Journal articles on the topic '170203 Knowledge Representation and Machine Learning'
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Twine, S. "Knowledge representation and organization in machine learning." Information and Software Technology 32, no. 7 (September 1990): 510–11. http://dx.doi.org/10.1016/0950-5849(90)90171-m.
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Maher, Mary Lou, and Heng Li. "Learning design concepts using machine learning techniques." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 8, no. 2 (1994): 95–111. http://dx.doi.org/10.1017/s0890060400000706.
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AbstractThe use of machine learning techniques requires the formulation of a learning problem in a particular domain. The application of machine learning techniques in a design domain requires the consideration of the representation of the learned design knowledge, that is, a target representation, as well as the content and form of the training data, or design examples. This paper examines the use of a target representation of design concepts and the application, adaptation, or generation of machine learning techniques to generate design concepts from design examples. The examples are taken from the domain of bridge design. The primary machine learning paradigm considered is concept formation.
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Ma, Yunpu, and Volker Tresp. "Quantum Machine Learning Algorithm for Knowledge Graphs." ACM Transactions on Quantum Computing 2, no. 3 (September 30, 2021): 1–28. http://dx.doi.org/10.1145/3467982.
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Semantic knowledge graphs are large-scale triple-oriented databases for knowledge representation and reasoning. Implicit knowledge can be inferred by modeling the tensor representations generated from knowledge graphs. However, as the sizes of knowledge graphs continue to grow, classical modeling becomes increasingly computationally resource intensive. This article investigates how to capitalize on quantum resources to accelerate the modeling of knowledge graphs. In particular, we propose the first quantum machine learning algorithm for inference on tensorized data, i.e., on knowledge graphs. Since most tensor problems are NP-hard [18], it is challenging to devise quantum algorithms to support the inference task. We simplify the modeling task by making the plausible assumption that the tensor representation of a knowledge graph can be approximated by its low-rank tensor singular value decomposition, which is verified by our experiments. The proposed sampling-based quantum algorithm achieves speedup with a polylogarithmic runtime in the dimension of knowledge graph tensor.
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Maher, Mary Lou, David C. Brown, and Alex Duffy. "Special Issue: Machine Learning in Design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 8, no. 2 (1994): 81–82. http://dx.doi.org/10.1017/s0890060400000688.
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The linking of research in machine learning with research in knowledge-based design is such that each of the two areas benefit from the consideration of the other. The use of machine learning in design addresses the perceived need to support the capture and representation of design knowledge, because handcrafting a representation is a difficult and time-consuming task. In addition, design provides a task with which to investigate the usefulness of existing machine learning techniques, and, perhaps, to discover new ones.
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Littman, David, and Maarten van Someren. "International Workshop on Knowledge Representation and Organization in Machine Learning." AI Communications 1, no. 1 (1988): 44–45. http://dx.doi.org/10.3233/aic-1988-1108.
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Robinson, Peter N., and Melissa A. Haendel. "Ontologies, Knowledge Representation, and Machine Learning for Translational Research: Recent Contributions." Yearbook of Medical Informatics 29, no. 01 (August 2020): 159–62. http://dx.doi.org/10.1055/s-0040-1701991.
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Objectives: To select, present, and summarize the most relevant papers published in 2018 and 2019 in the field of Ontologies and Knowledge Representation, with a particular focus on the intersection between Ontologies and Machine Learning. Methods: A comprehensive review of the medical informatics literature was performed to select the most interesting papers published in 2018 and 2019 and that document the utility of ontologies for computational analysis, including machine learning. Results: Fifteen articles were selected for inclusion in this survey paper. The chosen articles belong to three major themes: (i) the identification of phenotypic abnormalities in electronic health record (EHR) data using the Human Phenotype Ontology ; (ii) word and node embedding algorithms to supplement natural language processing (NLP) of EHRs and other medical texts; and (iii) hybrid ontology and NLP-based approaches to extracting structured and unstructured components of EHRs. Conclusion: Unprecedented amounts of clinically relevant data are now available for clinical and research use. Machine learning is increasingly being applied to these data sources for predictive analytics, precision medicine, and differential diagnosis. Ontologies have become an essential component of software pipelines designed to extract, code, and analyze clinical information by machine learning algorithms. The intersection of machine learning and semantics is proving to be an innovative space in clinical research.
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Moreno, Marcio, Vítor Lourenço, Sandro Rama Fiorini, Polyana Costa, Rafael Brandão, Daniel Civitarese, and Renato Cerqueira. "Managing Machine Learning Workflow Components." International Journal of Semantic Computing 14, no. 02 (June 2020): 295–309. http://dx.doi.org/10.1142/s1793351x20400115.
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Machine Learning Workflows (MLWfs) have become an essential and disruptive approach in problem-solving over several industries. However, the development process of MLWfs may be complex, time-consuming, and error-prone. To handle this problem, we introduce machine learning workflow management (MLWfM) as a technique to aid the development and reuse of MLWfs and their components through three aspects: representation, execution, and creation. We introduce our approach to structure MLWfs’ components and metadata in order to aid component retrieval and reuse of new MLWfs. We also consider the execution of these components within a tool. A hybrid knowledge representation, called Hyperknowledge, frames our methodology, supporting the three MLWfM’s aspects. To validate our approach, we show a practical use case in the Oil & Gas industry. In addition, to evaluate the feasibility of the proposed technique, we create a dataset of MLWfs executions and discuss the MLWfM’s performance in loading and querying this dataset.
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Ullah, AMM Sharif. "Fundamental Issues of Concept Mapping Relevant to Discipline-Based Education: A Perspective of Manufacturing Engineering." Education Sciences 9, no. 3 (August 29, 2019): 228. http://dx.doi.org/10.3390/educsci9030228.
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This article addresses some fundamental issues of concept mapping relevant to discipline-based education. The focus is on manufacturing knowledge representation from the viewpoints of both human and machine learning. The concept of new-generation manufacturing (Industry 4.0, smart manufacturing, and connected factory) necessitates learning factory (human learning) and human-cyber-physical systems (machine learning). Both learning factory and human-cyber-physical systems require semantic web-embedded dynamic knowledge bases, which are subjected to syntax (machine-to-machine communication), semantics (the meaning of the contents), and pragmatics (the preferences of individuals involved). This article argues that knowledge-aware concept mapping is a solution to create and analyze the semantic web-embedded dynamic knowledge bases for both human and machine learning. Accordingly, this article defines five types of knowledge, namely, analytic a priori knowledge, synthetic a priori knowledge, synthetic a posteriori knowledge, meaningful knowledge, and skeptic knowledge. These types of knowledge help find some rules and guidelines to create and analyze concept maps for the purposes human and machine learning. The presence of these types of knowledge is elucidated using a real-life manufacturing knowledge representation case. Their implications in learning manufacturing knowledge are also described. The outcomes of this article help install knowledge-aware concept maps for discipline-based education.
