A Matrix Approach to Implicit Relationship Finding in Large-Scale Knowledge Bases

2011 ◽  
Author(s):  
Yan Wang ◽  
Yi Zeng ◽  
Ning Zhong ◽  
Zhisheng Huang
Keyword(s):  
Large Scale ◽  
Knowledge Bases ◽  
Matrix Approach

Capturing expert knowledge for ecosystem mapping using Bayesian networks

2006 ◽  
Vol 36 (12) ◽  
pp. 3087-3103 ◽  
Author(s):  
Adrian Walton ◽  
Del Meidinger
Keyword(s):  
Bayesian Network ◽  
Large Scale ◽  
Expert Knowledge ◽  
Accuracy Assessment ◽  
Knowledge Bases ◽  
Western Hemlock ◽  
Matrix Approach ◽  
Accuracy Assessments ◽  
Map Accuracy Assessment ◽  
Map Accuracy

Large-scale ecosystem maps are essential tools for managers of forest-related activities. In British Columbia, the prevailing approach for ecosystem mapping has been to use an expert system that captures expert knowledge in the form of a belief matrix. In this project, a Bayesian network rather than a belief matrix was used in an attempt to overcome some of the drawbacks of the belief-matrix approach. A Bayesian-network knowledge base was created for each of the following three biogeoclimatic variants: montane very wet maritime coastal western hemlock (CWHvm2), submontane very wet maritime coastal western hemlock (CWHvm1), and central very wet hypermaritime coastal western hemlock (CWHvh2), and applied to a study area encompassing Prince Rupert. A map of ecosystems by grouping site series was produced using each of the knowledge bases. Accuracy assessments performed on each of the maps of grouped site series revealed that the maps poorly predicted the spatial distribution of uncommon and very wet site-series groups. For example, overall map accuracy for the CWHvm2, CWHvm1, and CWHvh2 variants was 47.8%, 50.3%, and 33.3%, respectively. The results of the map-accuracy assessment, however, were consistent with those resulting from a belief-matrix approach conducted in an earlier study. We feel that Bayesian network knowledge bases are easier to develop, interpret, and update than belief matrices.

scholarly journals Yield Prediction for Tomato Greenhouse Using EFuNN

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Kefaya Qaddoum ◽  
E. L. Hines ◽  
D. D. Iliescu
Keyword(s):  
Large Scale ◽  
Management Practices ◽  
Intelligent System ◽  
Vapour Pressure Deficit ◽  
Knowledge Bases ◽  
Yield Prediction ◽  
Tomato Yield ◽  
Fuzzy Neural ◽  
Rule Sets ◽  
Human Operators

In the area of greenhouse operation, yield prediction still relies heavily on human expertise. This paper proposes an automatic tomato yield predictor to assist the human operators in anticipating more effectively weekly fluctuations and avoid problems of both overdemand and overproduction if the yield cannot be predicted accurately. The parameters used by the predictor consist of environmental variables inside the greenhouse, namely, temperature, CO2, vapour pressure deficit (VPD), and radiation, as well as past yield. Greenhouse environment data and crop records from a large scale commercial operation, Wight Salads Group (WSG) in the Isle of Wight, United Kingdom, collected during the period 2004 to 2008, were used to model tomato yield using an Intelligent System called “Evolving Fuzzy Neural Network” (EFuNN). Our results show that the EFuNN model predicted weekly fluctuations of the yield with an average accuracy of 90%. The contribution suggests that the multiple EFUNNs can be mapped to respective task-oriented rule-sets giving rise to adaptive knowledge bases that could assist growers in the control of tomato supplies and more generally could inform the decision making concerning overall crop management practices.

SEARCH FOR NEW METHODOLOGICAL APPROACHES AND METHODS IN RISK MANAGEMENT

2021 ◽  
Vol 54 (2) ◽  
pp. 35-47
Author(s):  
Svetlana N. Dvoryatkina ◽  
◽  
Arseny M. Lopukhin ◽  
Keyword(s):  
Risk Management ◽  
Large Scale ◽  
Quantitative Methods ◽  
Knowledge Bases ◽  
Fractal Model ◽  
Educational Process ◽  
Machine Learning Algorithms ◽  
Risk Modeling ◽  
Negative Consequences ◽  
Fractal Approach

