Ontology Based Ocean Knowledge Representation for Semantic Information Retrieval

The drastic growth of coastal observation sensors results in copious data that provide weather information.The intricacies in sensor-generated big data are heterogeneity and interpretation,driving high-end Information Retrieval(IR)systems.The Semantic Web(SW)can solve this issue by integrating data into a single platform for information exchange and knowledge retrieval.This paper focuses on exploiting the SWbase systemto provide interoperability through ontologies by combining the data concepts with ontology classes.This paper presents a 4-phase weather data model:data processing,ontology creation,SW processing,and query engine.The developed Oceanographic Weather Ontology helps to enhance data analysis,discovery,IR,and decision making.In addition to that,it also evaluates the developed ontology with other state-of-the-art ontologies.The proposed ontology’s quality has improved by 39.28%in terms of completeness,and structural complexity has decreased by 45.29%,11%and 37.7%in Precision and Accuracy.Indian Meteorological Satellite INSAT-3D’s ocean data is a typical example of testing the proposed model.The experimental result shows the effectiveness of the proposed data model and its advantages in machine understanding and IR.

Method of Dynamic Knowledge Representation and Learning Based on Fuzzy Petri Nets

A method of knowledge representation and learning based on fuzzy Petri nets was designed. In this way the parameters of weights, threshold value and certainty factor in knowledge model can be adjusted dynamically. The advantages of knowledge representation based on production rules and neural networks were integrated into this method. Just as production knowledge representation, this method has clear structure and specific parameters meaning. In addition, it has learning and parallel reasoning ability as neural networks knowledge representation does. The result of simulation shows that the learning algorithm can converge, and the parameters of weights, threshold value and certainty factor can reach the ideal level after training.

Knowledge Representation in Patient Safety Reporting: An Ontological Approach

Purpose： The current development of patient safety reporting systems is criticized for loss of information and low data quality due to the lack of a uniformed domain knowledge base and text processing functionality. To improve patient safety reporting, the present paper suggests an ontological representation of patient safety knowledge. Design/methodology/approach： We propose a framework for constructing an ontological knowledge base of patient safety. The present paper describes our design, implementation,and evaluation of the ontology at its initial stage. Findings： We describe the design and initial outcomes of the ontology implementation. The evaluation results demonstrate the clinical validity of the ontology by a self-developed survey measurement. Research limitations： The proposed ontology was developed and evaluated using a small number of information sources. Presently, US data are used, but they are not essential for the ultimate structure of the ontology.Practical implications： The goal of improving patient safety can be aided through investigating patient safety reports and providing actionable knowledge to clinical practitioners.As such, constructing a domain specific ontology for patient safety reports serves as a cornerstone in information collection and text mining methods.Originality/value： The use of ontologies provides abstracted representation of semantic information and enables a wealth of applications in a reporting system. Therefore, constructing such a knowledge base is recognized as a high priority in health care.

Knowledge Representation and Fuzzy Reasoning of an Agricultural Expert System

The design scheme of an agricultural expert system based on longan and cauliflower planting techniques is presented. Using an object-oriented design and a combination of the techniques in multimedia, database, expert system and artificial intelligence, an in-depth analysis and summary are made of the knowledge features of die agricultural multimedia expert system and data models involved. According to the practical problems in agricultural field, the architectures and functions of the system are designed, and some design ideas about the hybrid knowledge representation and fuzzy reasoning are proposed.

The joint knowledge reasoning model based on knowledge representation learning for aviation assembly domain

Knowledge graph technology is widely applied in the domain of general knowledge reasoning with an excellent performance.For fine-grained professional fields,professional knowledge graphs can provide more accurate information in practical industrial scenarios.Based on an aviation assembly domain-specific knowledge graph,the article constructs a joint knowledge reasoning model,which combines a named entity recognition model and a subgraph embedding learning model.When performing knowledge reasoning tasks,the two models vectorize entities,relationships and entity attributes in the same space,so as to share parameters and optimize learning efficiency.The knowledge reasoning model,which provides intelligent question answering services,is able to reduce the assembly error rate and improve the assembly efficiency.The system can accurately solve general knowledge reasoning problems in the assembly process in actual industrial scenarios of general assembly and component assembly under interference-free conditions.Finally,this paper compares the proposed knowledge reasoning model based on knowledge representation learning and the question-answering system based on large-scale pre-trained models.In the application scenario of system functional testing in general assembly,the joint model attains an accuracy rate of 95%,outperforming GPT with 78%accuracy and enhanced representation through knowledge integration with 71%accuracy.