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GRANER, NICOLAS, SUNIL SHARMA, D. SLEEMAN, MICHALIS RISSAKIS, SUSAN CRAW, and CHRIS MOORE. "THE MACHINE LEARNING TOOLBOX CONSULTANT." International Journal on Artificial Intelligence Tools 02, no. 03 (September 1993): 307–28. http://dx.doi.org/10.1142/s0218213093000163.
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The Machine Learning Toolbox contains a set of ten Machine Learning algorithms, integrated with a common interface and common knowledge representation language. An essential component of the Toolbox is the Consultant, a knowledge-based system that advises novice users about which algorithm they could use for a particular application. We show how the Consultant’s architecture evolved, through its successive implementations, from a rigid rule-based expert system to a flexible information browsing system supporting user experimentation. In particular, we show how a task description can be elicited from the user in three different modes and exploited by several functions to provide advice and explanations at various levels of detail. The system’s output also increased in sophistication: initially limited to the recommendation of a suitable algorithm, it now includes detailed information about the algorithm and its usage, and will be extended to help the user interpret and improve the results of learning.
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Kocabas, S. "A review of learning." Knowledge Engineering Review 6, no. 3 (September 1991): 195–222. http://dx.doi.org/10.1017/s0269888900005804.
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AbstractLearning is one of the important research fields in artificial intelligence. This paper begins with an outline of the definitions of learning and intelligence, followed by a discussion of the aims of machine learning as an emerging science, and an historical outline of machine learning. The paper then examines the elements and various classifications of learning, and then introduces a new classification of learning based on the levels of representation and learning as knowledge-, symboland device-level learning. Similarity- and explanation-based generalization and conceptual clustering are described as knowledge level learning methods. Learning in classifiers, genetic algorithms and classifier systems are described as symbol level learning, and neural networks are described as device level systems. In accordance with this classification, methods of learning are described in terms of inputs, learning algorithms or devices, and outputs. Then there follows a discussion on the relationships between knowledge representation and learning, and a discussion on the limits of learning in knowledge systems. The paper concludes with a summary of the results drawn from this review.
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Sun, Xiuming, Weina Wu, Peng Geng, and Lin Lu. "International Journal of Applied Mathematics and Machine Learning." International Journal of Applied Mathematics and Machine Learning 15, no. 1 (September 10, 2021): 1–29. http://dx.doi.org/10.18642/ijamml_7100122215.
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In order to achieve the multi-focus image fusion task, a sparse representation method based on quaternion for multi-focus image fusion is proposed in this paper. Firstly, the RGB color information of each pixel in the color image is represented by quaternion based on the relevant knowledge of computational mathematics, and the color image pixel is processed as a whole vector to maintain the relevant information between the three color channels. Secondly, the dictionary represented by quaternion and the sparse coefficient represented by quaternion are obtained by using the our proposed sparse representation model. Thirdly, the coefficient fusion is carried out by using the “max-L1” rule. Finally, the fused sparse coefficient and dictionary are used for image reconstruction to obtain the quaternion fused image, which is then converted into RGB color multi-focus fused image. Our method belongs to computational mathematics, and uses the relevant knowledge in the field of computational mathematics to help us carry out the experiment. The experimental results show that the method has achieved good results in visual quality and objective evaluation.
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Zamansky, Anna, Aleksandr Sinitca, Dirk van der Linden, and Dmitry Kaplun. "Automatic Animal Behavior Analysis: Opportunities for Combining Knowledge Representation with Machine Learning." Procedia Computer Science 186 (2021): 661–68. http://dx.doi.org/10.1016/j.procs.2021.04.187.
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Cox, Geoff. "Ways of Machine Seeing." A Peer-Reviewed Journal About 6, no. 1 (April 1, 2017): 8–15. http://dx.doi.org/10.7146/aprja.v6i1.116007.
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Algorithms do not act alone or with magical (totalising) power but exist as part of larger infrastructures and ideologies. Some well-publicised recent cases have come to public attention that exemplify a contemporary politics (and crisis) of representation in this way. The problem is one of learning in its widest sense, and “machine learning” techniques are employed on data to produce forms of knowledge that are inextricably bound to hegemonic systems of power and prejudice.
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Dhombres, Ferdinand, and Jean Charlet. "Knowledge Representation and Management: Interest in New Solutions for Ontology Curation." Yearbook of Medical Informatics 30, no. 01 (August 2021): 185–90. http://dx.doi.org/10.1055/s-0041-1726508.
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Summary Objective: To select, present and summarize some of the best papers in the field of Knowledge Representation and Management (KRM) published in 2020. Methods: A comprehensive and standardized review of the medical informatics literature was performed to select the most interesting papers of KRM published in 2020, based on PubMed queries. This review was conducted according to the IMIA Yearbook guidelines. Results: Four best papers were selected among 1,175 publications. In contrast with the papers selected last year, the four best papers of 2020 demonstrated a significant focus on methods and tools for ontology curation and design. The usual KRM application domains (bioinformatics, machine learning, and electronic health records) were also represented. Conclusion: In 2020, ontology curation emerges as a significant topic of research interest. Bioinformatics, machine learning, and electronics health records remain significant research areas in the KRM community with various applications. Knowledge representations are key to advance machine learning by providing context and to develop novel bioinformatics metrics. As in 2019, representations serve a great variety of applications across many medical domains, with actionable results and now with growing adhesion to the open science initiative.
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Rassinoux, A. M. "Knowledge Representation and Management." Yearbook of Medical Informatics 19, no. 01 (August 2010): 64–67. http://dx.doi.org/10.1055/s-0038-1638691.
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Summary Objectives: To summarize current outstanding research in the field of knowledge representation and management. Method: Synopsis of the articles selected for the IMIA Yearbook 2010. Results: Four interesting papers, dealing with structured knowledge, have been selected for the section knowledge representation and management. Combining the newest techniques in computational linguistics and natural language processing with the latest methods in statistical data analysis, machine learning and text mining has proved to be efficient for turning unstructured textual information into meaningful knowledge. Three of the four selected papers for the section knowledge representation and management corroborate this approach and depict various experiments conducted to. extract meaningful knowledge from unstructured free texts such as extracting cancer disease characteristics from pathology reports, or extracting protein-protein interactions from biomedical papers, as well as extracting knowledge for the support of hypothesis generation in molecular biology from the Medline literature. Finally, the last paper addresses the level of formally representing and structuring informa- tion within clinical terminologies in order to render such information easily available and shareable among the health informatics com- munity. Conclusions: Delivering common powerful tools able to automati- cally extract meaningful information from the huge amount of elec- tronically unstructured free texts is an essential step towards promot- ing sharing and reusability across applications, domains, and institutions thus contributing to building capacities worldwide.
