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A Model Approach to Infer the Quality in Agricultural Sprayers Supported by Knowledge Bases and Experimental Measurements

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INTERNATIONAL JOURNAL OF semantic computing 2017年第3期11卷 279-292页

作者： Penaloza, Elmer A. G. Cruvinel, Paulo E. Oliveira, Vilma A. Costa, Augusto G. F. Univ Sao Paulo Dept Elect Engn & Comp Sao Carlos SP Brazil Brazilian Agr Res Corp Embrapa Instrumentat Sao Carlo SP Brazil Brazilian Agr Res Corp Embrapa Cotton Campina Grande Paraiba Brazil

This paper presents a method to infer the quality of sprayers based on data collection of the drop spectra and their physical descriptors, which are used to generate a knowledge base to support decision-making in agriculture. The knowledge base is formed by collected experimental data, obtained in a controlled environment under specific operating conditions, and the semantics used in the spraying process to infer the quality in the application. The electro-hydraulic operating conditions of the sprayer system, which include speed and flow measurements, are used to define experimental tests, perform calibration of the spray booms and select the nozzle types. Using the Grubbs test and the quartile-quartile plot an exploratory analysis of the collected data was made in order to determine the data consistency, the deviation of atypical values, the independence between the data of each test, the repeatability and the normal representation of them. Therefore, integrating measurements to a knowledge base it was possible to improve the decision-making in relation to the quality of the spraying process defined in terms of a distribution function. Results shown that the use of advanced models and semantic interpretation improved the decision-making processes related to the quality of the agricultural sprayers.

关键词： Advanced modeling semantic computing quality of agricultural application drop size distribution average diameters agricultural sprayers

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semantic Technologies for semantic Applications. Part 2. Models of Comparative Text semantics

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SCIENTIFIC AND TECHNICAL INFORMATION PROCESSING 2020年第6期47卷 365-373页

作者： Gorodetsky, V., I Tushkanova, O. N. St Petersburg State Electrotech Univ LETI St Petersburg Russia Russian Acad Sci St Petersburg Fed Res Ctr St Petersburg Russia

Both parts of this paper discuss the basic aspects of semantic computing, semantic technologies, and semantic applications applied to NL-text big data processing for knowledge extracting and decision making. The basic components of the corresponding systems and technologies are reviewed, which include ontologies and semantic models of their use, semantic resources, and semantic component. The semantic resources contain knowledge about the semantics and means for refinement of this semantics. The semantic component of the technology is used to formally describe the meaning of NL-entities and numerically evaluate their pairwise semantic similarity. The main focus of this part is on numerical models of pairwise semantic similarity of NL-entities. These models are important for solving tasks of text semantic clustering and classification and their various applications. Various types of semantic relatedness and semantic similarity measures for NL-entities in the context of semantic computing tasks are discussed and compared. Problems that constrain the practical use of semantic technologies for the development of semantic applications are analyzed.

关键词： semantic technology semantic computing semantic resource comparative semantics semantic relatedness semantic similarity

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USING SCDL FOR INTEGRATING TOOLS AND DATA FOR COMPLEX BIOMEDICAL APPLICATIONS

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INTERNATIONAL JOURNAL OF semantic computing 2008年第2期2卷 291-308页

作者： Wang, Shu Hu, Rouh-Mei Hsiao, Han C. W. Hecht, David A. Ng, Ka-Lok Chen, Rong-Ming Sheu, Phillip C-Y. Tsai, Jeffrey J. P. Univ Calif Irvine Dept Elect Engn & Comp Sci Irvine CA 92717 USA Asia Univ Dept Biotechnol Taichung Taiwan Asia Univ Dept Bioinformat Taichung Taiwan Southwestern Coll Dept Chem Chula Vista CA USA Natl Univ Tainan Dept Comp Sci & Informat Engn Tainan Taiwan Univ Illinois Dept Comp Sci Chicago IL USA

Current bioinformatics tools or databases are very heterogeneous in terms of data formats, database schema, and terminologies. Additionally, most biomedical databases and analysis tools are scattered across different web sites making interoperability across such different services more difficult. It is desired that these diverse databases and analysis tools be normalized, integrated and encompassed with a semantic interface such that users of biological data and tools could communicate with the system in natural language and a workflow could be automatically generated and distributed into appropriate tools. In this paper, the Biosemantic System is presented to bridge complex biological/ biomedical research problems and computational solutions via semantic computing. Due to the diversity of problems in various research fields, the semantic capability description language (SCDL) plays an important role as a common language and generic form for problem formalization. Several queries as well as their corresponding SCDL descriptions are provided as examples. For complex applications, multiple SCDL queries may be connected via control structures. For these cases, we present an algorithm to map a user request to one or more existing services if they exist.