Dynamic Uncertain Causality Graph for Knowledge Representation and Reasoning: Discrete DAG Cases

Developed from the dynamic causality diagram （DCD） model, a new approach for knowledge representation and reasoning named as dynamic uncertain causality graph （DUCG） is presented, which focuses on the compact representation of complex uncertain causalities and efficient probabilistie inference. It is pointed out that the existing models of compact representation and inference in Bayesian Network （BN） is applicable in single-valued cases, but may not be suitable to be applied in multi-valued cases. DUCG overcomes this problem and beyond. The main features of DUCG are： 1） compactly and graphically representing complex conditional probability distributions （CPDs）, regardless of whether the cases are single-valued or multi-valued; 2） able to perform exact reasoning in the case of the incomplete knowledge representation; 3） simplifying the graphical knowledge base conditional on observations before other calculations, so that the scale and complexity of problem can be reduced exponentially; 4） the efficient two-step inference algorithm consisting of （a） logic operation to find all possible hypotheses in concern for given observations and （b） the probability calculation for these hypotheses; and 5） much less relying on the parameter accuracy. An alarm system example is provided to illustrate the DUCG methodology.

Standard model of knowledge representation

Knowledge representation is the core of artificial intelligence research. Knowledge representation methods include predicate logic, semantic network, computer programming language, database, mathematical model, graphics language, natural language, etc. To establish the intrinsic link between various knowledge representation methods, a unified knowledge representa- tion model is necessary. According to ontology, system theory, and control theory, a standard model of knowledge representation that reflects the change of the objective world is proposed. The model is composed of input, processing, and output. This knowledge representation method is not a contradiction to the traditional knowledge representation method. It can express knowledge in terms of multivariate and multidimensional. It can also express process knowledge, and at the same time, it has a strong ability to solve problems. In addition, the standard model of knowledge representation provides a way to solve problems of non-precision and inconsistent knowledge.

Towards knowledge-based geovisualisation using Semantic Web technologies:a knowledge representation approach coupling ontologies and rules

Geovisualisation is a knowledge-intensive art in which both providers and users need to possess a wide range of knowledge.Current syntactic approaches to presenting visualisation information lack semantics on the one hand,and on the other hand are too bespoke.Such limitations impede the transfer,interpretation,and reuse of the geovisualisation knowledge.In this paper,we propose a knowledge-based approach to formally represent geovisualisation knowledge in a semantically-enriched and machine-readable manner using Semantic Web technologies.Specifically,we represent knowledge regarding cartographic scale,data portrayal and geometry source,which are three key aspects of geovisualisation in the contemporary web mapping era,coupling ontologies and semantic rules.The knowledge base enables inference for deriving the corresponding geometries and portrayals for visualisation under different conditions.A prototype system is developed in which geospatial linked data are used as underlying data,and some geovisualisation knowledge is formalised into a knowledge base to visualise the data and provide rich semantics to users.The proposed approach can partially form the foundation for the vision of web of knowledge for geovisualisation.

Knowledge Organization and Representation under the AI Lens

Purpose:This paper compares the paradigmatic differences between knowledge organization(KO)in library and information science and knowledge representation(KR)in AI to show the convergence in KO and KR methods and applications.Methodology:The literature review and comparative analysis of KO and KR paradigms is the primary method used in this paper.Findings:A key difference between KO and KR lays in the purpose of KO is to organize knowledge into certain structure for standardizing and/or normalizing the vocabulary of concepts and relations,while KR is problem-solving oriented.Differences between KO and KR are discussed based on the goal,methods,and functions.Research limitations:This is only a preliminary research with a case study as proof of concept.Practical implications:The paper articulates on the opportunities in applying KR and other AI methods and techniques to enhance the functions of KO.Originality/value:Ontologies and linked data as the evidence of the convergence of KO and KR paradigms provide theoretical and methodological support to innovate KO in the AI era.

Bayesian Diagnostic Network: A Powerful Model for Representation and Reasoning of Engineering Diagnostic Knowledge

Engineering diagnosis is essential to the operation of industrial equipment.The key to successful diagnosis is correct knowledge representation and reasoning. The Bayesiannetwork is a powerful tool for it. This paper utilizes the Bayesian network to represent and reasondiagnostic knowledge, named Bayesian diagnostic network. It provides a three-layer topologicstructure based on operating conditions, possible faults and corresponding symptoms. The paper alsodiscusses an approximate stochastic sampling algorithm. Then a practical Bayesian network for gasturbine diagnosis is constructed on a platform developed under a Visual C++ environment. It showsthat the Bayesian network is a powerful model for representation and reasoning of diagnosticknowledge. The three-layer structure and the approximate algorithm are effective also.

