Knowledge Representation and Semantic Inference of Process Based on Ontology and Semantic Web Rule Language

The process inference cannot be achieved effectively by the traditional expert system,while the ontology and semantic technology could provide better solution to the knowledge acquisition and intelligent inference of expert system.The application mode of ontology and semantic technology on the process parameters recommendation are mainly investigated.Firstly,the content about ontology,semantic web rule language(SWRL)rules and the relative inference engine are introduced.Then,the inference method about process based on ontology technology and the SWRL rule is proposed.The construction method of process ontology base and the writing criterion of SWRL rule are described later.Finally,the results of inference are obtained.The mode raised could offer the reference to the construction of process knowledge base as well as the expert system's reusable process rule library.

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.

Configuration knowledge modeling of customizable products based on semantic web technologies

In order to solve the problem of modeling product configuration knowledge at the semantic level to successfully implement the mass customization strategy, an approach of ontology-based configuration knowledge modeling, combining semantic web technologies, was proposed. A general configuration ontology was developed to provide a common concept structure for modeling configuration knowledge and rules of specific product domains. The OWL web ontology language and semantic web rule language （SWRL） were used to formally represent the configuration ontology, domain configuration knowledge and rules to enhance the consistency, maintainability and reusability of all the configuration knowledge. The configuration knowledge modeling of a customizable personal computer family shows that the approach can provide explicit, computerunderstandable knowledge semantics for specific product configuration domains and can efficiently support automatic configuration tasks of complex products.

A Design Method with Knowledge Modeling for Complex Product Based on Ontology

Innovation design for complex products is a difficult issue in the military manufacturing industry. Ontology may provide a feasible way to rebuild the design process for complex products via sharing and reusing of design knowledge. In this paper, a design method used in the innovation design process of the complex products with knowledge modeling is proposed. Knowledge modeling is realized through ontology construction by combining the cycling evolutional theory of constructing ontology and OWL (Ontology Web Language)-based knowledge representation. As a case study, the satellite is selected as one of the complex products. The application domain of the satellite ontology is analyzed. According to the analysis result, the knowledge structure of satellite ontology is put forward based on OWL. The application of satellite product design shows that the method can effectively organize and reuse the knowledge resources in the innovation design of complex product and help companies to create more competitive products based on the existing knowledge and experience.

A New Method of Semantic Network Knowledge Representation Based on Extended Petri Net

Abstract： It was discussed that the way to reflect the internal relations between judgment and identification, the two most fundamental ways of thinking or cognition operations, during the course of the semantic network knowledge representation processing. A new extended Petri net is defined based on qualitative mapping, which strengths the expressive ability of the feature of thinking and the mode of action of brain. A model of semantic network knowledge representation based on new Petri net is given. Semantic network knowledge has a more efficient representation and reasoning mechanism. This model not only can reflect the characteristics of associative memory in semantic network knowledge representation, but also can use Petri net to express the criterion changes and its change law of recognition judgment, especially the cognitive operation of thinking based on extraction and integration of sensory characteristics to well express the thinking transition course from quantitative change to qualitative change of human cognition.

Knowledge-Enhanced Bilingual Textual Representations for Cross-Lingual Semantic Textual Similarity

Joint learning of words and entities is advantageous to various NLP tasks, while most of the works focus on single language setting. Cross-lingual representations learning receives high attention recently, but is still restricted by the availability of parallel data. In this paper, a method is proposed to jointly embed texts and entities on comparable data. In addition to evaluate on public semantic textual similarity datasets, a task (cross-lingual text extraction) was proposed to assess the similarities between texts and contribute to this dataset. It shows that the proposed method outperforms cross-lingual representations methods using parallel data on cross-lingual tasks, and achieves competitive results on mono-lingual tasks.

Semantic web-based networked manufacturing knowledge retrieval system

To deal with a lack of semantic interoperability of traditional knowledge retrieval approaches, a semantic-based networked manufacturing （NM） knowledge retrieval architecture is proposed, which offers a series of tools for supporting the sharing of knowledge and promoting NM collaboration. A 5-tuple based semantic information retrieval model is proposed, which includes the interoperation on the semantic layer, and a test process is given for this model. The recall ratio and the precision ratio of manufacturing knowledge retrieval are proved to be greatly improved by evaluation. Thus, a practical and reliable approach based on the semantic web is provided for solving the correlated concrete problems in regional networked manufacturing.