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Liu, Han, Alexander Gegov, and Mihaela Cocea. "Rule Based Networks: An Efficient and Interpretable Representation of Computational Models." Journal of Artificial Intelligence and Soft Computing Research 7, no. 2 (April 1, 2017): 111–23. http://dx.doi.org/10.1515/jaiscr-2017-0008.
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Abstract Due to the vast and rapid increase in the size of data, data mining has been an increasingly important tool for the purpose of knowledge discovery to prevent the presence of rich data but poor knowledge. In this context, machine learning can be seen as a powerful approach to achieve intelligent data mining. In practice, machine learning is also an intelligent approach for predictive modelling. Rule learning methods, a special type of machine learning methods, can be used to build a rule based system as a special type of expert systems for both knowledge discovery and predictive modelling. A rule based system may be represented through different structures. The techniques for representing rules are known as rule representation, which is significant for knowledge discovery in relation to the interpretability of the model, as well as for predictive modelling with regard to efficiency in predicting unseen instances. This paper justifies the significance of rule representation and presents several existing representation techniques. Two types of novel networked topologies for rule representation are developed against existing techniques. This paper also includes complexity analysis of the networked topologies in order to show their advantages comparing with the existing techniques in terms of model interpretability and computational efficiency.
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Hadioui, Abdelladim, Yassine Benjelloun Touimi, Nour-eddine El Faddouli, and Samir Bennani. "Intelligent machine for ontological representation of massive pedagogical knowledge based on neural networks." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 2 (April 1, 2021): 1675. http://dx.doi.org/10.11591/ijece.v11i2.pp1675-1688.
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Higher education is increasingly integrating free learning management systems (LMS). The main objective underlying such systems integration is the automatization of online educational processes for the benefit of all the involved actors who use these systems. The said processes are developed through the integration and implementation of learning scenarios similar to traditional learning systems. LMS produce big data traces emerging from actors’ interactions in online learning. However, we note the absence of instruments adequate for representing knowledge extracted from big traces. In this context, the research at hand is aimed at transforming the big data produced via interactions into big knowledge that can be used in MOOCs by actors falling within a given learning level within a given learning domain, be it formal or informal. In order to achieve such an objective, ontological approaches are taken, namely: mapping, learning and enrichment, in addition to artificial intelligence-based approaches which are relevant in our research context. In this paper, we propose three interconnected algorithms for a better ontological representation of learning actors’ knowledge, while premising heavily on artificial intelligence approaches throughout the stages of this work. For verifying the validity of our contribution, we will implement an experiment about knowledge sources example.
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Tian, Yousheng, Yingxu Wang, Marina L. Gavrilova, and Guenther Ruhe. "A Formal Knowledge Representation System (FKRS) for the Intelligent Knowledge Base of a Cognitive Learning Engine." International Journal of Software Science and Computational Intelligence 3, no. 4 (October 2011): 1–17. http://dx.doi.org/10.4018/jssci.2011100101.
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It is recognized that the generic form of machine learning is a knowledge acquisition and manipulation process mimicking the brain. Therefore, knowledge representation as a dynamic concept network is centric in the design and implementation of the intelligent knowledge base of a Cognitive Learning Engine (CLE). This paper presents a Formal Knowledge Representation System (FKRS) for autonomous concept formation and manipulation based on concept algebra. The Object-Attribute-Relation (OAR) model for knowledge representation is adopted in the design of FKRS. The conceptual model, architectural model, and behavioral models of the FKRS system is formally designed and specified in Real-Time Process Algebra (RTPA). The FKRS system is implemented in Java as a core component towards the development of the CLE and other knowledge-based systems in cognitive computing and computational intelligence.
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Shu, Chao, Junjie He, Guangjie Xue, and Cheng Xie. "Grain Knowledge Graph Representation Learning: A New Paradigm for Microstructure-Property Prediction." Crystals 12, no. 2 (February 18, 2022): 280. http://dx.doi.org/10.3390/cryst12020280.
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The mesoscopic structure significantly affects the properties of polycrystalline materials. Current artificial-based microstructure-performance analyses are expensive and require rich expert knowledge. Recently, some machine learning models have been used to predict the properties of polycrystalline materials. However, they cannot capture the complex interactive relationship between the grains in the microstructure, which is a crucial factor affecting the material’s macroscopic properties. Here, we propose a grain knowledge graph representation learning method. First, based on the polycrystalline structure, an advanced digital representation of the knowledge graph is constructed, embedding ingenious knowledge while completely restoring the polycrystalline structure. Then, a heterogeneous grain graph attention model (HGGAT) is proposed to realize the effective high-order feature embedding of the microstructure and to mine the relationship between the structure and the material properties. Through benchmarking with other machine learning methods on magnesium alloy datasets, HGGAT consistently demonstrates superior accuracy on different performance labels. The experiment shows the rationality and validity of the grain knowledge graph representation and the feasibility of this work to predict the material’s structural characteristics.
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Demidova, Elena, Alishiba Dsouza, Simon Gottschalk, Nicolas Tempelmeier, and Ran Yu. "Creating knowledge graphs for geographic data on the web." ACM SIGWEB Newsletter, Winter (December 2022): 1–8. http://dx.doi.org/10.1145/3522598.3522602.
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Geographic data plays an essential role in various Web, Semantic Web and machine learning applications. OpenStreetMap and knowledge graphs are critical complementary sources of geographic data on the Web. However, data veracity, the lack of integration of geographic and semantic characteristics, and incomplete representations substantially limit the data utility. Verification, enrichment and semantic representation are essential for making geographic data accessible for the Semantic Web and machine learning. This article describes recent approaches we developed to tackle these challenges.
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Xie, Cheng, Ziwen Pan, and Chao Shu. "Microstructure Representation Knowledge Graph to Explore the Twinning Formation." Crystals 12, no. 4 (March 27, 2022): 466. http://dx.doi.org/10.3390/cryst12040466.
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Deformation twinning is an important mechanism of the plastic deformation of materials. The density of twins also affects the properties of the material. At present, the research methods of deformation twinning mainly depend on in situ EBSD, numerically investigated analysis and the finite element method. The application of machine learning methods to material microstructure research can shorten the time taken for material analysis. Machine learning methods are faced with the problem of the effective representation of the microstructure. We present a deformation twinning research method based on the representation of grain morphology features in a knowledge graph. We construct an autoencoder to extract grain morphology characteristics for building a grain knowledge graph. Then, a graph convolutional network (GCN) and fully connected network are developed to extract grain knowledge graph features and predict the twin density of materials subjected to specific tensile deformation. We use Mg-2Zn-3Li alloy as an experimental example to predict the twin density on three indexes of average grain size, twin boundaries density and average grain surface. The R2 score of the prediction result on the twin boundaries density is up to 0.510, and the R2 score of the average grain size and average grain surface is over 0.750. Therefore, the proposed method for deformation twinning research is effective and feasible.
