program duration, NEEP collected lessons learned that were analyzed and disaggregated into key themes considered critical for the completion of high-quality impact evaluations. From these lessons learned, NEEP provides an ETA program model that can be replicated or adapted to other international development sectors. This model highlights the key role of the three tiers (donor, ETA manager, and CSOs) in ensuring the best value for money and effective technical support for conducting impact evaluations and fostering the importance of knowledge uptake and evaluative culture for maximum knowledge diffusion. In this way, global research can be targeted to approaches that provide options to collaborate with the program implementers and contribute to a holistic evidence base to inform policy and programmatic decisions.

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Luo, Liangchen, Wenhao Huang, Qi Zeng, Zaiqing Nie, and Xu Sun. "Learning Personalized End-to-End Goal-Oriented Dialog." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6794–801. http://dx.doi.org/10.1609/aaai.v33i01.33016794.

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Most existing works on dialog systems only consider conversation content while neglecting the personality of the user the bot is interacting with, which begets several unsolved issues. In this paper, we present a personalized end-to-end model in an attempt to leverage personalization in goal-oriented dialogs. We first introduce a PROFILE MODEL which encodes user profiles into distributed embeddings and refers to conversation history from other similar users. Then a PREFERENCE MODEL captures user preferences over knowledge base entities to handle the ambiguity in user requests. The two models are combined into the PERSONALIZED MEMN2N. Experiments show that the proposed model achieves qualitative performance improvements over state-of-the-art methods. As for human evaluation, it also outperforms other approaches in terms of task completion rate and user satisfaction.

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Brown, Courtney, Lauren Cline, and J. Robinson. "Transformative Learning in an African American Agriculture Course." Journal of Agricultural Education 63, no. 1 (April 1, 2022): 62–79. http://dx.doi.org/10.5032/jae.2022.01062.

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Despite agricultural education’s prioritized efforts to increase diversity, people of color remain minimally represented. The overwhelming majority of all School-Based Agricultural Education (SBAE) teachers are White, non-Hispanic. The limitations of SBAE teachers’ prior experiences or knowledge base of ethnic and racial diversity could lead to challenges in successfully supporting minority students. Agricultural education programs play an essential role in supporting preservice teachers’ attainment of deeper understanding by providing multicultural education curriculum that encourages growth in their critical awareness of diverse cultures. The purpose of our study was to evaluate the transformative learning experience of students completing an African American (AA) Agriculture course. This study analyzed responses provided by students both at the beginning and end of the course experience to understand if and to what degree the transformative learning process occurred. Findings revealed evidence of transformative learning among the students in the way of six emergent themes that described their perceptions, attitudes, and beliefs about AA agriculture when comparing responses acquired before and after the completion of the course. With the limited representation of AA teachers in SBAE programs, the evaluation of the transformative learning process of students in this course may open the door to create a more culturally inclusive environment in SBAE and the agricultural industry as a whole.

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Silva Restrepo, Marcos, Michel A. Boivin, Pierre Kory, Pralay K. Sarkar, Gisela I. Banauch, Stephen Halpern, and Paul H. Mayo. "Effectiveness of a Transesophageal Echocardiography Course." Journal of Intensive Care Medicine 35, no. 11 (March 13, 2019): 1148–52. http://dx.doi.org/10.1177/0885066619836665.

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Background: Transesophageal echocardiography has important applications for the management of the critically ill patient. There is a need to develop effective training programs for the critical care community in acquiring skill at critical care transesophageal echocardiography. Objective: We studied the effectiveness of a 1-day simulation-based course that focused on the acquisition of skill in the performance of critical care transesophageal echocardiography. Methods: Learners received training in image acquisition with a transesophageal simulator and training in image interpretation in small group sessions. Skill at image acquisition and image interpretation was assessed at the beginning and at the completion of the course. Results: There were 27 learners who attended the course. Pre and post knowledge scores were 55 (19; mean [SD]) and 88 (9; P < .0005), respectively. Pre and post image acquisition scores were 3.6 (3.7) and 9.9 (0.3; P < .0001), respectively. Conclusions: A 1-day course in critical care transesophageal echocardiography that combined case-based image interpretation with image acquisition training using a simulator improved technical skills and knowledge base.

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Chenok, Katherine Eresian, Feifei Ye, Kristen K. McNiff Landrum, Emma Hoo, Valerie Kong, Jennifer J. Griggs, and Rachel Brodie. "Development of PRO-PMs assessing symptoms following completion of curative-intent chemotherapy." Journal of Clinical Oncology 39, no. 28_suppl (October 1, 2021): 171. http://dx.doi.org/10.1200/jco.2020.39.28_suppl.171.