Product-design knowledge retrieval based on ontology

In order to improve the utilization ratio of knowledge retrieval, a product-design knowledge retrieval approach based on ontology is proposed. A representation model of product-design knowledge is proposed according to its characteristics. Domain ontology of product-design is estab- lished and the semantic annotation technology is used to connect the design knowledge and ontolo- gy. A new semantic annotation format is developed and semantic information of the design knowl- edge is enriched by making use of ontology. On that basis a retrieval algorithm is designed for semantic retrieval. Finally, this approach is used in a knowledge management system for military-vehi- cle design and its effectiveness and feasibility are validated. Results show that the recall ratio and the precision ratio of knowledge retrieval are improved greatly and users＇ requirements in semantic retrieval are satisfied.

Knowledge acquisition, semantic text mining, and security risks in health and biomedical informatics

Computational techniques have been adopted in medi-cal and biological systems for a long time. There is no doubt that the development and application of computational methods will render great help in better understanding biomedical and biological functions. Large amounts of datasets have been produced by biomedical and biological experiments and simulations. In order for researchers to gain knowledge from origi- nal data, nontrivial transformation is necessary, which is regarded as a critical link in the chain of knowledge acquisition, sharing, and reuse. Challenges that have been encountered include: how to efficiently and effectively represent human knowledge in formal computing models, how to take advantage of semantic text mining techniques rather than traditional syntactic text mining, and how to handle security issues during the knowledge sharing and reuse. This paper summarizes the state-of-the-art in these research directions. We aim to provide readers with an introduction of major computing themes to be applied to the medical and biological research.

World-Wide Semantic Web of Agriculture Knowledge

The rapid increase in the publication of knowledge bases as linked open data （LOD） warrants serious consideration from all concerned, as this phenomenon will potentially scale exponentially. This paper will briefly describe the evolution of the LOD, the emerging world-wide semantic web （WWSW）, and explore the scalability and performance features Of the service oriented architecture that forms the foundation of the semantic technology platform developed at MIMOS Bhd., for addressing the challenges posed by the intelligent future internet. This paper＂ concludes with a review of the current status of the agriculture linked open data.

ALBERT with Knowledge Graph Encoder Utilizing Semantic Similarity for Commonsense Question Answering

Recently,pre-trained language representation models such as bidirec-tional encoder representations from transformers(BERT)have been performing well in commonsense question answering(CSQA).However,there is a problem that the models do not directly use explicit information of knowledge sources existing outside.To augment this,additional methods such as knowledge-aware graph network(KagNet)and multi-hop graph relation network(MHGRN)have been proposed.In this study,we propose to use the latest pre-trained language model a lite bidirectional encoder representations from transformers(ALBERT)with knowledge graph information extraction technique.We also propose to applying the novel method,schema graph expansion to recent language models.Then,we analyze the effect of applying knowledge graph-based knowledge extraction techniques to recent pre-trained language models and confirm that schema graph expansion is effective in some extent.Furthermore,we show that our proposed model can achieve better performance than existing KagNet and MHGRN models in CommonsenseQA dataset.

Development of an Ontology-Based Knowledge Network by Interconnecting Soil/Water Concepts/Properties, Derived from Standards Methods and Published Scientific References Outlining Infiltration/Percolation Process of Contaminated Water

The present work deals with the development of an Ontology-Based Knowledge Network of soil/water physicochemical & biological properties (soil/water concepts), derived from ASTM Standard Methods (ASTMi,n) and relevant scientific/applicable references (published papers—PPi,n) to fill up/bridge the gap of the information science between cited Standards and infiltration discipline conceptual vocabulary providing accordingly a dedicated/internal Knowledge Base (KB). This attempt constitutes an innovative approach, since it is based on externalizing domain knowledge in the form of Ontology-Based Knowledge Networks, incorporating standardized methodology in soil engineering. The ontology soil/water concepts (semantics) of the developed network correspond to soil/water physicochemical & biological properties, classified in seven different generations that are distinguished/located in infiltration/percolation process of contaminated water through soil porous media. The interconnections with arcs between corresponding concepts/properties among the consecutive generations are defined by the relationship of dependent and independent variables. All these interconnections are documented according to the below three ways: 1) dependent and independent variables interconnected by using the logical operator “depends on” quoting existent explicit functions and equations;2) dependent and independent variables interconnected by using the logical operator “depends on” quoting produced implicit functions, according to Rayleigh’s method of indices;3) dependent and independent variables interconnected by using the logical operator “related to” based on a logical dependence among the examined nodes-concepts-variables. The aforementioned approach provides significant advantages to semantic web developers and web users by means of prompt knowledge navigation, tracking, retrieval and usage.