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Xu, He, Qunli Zheng, Jingshu Zhu, Zuoling Xie, Haitao Cheng, Peng Li, and Yimu Ji. "A Deep Learning Model Incorporating Knowledge Representation Vectors and Its Application in Diabetes Prediction." Disease Markers 2022 (August 12, 2022): 1–17. http://dx.doi.org/10.1155/2022/7593750.
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The deep learning methods for various disease prediction tasks have become very effective and even surpass human experts. However, the lack of interpretability and medical expertise limits its clinical application. This paper combines knowledge representation learning and deep learning methods, and a disease prediction model is constructed. The model initially constructs the relationship graph between the physical indicator and the test value based on the normal range of human physical examination index. And the human physical examination index for testing value by knowledge representation learning model is encoded. Then, the patient physical examination data is represented as a vector and input into a deep learning model built with self-attention mechanism and convolutional neural network to implement disease prediction. The experimental results show that the model which is used in diabetes prediction yields an accuracy of 97.18% and the recall of 87.55%, which outperforms other machine learning methods (e.g., lasso, ridge, support vector machine, random forest, and XGBoost). Compared with the best performing random forest method, the recall is increased by 5.34%, respectively. Therefore, it can be concluded that the application of medical knowledge into deep learning through knowledge representation learning can be used in diabetes prediction for the purpose of early detection and assisting diagnosis.
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Shafiq, Syed Imran, Cesar Sanin, and Edward Szczebicki. "Integrating Experience-Based Knowledge Representation and Machine Learning for Efficient Virtual Engineering Object Performance." Procedia Computer Science 192 (2021): 3955–65. http://dx.doi.org/10.1016/j.procs.2021.09.170.
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Hu, Yang, Adriane Chapman, Guihua Wen, and Dame Wendy Hall. "What Can Knowledge Bring to Machine Learning?—A Survey of Low-shot Learning for Structured Data." ACM Transactions on Intelligent Systems and Technology 13, no. 3 (June 30, 2022): 1–45. http://dx.doi.org/10.1145/3510030.
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Supervised machine learning has several drawbacks that make it difficult to use in many situations. Drawbacks include heavy reliance on massive training data, limited generalizability, and poor expressiveness of high-level semantics. Low-shot Learning attempts to address these drawbacks. Low-shot learning allows the model to obtain good predictive power with very little or no training data, where structured knowledge plays a key role as a high-level semantic representation of human. This article will review the fundamental factors of low-shot learning technologies, with a focus on the operation of structured knowledge under different low-shot conditions. We also introduce other techniques relevant to low-shot learning. Finally, we point out the limitations of low-shot learning, the prospects and gaps of industrial applications, and future research directions.
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Yeom, Chan-Uk, and Keun-Chang Kwak. "Short-Term Electricity-Load Forecasting Using a TSK-Based Extreme Learning Machine with Knowledge Representation." Energies 10, no. 10 (October 16, 2017): 1613. http://dx.doi.org/10.3390/en10101613.
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Jia, Lilin, Cordelia Mattuvarkuzhali Ezhilarasu, and Ian Jennions. "Novel Way to Apply Transfer Learning to Aircraft System Fault Diagnosis." PHM Society European Conference 7, no. 1 (June 29, 2022): 577–79. http://dx.doi.org/10.36001/phme.2022.v7i1.3299.
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In recent years, transfer learning as a method that solves many issues limiting the real-world application of conventional machine learning methods has received dramatically increasing attention in the field of machine fault diagnosis. One major finding from an initial literature review shows that the majority of the existing research only focus on the transfer of diagnostic knowledge between various conditions of the same machine or different representation of similar machines. The primary goal of the current work is to seek a way to apply transfer learning to distinct domains, thereby expanding the boundary of transfer learning in the fault diagnosis field. In particular, attempts will be made to explore ways of transferring knowledge between diagnostic tasks of different aircraft systems. One promising method to help achieving this goal is transfer learning by structural analogy, since this method is capable of extracting high-level structural knowledge to apply transfer learning between seemingly unrelated domains, similar to the scenarios of transfer between different aircraft systems.
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Elfadil, Nazar. "Machine Learning: Automated Knowledge Acquisition Based on Unsupervised Neural Network and Expert System Paradigms." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 6 (November 20, 2005): 693–97. http://dx.doi.org/10.20965/jaciii.2005.p0693.
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Self-organizing maps are unsupervised neural network models that lend themselves to the cluster analysis of high-dimensional input data. Interpreting a trained map is difficult because features responsible for specific cluster assignment are not evident from resulting map representation. This paper presents an approach to automated knowledge acquisition using Kohonen's self-organizing maps and k-means clustering. To demonstrate the architecture and validation, a data set representing animal world has been used as the training data set. The verification of the produced knowledge base is done by using conventional expert system.
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Perlovsky, Leonid, and Gary Kuvich. "Machine Learning and Cognitive Algorithms for Engineering Applications." International Journal of Cognitive Informatics and Natural Intelligence 7, no. 4 (October 2013): 64–82. http://dx.doi.org/10.4018/ijcini.2013100104.
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Mind is based on intelligent cognitive processes, which are not limited by language and logic only. The thought is a set of informational processes in the brain, and such processes have the same rationale as any other systematic informational processes. Their specifics are determined by the ways of how brain stores, structures, and process this information. Systematic approach allows representing them in a diagrammatic form that can be formalized. Semiotic approach allows for the universal representation of such diagrams. In that approach, logic is a way of synthesis of such structures, which is a small but clearly visible top of the iceberg. The most efforts were traditionally put into logics without paying much attention to the rest of the mechanisms that make the entire thought system working autonomously. Dynamic fuzzy logic is reviewed and its connections with semiotics are established. Dynamic fuzzy logic extends fuzzy logic in the direction of logic-processes, which include processes of fuzzification and defuzzification as parts of logic. The paper reviews basic cognitive mechanisms, including instinctual drives, emotional and conceptual mechanisms, perception, cognition, language, a model of interaction between language and cognition upon the new semiotic models. The model of interacting cognition and language is organized in an approximate hierarchy of mental representations from sensory percepts at the “bottom” to objects, contexts, situations, abstract concepts-representations, and to the most general representations at the “top” of mental hierarchy. Knowledge Instinct and emotions are driving feedbacks for these representations. Interactions of bottom-up and top-down processes in such hierarchical semiotic representation are essential for modeling cognition. Dynamic fuzzy logic is analyzed as a fundamental mechanism of these processes. Future research directions are discussed.