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171 Background: Few patient-reported outcome performance measures (PRO-PMs) have been validated for the cancer population. The testing that has occurred mostly focuses on advanced cancers despite the fact that the majority of people with cancer are diagnosed with earlier stage disease. We developed and tested PRO-PMs to assess quality of life, pain and fatigue in adult patients completing curative-intent chemotherapy for breast, colon and non-small cell lung cancers. Our goal is to develop measures that target symptoms that impact entry into the survivorship phase. Methods: We recruited 20 diverse test sites from the Michigan Oncology Quality Collaborative (MOQC) and the Alliance of Dedicated Cancer Centers (ADCC). Test sites enrolled patients, administered surveys, and collected clinical and demographic data. A Technical Expert Panel and the Patient and Caregiver Council selected PROMs and provided testing guidance. We assessed data collection feasibility and clinician/staff/patient burden throughout the testing process. Results: PROMIS instruments were selected due to psychometric testing in the target population, public availability and acceptability to patients and test sites. 1,753 patients were enrolled between 10/1/19 – 3/31/21. The COVID public health emergency disrupted testing and resulted in lower than expected enrolled patients/completed surveys; however, adaptations led to expansions in survey administration methods. Preliminary practice-level performance results from 10 sites show variation across sites for pain interference (mean = 50.5, SD = 2.8, with a range of 44.6—54.6) and fatigue (mean 49.2, SD = 2.8, with a range of 44.6--54.3). Some test sites reported PRO implementation to be burdensome; however, most patients evaluated did not find survey completion to be burdensome. Conclusions: Next steps include testing risk adjustment variables/model, creating adjusted performance scores, reliability and validity testing. Despite the consensus goal of PRO-PMs in oncology, barriers to implementation persist and important methodologic barriers exist (e.g., ability to achieve sufficient sample size in an oncology practice; defining the most appropriate numerator calculation that reports the desired quality concept and is appropriate for the PROM being used; analytic best practices for PRO-PM adjustment/testing). This project is contributing to the knowledge base as we seek to ensure that PRO-PMs provide meaningful, actionable, patient-centered quality data with benefits that outweigh the burden of implementation.

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Thomas, Gary S., Richard W. Obermayer, William B. Raspotnik, and Wayne L. Waag. "Modeling Pilot Expertise in Air Combat." Proceedings of the Human Factors Society Annual Meeting 36, no. 17 (October 1992): 1331–34. http://dx.doi.org/10.1518/107118192786749379.

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The purpose of this effort was to model expert pilot performance and decision making in one-versus-one (1v1) air-to-air combat. Several knowledge-elicitation techniques were used to extract air combat expertise from a former fighter pilot, who served as the subject-matter-expert (SME). Unstructured and then structured interviews were used to elicit the goals and sub-goals of air-to-air combat, plus some of the pilot behaviors necessary to accomplish the goals. The SME also flew a number of combat sorties against another former fighter pilot in the Simulator for Air-to-Air Combat (SAAC) to demonstrate pilot performance required to accomplish the goals of air combat. Based on the SME's verbal protocols, a group of air combat rules were developed. A rule-based production system was then designed to incorporate the resulting knowledge base. The production system was also designed to be capable of analyzing an existing data base of air combat engagements. Expert system development required additional input from the SME to identify specific values of flight parameters required by the production system. Upon completion and SME verification of the expert model, it will be validated by comparing its performance to that of our SME in simulated air-to-air combat. If the model can successfully describe expert pilot performance, the model will be used to provide diagnostic performance feedback in conjunction with SAAC training.

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Merino, Daniela Fernanda Guano, Marcelo Eduardo Allauca Peñafiel, Enrique Jesús Guambo Yerovi, and Luis Alberto Veloz Andrade. "El Aprendizaje Significativo Como Estrategia De Estimulación De La Escritura Del Idioma Inglés En Educación General Básica." European Scientific Journal, ESJ 13, no. 19 (July 31, 2017): 128. http://dx.doi.org/10.19044/esj.2017.v13n19p128.

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The objective of the present investigation was the analysis of the use of strategies of significant learning in order to stimulate writing in the English language, which affect the development of the writing skill causing low performance and deficient level of knowledge in the students of sixth and seventh years of general basic education. The bibliographic information on significant learning sustains the importance of the use of didactic material in the processes of assimilation and interaction when acquiring educational skills and competences. They facilitate the connection to preexisting mental schematics in order to incorporate knowledge that generates significant learning. The methods used were deductive-inductive, documentary and applied bibliography, which allowed the collection of information through observation sheets. The results showed that the cognitive processes are inadequate due to lack of didactic material to develop and stimulate the writing of the English language. Due to this worksheets were designed and applied that contain grammatical structure, vocabulary, completion exercises, active memorization, spelling, word classification, sentence formation, short sentences and long with positive and negative answers; use of demonstrative adjectives, possessive pronouns, classification of nouns; all this to stimulate writing and a base of knowledge in the learning of English as a second language.

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Cooper-Stanton, Garry. "Compression therapy and heart failure: a scoping review of the existing evidence." British Journal of Community Nursing 27, no. 3 (March 2, 2022): 128–34. http://dx.doi.org/10.12968/bjcn.2022.27.3.128.

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The completion of a scoping review within the area of compression therapy and heart failure offers an insight into the present literature in this area, alongside offering the ability to connect this existing knowledge to chronic oedema/lymphoedema when both conditions co-exist. The evidence obtained included national agreed guidelines, consensus documents and existing primary/secondary research. The review identified existing evidence that suggests that the application of compression therapy in those with heart failure may be appropriate, but is dependent upon staging and stability. However, this needs to be contextualised against other co-morbidities, such as lymphoedema, which may impact upon the exact compression therapy and level applied. Further research within the area of heart failure in combination with chronic oedema/lymphoedema would expand the existing evidence base. This is set against a need for further consensus guidance to bridge the gap that exists within the literature.

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Journal articles on the topic 'Knowledge Base Completion'

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