关键词： semantic computing semantic capability description language biomedical applications

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Towards semantic Biomedical Problem Solving

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INTERNATIONAL JOURNAL OF semantic computing 2015年第4期9卷 415-431页

作者： Wang, Charles C. N. Sheu, Phillip C. -Y. Tsai, Jeffrey J. P. Asia Univ Dept Biomed Informat 500 Lioufeng Rd Taichung 41354 Taiwan Univ Calif Irvine Dept Elect Engn & Comp Sci Irvine CA 92697 USA Asia Univ Dept Biomed Informat Taichung 41354 Taiwan

Biological and medical intelligence (BMI) has been studied in solos, lacking a systematic methodology. In this paper, we describe how semantic computing can enhance biological and medical intelligence. Specifically, we show how Structured Natural Language (SNL) can express many problems in BMI with a finite number of sentence patterns, and show how biological tools, OLAP, data mining tools and statistical analysis tools may be linked to solve problems related to biomedical data.

关键词： Biomedical intelligence semantic computing structured natural language

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SCMR:a semantic-based coherence micro-cluster recognition algorithm for hybrid web data stream

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High Technology Letters 2016年第2期22卷 224-232页

作者：王珉 Wang Yongbin Li Ying School of Computer Communication University of China

Data aggregation from various web sources is very significant for web data analysis domain. In ad- dition, the recognition of coherence micro cluster is one of the most interesting issues in the field of data aggregation. Until now, many algorithms have been proposed to work on this issue. However, the deficiency of these solutions is that they cannot recognize the micro-cluster data stream accurately. A semantic-based coherent micro-cluster recognition algorithm for hybrid web data stream is ***, an objective function is proposed to recognize the coherence micro-cluster and then the coher- ence micro-cluster recognition algorithm for hybrid web data stream based on semantic is raised. Fi-

关键词： hybrid web data stream coherence micro-clustering entity unified object coher-ence semantic computing

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semantic Technologies for semantic Applications. Part 1. Basic Components of semantic Technologies

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SCIENTIFIC AND TECHNICAL INFORMATION PROCESSING 2019年第5期46卷 306-313页

作者： Gorodetsky, V. I. Tushkanova, O. N. Russian Acad Sci St Petersburg Inst Informat & Automat St Petersburg 199178 Russia Peter Great St Petersburg Polytech Univ St Petersburg 195251 Russia

This paper discusses the basic aspects of the modern understanding of semantic computations, semantic technologies, and semantic applications in the field of artificial intelligence. The basic terminology accepted in the work is introduced and specific examples of semantic applications, including industrial-level ones, are given. The paper demonstrates that the basic components of semantic technologies of artificial intelligence are ontologies and semantic models of their use, semantic resources, and the semantic component of the technology. The semantic resources contain information about the semantics of words and other entities, as well as means of refinement of these semantics. The semantic component is used to create formal descriptions of the meanings of natural language entities and numerically evaluate their pairwise semantic similarity. The available semantic resources are discussed and a comparative analysis of them is given. Information on natural language entity types (primitives) is given and then used for the practical purposes of building models of formal description of the meaning of texts in various semantic applications. The latter components of description of text semantics constitute the contents of the second part of this paper.

关键词： natural language semantics semantic technology semantic computing semantic application ontology semantic resource

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Programming Languages For Interactive computing

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ELECTRONIC NOTES IN THEORETICAL COMPUTER SCIENCE 2008年第3期203卷 35-52页

作者： Perera, Roly Univ Birmingham Sch Comp Sci Birmingham W Midlands England

Traditional programming languages are algorithmic: they are best suited to writing programs that acquire all their inputs before executing and only produce a result on termination. By contrast most applications are interactive: they maintain ongoing interactions with their environments. Modern systems address this incompatibility by manually extending the execution model of the host language to support interaction, usually by embedding an event-driven state management scheme which executes fragments of imperative code in response to interactions, the job of each executed fragment being to restore the internal consistency of the computation. The downside of this approach to interaction is that it relies heavily on mutable stores and side-effects and mixes application logic in with behaviour which is more properly the responsibility of an execution model. I describe a programming model called declarative interaction which supports interaction directly. The distinguishing feature of the model is its modal construal of state and interaction.