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GAO, TIANTIAN, PAUL FODOR, and MICHAEL KIFER. "Querying Knowledge via Multi-Hop English Questions." Theory and Practice of Logic Programming 19, no. 5-6 (September 2019): 636–53. http://dx.doi.org/10.1017/s1471068419000103.
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AbstractThe inherent difficulty of knowledge specification and the lack of trained specialists are some of the key obstacles on the way to making intelligent systems based on the knowledge representation and reasoning (KRR) paradigm commonplace.Knowledge and query authoringusing natural language, especiallycontrollednatural language (CNL), is one of the promising approaches that could enable domain experts, who are not trained logicians, to both create formal knowledge and query it. In previous work, we introduced theKALMsystem (Knowledge Authoring Logic Machine) that supports knowledge authoring (and simple querying) with very high accuracy that at present is unachievable via machine learning approaches. The present paper expands on the question answering aspect of KALM and introducesKALM-QA(KALM for Question Answering) that is capable of answering much more complex English questions. We show that KALM-QA achieves 100% accuracy on an extensive suite of movie-related questions, calledMetaQA, which contains almost 29,000 test questions and over 260,000 training questions. We contrast this with a published machine learning approach, which falls far short of this high mark.
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Chen, Chi-Jen, Neha Warikoo, Yung-Chun Chang, Jin-Hua Chen, and Wen-Lian Hsu. "Medical knowledge infused convolutional neural networks for cohort selection in clinical trials." Journal of the American Medical Informatics Association 26, no. 11 (August 7, 2019): 1227–36. http://dx.doi.org/10.1093/jamia/ocz128.
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Abstract Objective In this era of digitized health records, there has been a marked interest in using de-identified patient records for conducting various health related surveys. To assist in this research effort, we developed a novel clinical data representation model entitled medical knowledge-infused convolutional neural network (MKCNN), which is used for learning the clinical trial criteria eligibility status of patients to participate in cohort studies. Materials and Methods In this study, we propose a clinical text representation infused with medical knowledge (MK). First, we isolate the noise from the relevant data using a medically relevant description extractor; then we utilize log-likelihood ratio based weights from selected sentences to highlight “met” and “not-met” knowledge-infused representations in bichannel setting for each instance. The combined medical knowledge-infused representation (MK) from these modules helps identify significant clinical criteria semantics, which in turn renders effective learning when used with a convolutional neural network architecture. Results MKCNN outperforms other Medical Knowledge (MK) relevant learning architectures by approximately 3%; notably SVM and XGBoost implementations developed in this study. MKCNN scored 86.1% on F1metric, a gain of 6% above the average performance assessed from the submissions for n2c2 task. Although pattern/rule-based methods show a higher average performance for the n2c2 clinical data set, MKCNN significantly improves performance of machine learning implementations for clinical datasets. Conclusion MKCNN scored 86.1% on the F1 score metric. In contrast to many of the rule-based systems introduced during the n2c2 challenge workshop, our system presents a model that heavily draws on machine-based learning. In addition, the MK representations add more value to clinical comprehension and interpretation of natural texts.
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Jadhav, Mrunal, and Matthew Guzdial. "Tile Embedding: A General Representation for Level Generation." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 17, no. 1 (October 4, 2021): 34–41. http://dx.doi.org/10.1609/aiide.v17i1.18888.
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In recent years, Procedural Level Generation via Machine Learning (PLGML) techniques have been applied to generate game levels with machine learning. These approaches rely on human-annotated representations of game levels. Creating annotated datasets for games requires domain knowledge and is time-consuming. Hence, though a large number of video games exist, annotated datasets are curated only for a small handful. Thus current PLGML techniques have been explored in limited domains, with Super Mario Bros. as the most common example. To address this problem, we present tile embeddings, a unified, affordance-rich representation for tile-based 2D games. To learn this embedding, we employ autoencoders trained on the visual and semantic information of tiles from a set of existing, human-annotated games. We evaluate this representation on its ability to predict affordances for unseen tiles, and to serve as a PLGML representation for annotated and unannotated games.
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Wang, Yingxu, Yousheng Tian, and Kendal Hu. "Semantic Manipulations and Formal Ontology for Machine Learning based on Concept Algebra." International Journal of Cognitive Informatics and Natural Intelligence 5, no. 3 (July 2011): 1–29. http://dx.doi.org/10.4018/ijcini.2011070101.
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Towards the formalization of ontological methodologies for dynamic machine learning and semantic analyses, a new form of denotational mathematics known as concept algebra is introduced. Concept Algebra (CA) is a denotational mathematical structure for formal knowledge representation and manipulation in machine learning and cognitive computing. CA provides a rigorous knowledge modeling and processing tool, which extends the informal, static, and application-specific ontological technologies to a formal, dynamic, and general mathematical means. An operational semantics for the calculus of CA is formally elaborated using a set of computational processes in real-time process algebra (RTPA). A case study is presented on how machines, cognitive robots, and software agents may mimic the key ability of human beings to autonomously manipulate knowledge in generic learning using CA. This work demonstrates the expressive power and a wide range of applications of CA for both humans and machines in cognitive computing, semantic computing, machine learning, and computational intelligence.
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Trappey, Amy J. C., Chih-Ping Liang, and Hsin-Jung Lin. "Using Machine Learning Language Models to Generate Innovation Knowledge Graphs for Patent Mining." Applied Sciences 12, no. 19 (September 29, 2022): 9818. http://dx.doi.org/10.3390/app12199818.
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To explore and understand the state-of-the-art innovations in any given domain, researchers often need to study many domain patents and synthesize their knowledge content. This study provides a smart patent knowledge graph generation system, adopting a machine learning (ML) natural language modeling approach, to help researchers grasp the patent knowledge by generating deep knowledge graphs. This research focuses on converting chemical utility patents, consisting of chemistries and chemical processes, into summarized knowledge graphs. The research methods are in two parts, i.e., the visualization of the chemical processes in the chemical patents’ most relevant paragraphs and a knowledge graph of any domain-specific collection of patent texts. The ML language modeling algorithms, including ALBERT for text vectorization, Sentence-BERT for sentence classification, and KeyBERT for keyword extraction, are adopted. These models are trained and tested in the case study using 879 chemical patents in the carbon capture domain. The results demonstrate that the average retention rate of the summary graphs for five clustered patent texts exceeds 80%. The proposed approach is novel and proven to be reliable in graphical deep knowledge representation.