A Knowledge Representation Model for Video-Based Animation

In this papert a brief survey on knowledge-based animation techniques is given.Then a VideoStream-based Knowledge Representation Model (VSKRM) for Joint Objects is presented which includes the knowledge representation of: Graphic Object,Action and VideoStream. Next a general description of the UI framework of a system is given based on the VSKRM model. Finally a conclusion is reached.

Functional Knowledge Representation Based on Problem Reduction

This paper proposes an approach for functional knowledge representation based on problem reduction,which represents the organization of problem-solving activities in two levels:reduction and reasoning.The former makes the functional plans for problem-solving while the latter constructs functional units, called handlers,for executing subproblems designated by these plans.This approach emphasizes that the representation of domain knowledge should be closely combined with(rather than separated from)its use therefore provides a set of reasoning-level primitives to construct handlers and formulate the control strate- gies for executing them.As reduction-level primitives,handlers are used to construct handler-associative networks,which become the executable representation of problem-reduction graphs,in order to realize the problem-solving methods suited to domain features.Besides,handlers and their control slots can be used to focus the attention of knowledge acquisition and reasoning control.

Expert system for earthquake prediction（ESEP）（2）－Knowledge representation and evidence combination

This paper deals with knowledge representation of ESEP (Expert System for Earthqauke Prediction). Attending the characteristics of the knowledge in earthquake prediction domain, production representation and procedural representation are connected in the knowledge repesentation model of ESEP named ESEP/K, and three new ways of evidence conbination are proposed for production rules besides 'AND' and 'OR'.

Knowledge representation and decoupling analysis on failure mechanisms of remotely controlled intelligent machinery

Remotely controlled intelligent machinery has complications,including loose manage-ment of failure information,low information availability,and coupling influence among systems,which can be effectively solved by analyzing the system state and information characteristics of the equipment.Taking intelligent agricultural machinery as the object,this study applies the knowledge representation method to explore equipment failure states’informational features and construct a knowledge framework model of system fail-ure representation relations and a complex network conceptual model to visualize the fail-ure information more intuitively and facilitate systematic management and utilization.The feedback-based decoupling analysis method uncouples the coupling between subsys-tems,identifying the critical state of decoupling well.It attempts to apply the knowledge representation and decoupling analysis to remotely controlled intelligent agricultural machinery equipment.Through the example,the result further illustrates the feasibility of knowledge representation and decoupling for remotely controlled intelligent agricultural machinery systems and provides essential support for better failure analysis.

Knowledge Graph Representation Reasoning for Recommendation System

In view of the low interpretability of existing collaborative filtering recommendation algorithms and the difficulty of extracting information from content-based recommendation algorithms,we propose an efficient KGRS model.KGRS first obtains reasoning paths of knowledge graph and embeds the entities of paths into vectors based on knowledge representation learning TransD algorithm,then uses LSTM and soft attention mechanism to capture the semantic of each path reasoning,then uses convolution operation and pooling operation to distinguish the importance of different paths reasoning.Finally,through the full connection layer and sigmoid function to get the prediction ratings,and the items are sorted according to the prediction ratings to get the user’s recommendation list.KGRS is tested on the movielens-100k dataset.Compared with the related representative algorithm,including the state-of-the-art interpretable recommendation models RKGE and RippleNet,the experimental results show that KGRS has good recommendation interpretation and higher recommendation accuracy.

Sentence,Phrase,and Triple Annotations to Build a Knowledge Graph of Natural Language Processing Contributions—A Trial Dataset