关键词： interaction modal languages incremental computation retroactive data structures declarative concurrency transactional concurrency persistent computation semantic computing interactive programming languages

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ON CONCEPT ALGEBRA FOR computing WITH WORDS (CWW)

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INTERNATIONAL JOURNAL OF semantic computing 2010年第3期4卷 331-356页

作者： Wang, Yingxu Univ Calgary Dept Elect & Comp Engn 2500 Univ Dr NW Calgary AB T2N 1N4 Canada

computing with words (CWW) is an intelligent computing methodology for processing words, linguistic variables, and their semantics, which mimics the natural-language-based reasoning mechanisms of human beings in soft computing, semantic computing, and cognitive computing. The central objects in CWW techniques are words and linguistic variables, which may be formally modeled by abstract concepts that are a basic cognitive unit to identify and model a concrete entity in the real world and an abstract object in the perceived world. Therefore, concepts are the most fundamental linguistic entities that carries certain meanings in expression, thinking, reasoning, and system modeling, which may be formally modeled as an abstract and dynamic mathematical structure in denotational mathematics. This paper presents a formal theory for concept and knowledge manipulations in CWW known as concept algebra. The mathematical models of abstract and concrete concepts are developed based on the object-attributerelation (OAR) theory. The formal methodology for manipulating knowledge as a concept network is described. Case studies demonstrate that concept algebra provides a generic and formal knowledge manipulation means, which is capable of dealing with complex knowledge and their algebraic operations in CWW.

关键词： computing with word concept algebra semantic computing denotational mathematics cognitive informatics concept network knowledge representation OAR model soft computing cognitive computing computational intelligence case studies

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WeiboFinder: A Topic-Based Chinese Word Finding and Learning System 16th

WeiboFinder: A Topic-Based Chinese Word Finding and Learning...

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16th Annual International Conference on Web-Based Learning (ICWL)

作者： Chen, Wenhao Cai, Yi Lai, Kinkeung Yao, Li Zhang, Jun Li, Jingjing Jia, Xingdong City Univ Hong Kong Dept Management Sci Kowloon Tong Hong Kong Peoples R China South China Univ Technol Sch Software Engn Guangzhou Peoples R China Shaanxi Normal Univ Int Business Sch Xian Peoples R China Beijing Normal Univ Coll Informat Sci & Technol Beijing Peoples R China South China Univ Technol Sch Comp Sci Guangzhou Peoples R China South China Normal Univ Guangzhou Peoples R China Shenzhen Polytech Shenzhen Peoples R China

ISBN: (纸本)9783319667331;9783319667324

With the explosive growth of user-generated data in social media websites such as Twitter and Weibo, a lot of research has been conducted on using user-generated data for web-based learning. Finding users' desired data in an effective way is critical for language learners. Social media websites provide diversified data for language learners and some new words such as cyberspeak could only be learned in these online communities. In this paper, we present a system called WeiboFinder to suggest topic-based words and documents related to a target word for Chinese learners. All the words and documents are from the Chinese social media website: Weibo. Weibo is one of the largest microblog social meida websites in China which has similar functions as Twitter. The experimental results show that the proposed method is effective and better than other methods. The topics from our method are more interpretable and topic-based words are useful for Chinese learners.

关键词： Social media Chinese learning Topic modeling semantic computing Text mining

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Soft Sensors for Social Sensing in Cultural Heritage 2

Soft Sensors for Social Sensing in Cultural Heritage

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Digital Heritage International Congress

作者： Pilato, Giovanni Maniscalco, Umberto Italian Natl Res Council ICAR CNR Viale SciEd 11 I-90128 Palermo Italy

ISBN: (纸本)9781509000487

This paper presents a work in progress concerning a soft sensor approach for social sensing in the context of cultural heritage. The approach analyzes public posts and comments on the British Museum Facebook page and tries to give an overall measurement regarding the sentiment and the emotions arising from a post. This can help museums to better address their resources in order to improve the effectiveness of their divulgation action.

关键词： Soft sensors social sensing opinion mining semantic computing

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