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O’Mahony, Niall, Sean Campbell, Lenka Krpalkova, Anderson Carvalho, Joseph Walsh, and Daniel Riordan. "Representation Learning for Fine-Grained Change Detection." Sensors 21, no. 13 (June 30, 2021): 4486. http://dx.doi.org/10.3390/s21134486.
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Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.
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Li, Hui, Lin Yu, Jie Zhang, and Ming Lyu. "Fusion Deep Learning and Machine Learning for Heterogeneous Military Entity Recognition." Wireless Communications and Mobile Computing 2022 (January 17, 2022): 1–11. http://dx.doi.org/10.1155/2022/1103022.
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With respect to the fuzzy boundaries of military heterogeneous entities, this paper improves the entity annotation mechanism for entity with fuzzy boundaries based on related research works. This paper applies a BERT-BiLSTM-CRF model fusing deep learning and machine learning to recognize military entities, and thus, we can construct a smart military knowledge base with these entities. Furthermore, we can explore many military AI applications with the knowledge base and military Internet of Things (MIoT). To verify the performance of the model, we design multiple types of experiments. Experimental results show that the recognition performance of the model keeps improving with the increasing size of the corpus in the multidata source scenario, with the F -score increasing from 73.56% to 84.53%. Experimental results of cross-corpus cross-validation show that the more types of entities covered in the training corpus and the richer the representation type, the stronger the generalization ability of the trained model, in which the recall rate of the model trained with the novel random type corpus reaches 74.33% and the F -score reaches 76.98%. The results of the multimodel comparison experiments show that the BERT-BiLSTM-CRF model applied in this paper performs well for the recognition of military entities. The longitudinal comparison experimental results show that the F -score of the BERT-BiLSTM-CRF model is 18.72%, 11.24%, 9.24%, and 5.07% higher than the four models CRF, LSTM-CRF, BiLSTM-CR, and BERT-CRF, respectively. The cross-sectional comparison experimental results show that the F -score of the BERT-BiLSTM-CRF model improved by 6.63%, 7.95%, 3.72%, and 1.81% compared to the Lattice-LSTM-CRF, CNN-BiLSTM-CRF, BERT-BiGRU-CRF, and BERT-IDCNN-CRF models, respectively.
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Zhu, Yongjun, Woojin Jung, Fei Wang, and Chao Che. "Drug repurposing against Parkinson's disease by text mining the scientific literature." Library Hi Tech 38, no. 4 (April 24, 2020): 741–50. http://dx.doi.org/10.1108/lht-08-2019-0170.
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PurposeDrug repurposing involves the identification of new applications for existing drugs. Owing to the enormous rise in the costs of pharmaceutical R&D, several pharmaceutical companies are leveraging repurposing strategies. Parkinson's disease is the second most common neurodegenerative disorder worldwide, affecting approximately 1–2 percent of the human population older than 65 years. This study proposes a literature-based drug repurposing strategy in Parkinson's disease.Design/methodology/approachThe literature-based drug repurposing strategy proposed herein combined natural language processing, network science and machine learning methods for analyzing unstructured text data and producing actional knowledge for drug repurposing. The approach comprised multiple computational components, including the extraction of biomedical entities and their relationships, knowledge graph construction, knowledge representation learning and machine learning-based prediction.FindingsThe proposed strategy was used to mine information pertaining to the mechanisms of disease treatment from known treatment relationships and predict drugs for repurposing against Parkinson's disease. The F1 score of the best-performing method was 0.97, indicating the effectiveness of the proposed approach. The study also presents experimental results obtained by combining the different components of the strategy.Originality/valueThe drug repurposing strategy proposed herein for Parkinson's disease is distinct from those existing in the literature in that the drug repurposing pipeline includes components of natural language processing, knowledge representation and machine learning for analyzing the scientific literature. The results of the study provide important and valuable information to researchers studying different aspects of Parkinson's disease.
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Chen, Po-Chi, Ru-Fang Hsueh, and Shu-Yuen Hwang. "An ILP Based Knowledge Discovery System." International Journal on Artificial Intelligence Tools 06, no. 01 (March 1997): 63–95. http://dx.doi.org/10.1142/s0218213097000050.
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Interest in research into knowledge discovery in databases (KDD) has been growing continuously because of the rapid increase in the amount of information embedded in real-world data. Several systems have been proposed for studying the KDD process. One main task in a KDD system is to learn important and user-interesting knowledge from a set of collected data. Most proposed systems use simple machine learning methods to learn the pattern. This may result in efficient performance but the discovery quality is less useful. In this paper, we propose a method to integrated a new and complicated machine learning method called inductive logic programming (ILP) to improve the KDD quality. Such integration shows how this new learning technique can be easily applied to a KDD system and how it can improve the representation of the discovery. In our system, we use user's queries to indicate the importance and interestingness of the target knowledge. The system has been implemented on a SUN workstation using the Sybase database system. Detailed examples are also provided to illustrate the benefit of integrating the ILP technique with the KDD system.
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Wei, Qiang, Yaoyun Zhang, Muhammad Amith, Rebecca Lin, Jenay Lapeyrolerie, Cui Tao, and Hua Xu. "Recognizing software names in biomedical literature using machine learning." Health Informatics Journal 26, no. 1 (September 30, 2019): 21–33. http://dx.doi.org/10.1177/1460458219869490.
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Software tools now are essential to research and applications in the biomedical domain. However, existing software repositories are mainly built using manual curation, which is time-consuming and unscalable. This study took the initiative to manually annotate software names in 1,120 MEDLINE abstracts and titles and used this corpus to develop and evaluate machine learning–based named entity recognition systems for biomedical software. Specifically, two strategies were proposed for feature engineering: (1) domain knowledge features and (2) unsupervised word representation features of clustered and binarized word embeddings. Our best system achieved an F-measure of 91.79% for recognizing software from titles and an F-measure of 86.35% for recognizing software from both titles and abstracts using inexact matching criteria. We then created a biomedical software catalog with 19,557 entries using the developed system. This study demonstrates the feasibility of using natural language processing methods to automatically build a high-quality software index from biomedical literature.
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De Oliveira, João Gabriel Lopes, Editorial office Pedro Moreira Menezes Da Costa, and Flavio De Mello. "Knowledge Geometry in Phenomenon Perception and Artificial Intelligence." JUCS - Journal of Universal Computer Science 26, no. 5 (May 28, 2020): 604–23. http://dx.doi.org/10.3897/jucs.2020.032.