The study actualized the complex and large-scale problem of adapting the theory of risk man-agement for the education system. A comprehensive analysis of domestic and international stud-ies revealed the lack of a theoretical framework, a general methodological vision of the problem of riskiness and risk-taking in the educational sphere. While effective management of education-al activities, ensuring the development of the competitiveness of the individual in the labor mar-ket and its potential for active participation in the life of society is possible on the basis of the modern paradigm of risk management, integrating achievements in pedagogical, economic, mathematical and computer sciences. A new methodology in the study is the fractal approach, which defines the idea of quantitative and qualitative analysis and assessment of the risk of non-formation of professional competencies, complex educational and cognitive constructs of subject activity. The fractal model of assessing the formation of knowledge and competencies, its risk landscape, taking into account the subject and cognitive divergence, will ensure the effective-ness of the structure of knowledge storage in the educational process, minimizing the time for building space and engineering knowledge bases, and the depth of solving the problem of pre-dicting educational risks. New methods of risk modeling based on machine learning algorithms and factor analysis, methods for constructing neural integrators, quantitative methods with and without taking into account the probability distribution will ensure the accuracy and speed of risk assessment and prediction, will allow one to identify new patterns of risk activity and further ways to develop the theory of risk. The presented effective strategies and innovative tools will solve the problem of minimizing unplanned chaos, the cascade of negative consequences of risky situations, including the COVID-19 epidemic.

scholarly journals Progress and Challenges on Entity Alignment of Geographic Knowledge Bases

2019 ◽  
Vol 8 (2) ◽  
pp. 77 ◽  
Author(s):  
Kai Sun ◽  
Yunqiang Zhu ◽  
Jia Song
Keyword(s):  
Large Scale ◽  
Knowledge Bases ◽  
Evaluation Procedure ◽  
Multiple Sources ◽  
Alignment Algorithms ◽  
Geographic Knowledge ◽  
Heterogeneous Features ◽  
Benchmark Datasets ◽  
Alignment Process ◽  
Made In

Geographic knowledge bases (GKBs) with multiple sources and forms are of obvious heterogeneity, which hinders the integration of geographic knowledge. Entity alignment provides an effective way to find correspondences of entities by measuring the multidimensional similarity between entities from different GKBs, thereby overcoming the semantic gap. Thus, many efforts have been made in this field. This paper initially proposes basic definitions and a general framework for the entity alignment of GKBs. Specifically, the state-of-the-art of algorithms of entity alignment of GKBs is reviewed from the three aspects of similarity metrics, similarity combination, and alignment judgement; the evaluation procedure of alignment results is also summarized. On this basis, eight challenges for future studies are identified. There is a lack of methods to assess the qualities of GKBs. The alignment process should be improved by determining the best composition of heterogeneous features, optimizing alignment algorithms, and incorporating background knowledge. Furthermore, a unified infrastructure, techniques for aligning large-scale GKBs, and deep learning-based alignment techniques should be developed. Meanwhile, the generation of benchmark datasets for the entity alignment of GKBs and the applications of this field need to be investigated. The progress of this field will be accelerated by addressing these challenges.

scholarly journals Efficient Results in Semantic Interoperability for Health Care

2016 ◽  
Vol 25 (01) ◽  
pp. 184-187
Author(s):  
J. Charlet ◽  
L. F. Soualmia ◽  
Keyword(s):  
Health Care ◽  
Semantic Web ◽  
Knowledge Representation ◽  
Large Scale ◽  
Recommendation System ◽  
Clinical Care ◽  
Knowledge Bases ◽  
Semantic Interoperability ◽  
The Third ◽  
Research Activities

Summary Objectives: To summarize excellent current research in the field of Knowledge Representation and Management (KRM) within the health and medical care domain. Method: We provide a synopsis of the 2016 IMIA selected articles as well as a related synthetic overview of the current and future field activities. A first step of the selection was performed through MEDLINE querying with a list of MeSH descriptors completed by a list of terms adapted to the KRM section. The second step of the selection was completed by the two section editors who separately evaluated the set of 1,432 articles. The third step of the selection consisted of a collective work that merged the evaluation results to retain 15 articles for peer-review. Results: The selection and evaluation process of this Yearbook’s section on Knowledge Representation and Management has yielded four excellent and interesting articles regarding semantic interoperability for health care by gathering heterogeneous sources (knowledge and data) and auditing ontologies. In the first article, the authors present a solution based on standards and Semantic Web technologies to access distributed and heterogeneous datasets in the domain of breast cancer clinical trials. The second article describes a knowledge-based recommendation system that relies on ontologies and Semantic Web rules in the context of chronic diseases dietary. The third article is related to concept-recognition and text-mining to derive common human diseases model and a phenotypic network of common diseases. In the fourth article, the authors highlight the need for auditing the SNOMED CT. They propose to use a crowd-based method for ontology engineering. Conclusions: The current research activities further illustrate the continuous convergence of Knowledge Representation and Medical Informatics, with a focus this year on dedicated tools and methods to advance clinical care by proposing solutions to cope with the problem of semantic interoperability. Indeed, there is a need for powerful tools able to manage and interpret complex, large-scale and distributed datasets and knowledge bases, but also a need for user-friendly tools developed for the clinicians in their daily practice.