Product Knowledge Representation and Integration Technology in Web-based Collaborative Design

Because of the complexity of modern product design,the web-based collaborative product design aroused considerable attention of manufacturers in the last few years with the development of Internet technology. But it is still hardly achievable due to the difficulty to share product knowledge from different designers and systems. In this paper,we firstly create an ontology-based product model,which consists of PPR (Product,Process and Resource) concept models and PPR characteristic models,to describe product knowledge. Afterwards,how to represent the model in XML is discussed in detail. Then the mechanism of product knowledge collection and integration from different application systems based on interface agents is introduced. At last,a web-based open-architecture product knowledge integrating and sharing prototype system AD-HUB is developed. An example is also given and it shows that the theory discussed in this paper is efficient to represent and integrate product knowledge in web-based collaborative design processes.

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 rule-based solution system for dynamic programming model

A knowledge representation has been proposed using the state space theory of Artificial Intelligence for Dynamic Programming Model, in which a model can be defined as a six tuple M=(I,G,O,T,D,S). A building block modeling method uses the modules of a six tuple to form a rule based solution model. Moreover, a rule based system has been designed and set up to solve the Dynamic Programming Model. This knowledge based representation can be easily used to express symbolical knowledge and dynamic characteristics for Dynamic Programming Model, and the inference based on the knowledge in the process of solving Dynamic Programming Model can also be conveniently realized in computer.

Activity Theory Based Ontology Model for Efficient Knowledge Sharing in eHealth

Knowledge sharing has become an important issue that challenges the efficient healthcare delivery in eHealth system. It also rises as one of the most demanding applications with reference to dynamic interactivities among various healthcare actors (e.g. doctors, nurses, patients, relatives of patients). In this paper, we suggest an activity theory based ontology model to represent various healthcare actors. The goal of the suggested model is to enhance interactivities among these healthcare actors for conducting more efficient knowledge sharing, which helps to design eHealth system. To validate the feasibility of suggested ontology model, three typical use cases are further studied. A questionnaire based survey is carried out and the corresponding survey results are reported, together with the detailed discussions.

Expression model for multiple relationships in the ontology of traditional Chinese medicine knowledge

Objective:To explore multiple relationships in traditional Chinese medicine(TCM)knowledge by comparing binary and multiple relationships during knowledge organization.Methods:Characteristics of binary and multiple semantic relationships as well as their associations are described.A method to classify multiple relationships based on the involvement of time is proposed and theoretically validated using examples from the ancient TCM classic Important Formulas Worth a Thousand Gold Pieces.The classification includes parallel multiple relationships,restricted multiple relationships,multiple relationships that involve time,and multiple relationships that involve time restriction.Next,construction of multiple semantic relationships for TCM concepts in each classification using Protege,an ontology editing tool is described.Results:Protege is superior to a binary relationship and less than ideal with multiple relationships during the constitution of concept relationships.Conclusion:When applied in TCM,the semantic relationships constructed by Protege are superior than those constructed by correlation and/or attribute relationships,but less ideal than those constructed by the human cognitive process.

Knowledge Formalization about Perforated Stomach Ulcer on the Basis of Medical Ontology

The article defines knowledge about pelforated stomach ulcer that is formalized on the basis of an ontology model of medical diagnostics domain. The paper describes a base of observations for the disease and also knowledge base which determines a clinical presentation of the disease. The dependences on courses of the disease and process localization are taken into account during knowledge formalizing. The base of knowledge for the disease has the structure that is conventional for contemporary medicine. These knowledge will be used for building a medical intellectual system of for consulting and diagnostics.

Semantic model and optimization of creative processes at mathematical knowledge formation

The aim of this work is mathematical education through the knowledge system and mathematical modeling. A net model of formation of mathematical knowledge as a deductive theory is suggested here. Within this model the formation of deductive theory is represented as the development of a certain informational space, the elements of which are structured in the form of the orientated semantic net. This net is properly metrized and characterized by a certain system of coverings. It allows injecting net optimization parameters, regulating qualitative aspects of knowledge system under consideration. To regulate the creative processes of the formation and realization of mathematical know- edge, stochastic model of formation deductive theory is suggested here in the form of branching Markovian process, which is realized in the corresponding informational space as a semantic net. According to this stochastic model we can get correct foundation of criterion of optimization creative processes that leads to “great main points” strategy (GMP-strategy) in the process of realization of the effective control in the research work in the sphere of mathematics and its applications.

Enhancing Domain Knowledge with Semantic Models of Web Documents

The paper considers the problem of semantic processing of web documents by designing an approach, which combines extracted semantic document model and domain- related knowledge base. The knowledge base is populated with learnt classification rules categorizing documents into topics. Classification provides for the reduction of the dimensio0ality of the document feature space. The semantic model of retrieved web documents is semantically labeled by querying domain ontology and processed with content-based classification method. The model obtained is mapped to the existing knowledge base by implementing inference algorithm. It enables models of the same semantic type to be recognized and integrated into the knowledge base. The approach provides for the domain knowledge integration and assists the extraction and modeling web documents semantics. Implementation results of the proposed approach are presented.