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Artificial Intelligence (AI) pervades industry, entertainment, transportation, finance, and health. It seems to be in a kind of golden age, but today AI is based on the strength of techniques that bear little relation to the thought mechanism. Contemporary techniques of machine learning, deep learning and case-based reasoning seem to be occupied with delivering functional and optimized solutions, leaving aside the core reasons of why such solutions work. This paper, in turn, proposes a theoretical study of perception, a key issue for knowledge acquisition and intelligence construction. Its main concern is the formal representation of a perceived phenomenon by a casual observer and its relationship with machine intelligence. This work is based on recently proposed geometric theory, and represents an approach that is able to describe the inuence of scope, development paradigms, matching process and ground truth on phenomenon perception. As a result, it enumerates the perception variables and describes the implications for AI.
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Li, Congli. "A Study on Chinese-English Machine Translation Based on Transfer Learning and Neural Networks." Wireless Communications and Mobile Computing 2022 (March 18, 2022): 1–9. http://dx.doi.org/10.1155/2022/8282164.
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The existing Chinese-English machine translation has problems such as inaccurate word translation and difficult translation of long sentences. To this end, this paper proposes a new machine translation model based on bidirectional Chinese-English translation incorporating translation knowledge and transfer learning, and the components of this model include a recurrent neural network-based translation quality assessment model and a self-focused network-based model. The experimental results demonstrate that our method works better on the dataset of machine translation quality assessment task for Chinese-English translation with more information, and the Pearson correlation coefficient of its quality assessment feature vector (such as word prediction vector representation) is higher.
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Anami, Basavaraj S., Ramesh S. Wadawadagi, and Veerappa B. Pagi. "Machine Learning Techniques in Web Content Mining: A Comparative Analysis." Journal of Information & Knowledge Management 13, no. 01 (March 2014): 1450005. http://dx.doi.org/10.1142/s0219649214500051.
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With incessantly growing amount of information published over Web pages, the World Wide Web (WWW) has become prolific in the field of data mining research. The heterogeneous and semi-structured nature of Web data has made the process of automated discovery a challenging issue. Web Content Mining (WCM) essentially uses data mining techniques to effectively discover knowledge from Web page contents. The intent of this study is to provide a comparative analysis of Machine Learning (ML) techniques available in the literature for WCM. For analysis, the article focuses on issues such as representation techniques, learning methods, datasets used and performance of each method as a criterion. The survey observes that some of the traditional ML algorithms have been efficiently used to work on Web data. Finally, the paper concludes citing some promising issues for further research in this domain.
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Marquez, J. J., M. Rodriguez, M. L. Martinez, and J. M. Perez. "New Virtual Environment for Active Learning on Parameter Adjustment of Plastic Injection Molding." Materials Science Forum 625 (August 2009): 83–94. http://dx.doi.org/10.4028/www.scientific.net/msf.625.83.
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This paper describes some aspects of new software development and its academic application. This program is an alternative to enhance and to improve the available resources for students to acquire practical knowledge in plastic injection molding parameterization. A virtual injection molding environment has been developed, which allows preliminary machine capacity determination, number of cavities analysis, injection cycle parameter definition, and defects analysis and representation. The environment allows the student to carry out an iteration process in order to optimize the injection molding process parameters. All decision making is based on an Expert System which response is similar to a skilled machine operator.
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JADIDINEJAD, A. H., F. MAHMOUDI, and M. R. MEYBODI. "Clique-based semantic kernel with application to semantic relatedness." Natural Language Engineering 21, no. 5 (April 14, 2015): 725–42. http://dx.doi.org/10.1017/s135132491500008x.
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AbstractThe emergence of knowledge repositories in a variety of domains provides a valuable opportunity for semantic interpretation of high dimensional datasets. Previous researches investigate the use of concept instead of word as a core semantic feature for incorporating semantic knowledge from an ontology into the representation model of documents. On the other hand, in machine learning and information retrieval, data objects are represented as a flat feature vector. The inconsistency between the structural nature of the knowledge repositories and the flat representation of features in machine learning leads researchers to neglect the structure of the knowledge base and leverage concepts as isolated semantic features, which is known as bag-of-concepts. Although, using concepts has some advantages over words, by neglecting the relation between concepts, the problem of vocabulary mismatch remains in force. In this paper, a novel semantic kernel is proposed which is capable of incorporating the relatedness between conceptual features. This kernel leverages clique theory to map data objects to a novel feature space wherein complex data objects will be comparable. The proposed kernel is relevant to all applications which have a prior knowledge about the relatedness between features. We concentrate on representing text documents and words using Wikipedia and WordNet, respectively. The experimental results over a set of benchmark datasets have revealed that the proposed kernel significantly improves the representation of both words and texts in the application of semantic relatedness.
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RICHTER, MICHAEL M., and AGNAR AAMODT. "Case-based reasoning foundations." Knowledge Engineering Review 20, no. 3 (September 2005): 203–7. http://dx.doi.org/10.1017/s0269888906000695.
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A basic observation is that case-based reasoning has roots in different disciplines: cognitive science, knowledge representation and processing, machine learning and mathematics. As a consequence, there are foundational aspects from each of these areas. We briefly discuss them and comment on the relations between these types of foundations.
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Rhodes, Chris, Richard Allmendinger, and Ricardo Climent. "New Interfaces and Approaches to Machine Learning When Classifying Gestures within Music." Entropy 22, no. 12 (December 7, 2020): 1384. http://dx.doi.org/10.3390/e22121384.
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Interactive music uses wearable sensors (i.e., gestural interfaces—GIs) and biometric datasets to reinvent traditional human–computer interaction and enhance music composition. In recent years, machine learning (ML) has been important for the artform. This is because ML helps process complex biometric datasets from GIs when predicting musical actions (termed performance gestures). ML allows musicians to create novel interactions with digital media. Wekinator is a popular ML software amongst artists, allowing users to train models through demonstration. It is built on the Waikato Environment for Knowledge Analysis (WEKA) framework, which is used to build supervised predictive models. Previous research has used biometric data from GIs to train specific ML models. However, previous research does not inform optimum ML model choice, within music, or compare model performance. Wekinator offers several ML models. Thus, we used Wekinator and the Myo armband GI and study three performance gestures for piano practice to solve this problem. Using these, we trained all models in Wekinator and investigated their accuracy, how gesture representation affects model accuracy and if optimisation can arise. Results show that neural networks are the strongest continuous classifiers, mapping behaviour differs amongst continuous models, optimisation can occur and gesture representation disparately affects model mapping behaviour; impacting music practice.
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Lakshika, M. V. P. T., and H. A. Caldera. "Knowledge Graphs Representation for Event-Related E-News Articles." Machine Learning and Knowledge Extraction 3, no. 4 (September 26, 2021): 802–18. http://dx.doi.org/10.3390/make3040040.