Purpose:This work aims to normalize the NLPCONTRIBUTIONS scheme(henceforward,NLPCONTRIBUTIONGRAPH)to structure,directly from article sentences,the contributions information in Natural Language Processing(NLP)scholarly articles via a two-stage annotation methodology:1)pilot stage-to define the scheme(described in prior work);and 2)adjudication stage-to normalize the graphing model(the focus of this paper).Design/methodology/approach:We re-annotate,a second time,the contributions-pertinent information across 50 prior-annotated NLP scholarly articles in terms of a data pipeline comprising:contribution-centered sentences,phrases,and triple statements.To this end,specifically,care was taken in the adjudication annotation stage to reduce annotation noise while formulating the guidelines for our proposed novel NLP contributions structuring and graphing scheme.Findings:The application of NLPCONTRIBUTIONGRAPH on the 50 articles resulted finally in a dataset of 900 contribution-focused sentences,4,702 contribution-information-centered phrases,and 2,980 surface-structured triples.The intra-annotation agreement between the first and second stages,in terms of F1-score,was 67.92%for sentences,41.82%for phrases,and 22.31%for triple statements indicating that with increased granularity of the information,the annotation decision variance is greater.Research limitations:NLPCONTRIBUTIONGRAPH has limited scope for structuring scholarly contributions compared with STEM(Science,Technology,Engineering,and Medicine)scholarly knowledge at large.Further,the annotation scheme in this work is designed by only an intra-annotator consensus-a single annotator first annotated the data to propose the initial scheme,following which,the same annotator reannotated the data to normalize the annotations in an adjudication stage.However,the expected goal of this work is to achieve a standardized retrospective model of capturing NLP contributions from scholarly articles.This would entail a larger initiative of enlisting multiple annotators to accommodate different worldviews into a“single”set of structures and relationships as the final scheme.Given that the initial scheme is first proposed and the complexity of the annotation task in the realistic timeframe,our intraannotation procedure is well-suited.Nevertheless,the model proposed in this work is presently limited since it does not incorporate multiple annotator worldviews.This is planned as future work to produce a robust model.Practical implications:We demonstrate NLPCONTRIBUTIONGRAPH data integrated into the Open Research Knowledge Graph(ORKG),a next-generation KG-based digital library with intelligent computations enabled over structured scholarly knowledge,as a viable aid to assist researchers in their day-to-day tasks.Originality/value:NLPCONTRIBUTIONGRAPH is a novel scheme to annotate research contributions from NLP articles and integrate them in a knowledge graph,which to the best of our knowledge does not exist in the community.Furthermore,our quantitative evaluations over the two-stage annotation tasks offer insights into task difficulty.

Intelligent Multilevel Knowledge Acquisition System for Product Design and Its Implementation

The characteristics of design process, design object and domain knowledge of complex product are analyzed. A kind of knowledge representation schema based on integrated generalized rule is stated. An AND-OR tree based model of concept for domain knowledge is set up. The strategy of multilevel domain knowledge acquisition based on the model is presented. The intelligent multilevel knowledge acquisition system (IMKAS) for product design is developed, and it is applied in the intelligent decision support system of concept design of complex product.

COMPUTER PATTERN DESIGN AND PAINTING TECHNIQUE BASED ON AESTHETICS KNOWLEDGE

Computer Art (CA) is a very important field in computer applications. Based on the analysis and summarization of the painting process, a new method of CA creation using the techniques of com- puter graphics and the expert system is presented in this paper. The reduction of pattern model, simu- lation of special effect, representation of aesthetics knowledge and fuzzy judgement of beauty are includ- ed by this new method.

An Improved Method for Web Text Affective Cognition Computing Based on Knowledge Graph

The goal of research on the topics such as sentiment analysis and cognition is to analyze the opinions,emotions,evaluations and attitudes that people hold about the entities and their attributes from the text.The word level affective cognition becomes an important topic in sentiment analysis.Extracting the(attribute,opinion word)binary relationship by word segmentation and dependency parsing,and labeling those by existing emotional dictionary combined with webpage information and manual annotation,this paper constitutes a binary relationship knowledge base.By using knowledge embedding method,embedding each element in(attribute,opinion,opinion word)as a word vector into the Knowledge Graph by TransG,and defining an algorithm to distinguish the opinion between the attribute word vector and the opinion word vector.Compared with traditional method,this engine has the advantages of high processing speed and low occupancy,which makes up the time-costing and high calculating complexity in the former methods.

Let Some Unforeseen Knowledge Emerge from Heterogeneous Documents

Data production and exchange on the Web grows at a frenetic speed. Such uncontrolled and exponential growth pushes for new researches in the area of information extraction as it is of great interest and can be obtained by processing data gathered from several heterogeneous sources. While some extracted facts can be correct at the origin, it is not possible to verify that correlations among the mare always true (e.g., they can relate to different points of time). We need systems smart enough to separate signal from noise and hence extract real value from this abundance of content accessible on the Web. In order to extract information from heterogeneous sources, we are involved into the entire process of identifying specific facts/events of interest. We propose a gluing architecture, driving the whole knowledge acquisition process, from data acquisition from external heterogeneous resources to their exploitation for RDF trip lification to support reasoning tasks. Once the extraction process is completed, a dedicated reasoner can infer new knowledge as a result of the reasoning process defined by the end user by means of specific inference rules over both extracted information and the background knowledge. The end user is supported in this context with an intelligent interface allowing to visualize either specific data/concepts, or all information inferred by applying deductive reasoning over a collection of data.