COMBINING DEFAULT LOGIC DATABASES

1994 ◽  
Vol 03 (03) ◽  
pp. 319-348 ◽  
Author(s):  
CHITTA BARAL ◽  
SARIT KRAUS ◽  
JACK MINKER ◽  
V. S. SUBRAHMANIAN
Keyword(s):  
Knowledge Representation ◽  
Knowledge Base ◽  
Large Scale ◽  
Future Generation ◽  
Knowledge Bases ◽  
Default Logic ◽  
Integrity Constraints ◽  
Specialized Knowledge ◽  
The Past ◽  
The Future

During the past decade, it has become increasingly clear that the future generation of large-scale knowledge bases will consist, not of one single isolated knowledge base, but a multiplicity of specialized knowledge bases that contain knowledge about different domains of expertise. These knowledge bases will work cooperatively, pooling together their varied bodies of knowledge, so as to be able to solve complex problems that no single knowledge base, by itself, would have been able to address successfully. In any such situation, inconsistencies are bound to arise. In this paper, we address the question: "Suppose we have a set of knowledge bases, KB1, …, KBn, each of which uses default logic as the formalism for knowledge representation, and a set of integrity constraints IC. What knowledge base constitutes an acceptable combination of KB1, …, KBn?"

Mining Rules with Constants from Large Scale Knowledge Bases

2018 ◽  
pp. 521-535 ◽  
Author(s):  
Xuan Wang ◽  
Jingjing Zhang ◽  
Jinchuan Chen ◽  
Ju Fan
Keyword(s):  
Large Scale ◽  
Knowledge Bases

scholarly journals Assessing Large-Scale, Cross-Domain Knowledge Bases for Semantic Search

2020 ◽  
Vol 39 (3) ◽  
pp. 595-602
Author(s):  
Aatif Ahmad Khan ◽  
Sanjay Kumar Malik
Keyword(s):  
Domain Knowledge ◽  
Large Scale ◽  
Open Data ◽  
Knowledge Bases ◽  
Semantic Search ◽  
Semantic Web Technologies ◽  
Data Format ◽  
Web Technologies ◽  
Cross Domain ◽  
Description Framework

Semantic Search refers to set of approaches dealing with usage of Semantic Web technologies for information retrieval in order to make the process machine understandable and fetch precise results. Knowledge Bases (KB) act as the backbone for semantic search approaches to provide machine interpretable information for query processing and retrieval of results. These KB include Resource Description Framework (RDF) datasets and populated ontologies. In this paper, an assessment of the largest cross-domain KB is presented that are exploited in large scale semantic search and are freely available on Linked Open Data Cloud. Analysis of these datasets is a prerequisite for modeling effective semantic search approaches because of their suitability for particular applications. Only the large scale, cross-domain datasets are considered, which are having sizes more than 10 million RDF triples. Survey of sizes of the datasets in triples count has been depicted along with triples data format(s) supported by them, which is quite significant to develop effective semantic search models.

scholarly journals UniMF: A Unified Framework to Incorporate Multimodal Knowledge Bases intoEnd-to-End Task-Oriented Dialogue Systems

2021 ◽  
Author(s):  
Shiquan Yang ◽  
Rui Zhang ◽  
Sarah M. Erfani ◽  
Jey Han Lau
Keyword(s):  
Large Scale ◽  
Knowledge Bases ◽  
Language Models ◽  
Dialogue Systems ◽  
Unified Framework ◽  
Proposed Model ◽  
Multimodal Information ◽  
Task Oriented ◽  
Novel Model ◽  
Single Modality

Knowledge bases (KBs) are usually essential for building practical dialogue systems. Recently we have seen rapidly growing interest in integrating knowledge bases into dialogue systems. However, existing approaches mostly deal with knowledge bases of a single modality, typically textual information. As today's knowledge bases become abundant with multimodal information such as images, audios and videos, the limitation of existing approaches greatly hinders the development of dialogue systems. In this paper, we focus on task-oriented dialogue systems and address this limitation by proposing a novel model that integrates external multimodal KB reasoning with pre-trained language models. We further enhance the model via a novel multi-granularity fusion mechanism to capture multi-grained semantics in the dialogue history. To validate the effectiveness of the proposed model, we collect a new large-scale (14K) dialogue dataset MMDialKB, built upon multimodal KB. Both automatic and human evaluation results on MMDialKB demonstrate the superiority of our proposed framework over strong baselines.

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