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E-newspaper readers are overloaded with massive texts on e-news articles, and they usually mislead the reader who reads and understands information. Thus, there is an urgent need for a technology that can automatically represent the gist of these e-news articles more quickly. Currently, popular machine learning approaches have greatly improved presentation accuracy compared to traditional methods, but they cannot be accommodated with the contextual information to acquire higher-level abstraction. Recent research efforts in knowledge representation using graph approaches are neither user-driven nor flexible to deviations in the data. Thus, there is a striking concentration on constructing knowledge graphs by combining the background information related to the subjects in text documents. We propose an enhanced representation of a scalable knowledge graph by automatically extracting the information from the corpus of e-news articles and determine whether a knowledge graph can be used as an efficient application in analyzing and generating knowledge representation from the extracted e-news corpus. This knowledge graph consists of a knowledge base built using triples that automatically produce knowledge representation from e-news articles. Inclusively, it has been observed that the proposed knowledge graph generates a comprehensive and precise knowledge representation for the corpus of e-news articles.
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Latapie, Hugo, Ozkan Kilic, Gaowen Liu, Ramana Kompella, Adam Lawrence, Yuhong Sun, Jayanth Srinivasa, Yan Yan, Pei Wang, and Kristinn R. Thórisson. "A Metamodel and Framework for Artificial General Intelligence From Theory to Practice." Journal of Artificial Intelligence and Consciousness 08, no. 02 (April 22, 2021): 205–27. http://dx.doi.org/10.1142/s2705078521500119.
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This paper introduces a new metamodel-based knowledge representation that significantly improves autonomous learning and adaptation. While interest in hybrid machine learning/symbolic AI systems leveraging, for example, reasoning and knowledge graphs, is gaining popularity, we find there remains a need for both a clear definition of knowledge and a metamodel to guide the creation and manipulation of knowledge. Some of the benefits of the metamodel we introduce in this paper include a solution to the symbol grounding problem, cumulative learning and federated learning. We have applied the metamodel to problems ranging from time series analysis, computer vision and natural language understanding and have found that the metamodel enables a wide variety of learning mechanisms ranging from machine learning, to graph network analysis and learning by reasoning engines to interoperate in a highly synergistic way. Our metamodel-based projects have consistently exhibited unprecedented accuracy, performance, and ability to generalize. This paper is inspired by the state-of-the-art approaches to AGI, recent AGI-aspiring work, the granular computing community, as well as Alfred Korzybski’s general semantics. One surprising consequence of the metamodel is that it not only enables a new level of autonomous learning and optimal functioning for machine intelligences, but may also shed light on a path to better understanding how to improve human cognition.
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Balkanyi, Laszlo, and Ronald Cornet. "The Interplay of Knowledge Representation with Various Fields of Artificial Intelligence in Medicine." Yearbook of Medical Informatics 28, no. 01 (April 25, 2019): 027–34. http://dx.doi.org/10.1055/s-0039-1677899.
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Introduction: Artificial intelligence (AI) is widespread in many areas, including medicine. However, it is unclear what exactly AI encompasses. This paper aims to provide an improved understanding of medical AI and its constituent fields, and their interplay with knowledge representation (KR). Methods: We followed a Wittgensteinian approach (“meaning by usage”) applied to content metadata labels, using the Medical Subject Headings (MeSH) thesaurus to classify the field. To understand and characterize medical AI and the role of KR, we analyzed: (1) the proportion of papers in MEDLINE related to KR and various AI fields; (2) the interplay among KR and AI fields and overlaps among the AI fields; (3) interconnectedness of fields; and (4) phrase frequency and collocation based on a corpus of abstracts. Results: Data from over eighty thousand papers showed a steep, six-fold surge in the last 30 years. This growth happened in an escalating and cascading way. A corpus of 246,308 total words containing 21,842 unique words showed several hundred occurrences of notions such as robotics, fuzzy logic, neural networks, machine learning and expert systems in the phrase frequency analysis. Collocation analysis shows that fuzzy logic seems to be the most often collocated notion. Neural networks and machine learning are also used in the conceptual neighborhood of KR. Robotics is more isolated. Conclusions: Authors note an escalation of published AI studies in medicine. Knowledge representation is one of the smaller areas, but also the most interconnected, and provides a common cognitive layer for other areas.
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MITRA, ARINDAM, and CHITTA BARAL. "Incremental and Iterative Learning of Answer Set Programs from Mutually Distinct Examples." Theory and Practice of Logic Programming 18, no. 3-4 (July 2018): 623–37. http://dx.doi.org/10.1017/s1471068418000248.
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AbstractOver the years the Artificial Intelligence (AI) community has produced several datasets which have given the machine learning algorithms the opportunity to learn various skills across various domains. However, a subclass of these machine learning algorithms that aimed at learning logic programs, namely the Inductive Logic Programming algorithms, have often failed at the task due to the vastness of these datasets. This has impacted the usability of knowledge representation and reasoning techniques in the development of AI systems. In this research, we try to address this scalability issue for the algorithms that learn answer set programs. We present a sound and complete algorithm which takes the input in a slightly different manner and performs an efficient and more user controlled search for a solution. We show via experiments that our algorithm can learn from two popular datasets from machine learning community, namely bAbl (a question answering dataset) and MNIST (a dataset for handwritten digit recognition), which to the best of our knowledge was not previously possible. The system is publicly available athttps://goo.gl/KdWAcV.
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Luo, Lei, Jie Xu, Cheng Deng, and Heng Huang. "Orthogonality-Promoting Dictionary Learning via Bayesian Inference." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4472–79. http://dx.doi.org/10.1609/aaai.v33i01.33014472.
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Dictionary Learning (DL) plays a crucial role in numerous machine learning tasks. It targets at finding the dictionary over which the training set admits a maximally sparse representation. Most existing DL algorithms are based on solving an optimization problem, where the noise variance and sparsity level should be known as the prior knowledge. However, in practice applications, it is difficult to obtain these knowledge. Thus, non-parametric Bayesian DL has recently received much attention of researchers due to its adaptability and effectiveness. Although many hierarchical priors have been used to promote the sparsity of the representation in non-parametric Bayesian DL, the problem of redundancy for the dictionary is still overlooked, which greatly decreases the performance of sparse coding. To address this problem, this paper presents a novel robust dictionary learning framework via Bayesian inference. In particular, we employ the orthogonality-promoting regularization to mitigate correlations among dictionary atoms. Such a regularization, encouraging the dictionary atoms to be close to being orthogonal, can alleviate overfitting to training data and improve the discrimination of the model. Moreover, we impose Scale mixture of the Vector variate Gaussian (SMVG) distribution on the noise to capture its structure. A Regularized Expectation Maximization Algorithm is developed to estimate the posterior distribution of the representation and dictionary with orthogonality-promoting regularization. Numerical results show that our method can learn the dictionary with an accuracy better than existing methods, especially when the number of training signals is limited.