Academic literature on the topic 'Concept hierarchies'
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Journal articles on the topic "Concept hierarchies"
Zupan, Blaž, Marko Bohanec, Janez Demšar, and Ivan Bratko. "Learning by discovering concept hierarchies." Artificial Intelligence 109, no. 1-2 (June 1999): 211–42. http://dx.doi.org/10.1016/s0004-3702(99)00008-9.
Full textPetry, Frederick E., and Ronald R. Yager. "Evidence Resolution Using Concept Hierarchies." IEEE Transactions on Fuzzy Systems 16, no. 2 (April 2008): 299–308. http://dx.doi.org/10.1109/tfuzz.2007.895966.
Full textSnelting, Gregor, and Frank Tip. "Understanding class hierarchies using concept analysis." ACM Transactions on Programming Languages and Systems 22, no. 3 (May 2000): 540–82. http://dx.doi.org/10.1145/353926.353940.
Full textSnelting, Gregor, and Frank Tip. "Reengineering class hierarchies using concept analysis." ACM SIGSOFT Software Engineering Notes 23, no. 6 (November 1998): 99–110. http://dx.doi.org/10.1145/291252.288273.
Full textPetry, Frederick E., and Ronald R. Yager. "Fuzzy Concept Hierarchies and Evidence Resolution." IEEE Transactions on Fuzzy Systems 22, no. 5 (October 2014): 1151–61. http://dx.doi.org/10.1109/tfuzz.2013.2286412.
Full textPlantié, Michel, and Michel Crampes. "Visualizing and Interacting with Concept Hierarchies." International Journal on Artificial Intelligence Tools 24, no. 02 (April 2015): 1540006. http://dx.doi.org/10.1142/s0218213015400060.
Full textCimiano, P., A. Hotho, and S. Staab. "Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis." Journal of Artificial Intelligence Research 24 (August 1, 2005): 305–39. http://dx.doi.org/10.1613/jair.1648.
Full textReformat, Marek, Ronald R. Yager, and Zhan Li. "Ontology Enhanced Concept Hierarchies for Text Identification." International Journal on Semantic Web and Information Systems 4, no. 3 (July 2008): 16–43. http://dx.doi.org/10.4018/jswis.2008070102.
Full textLI, DONG (HAOYUAN), ANNE LAURENT, and PASCAL PONCELET. "DISCOVERING FUZZY UNEXPECTED SEQUENCES WITH CONCEPT HIERARCHIES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 17, supp01 (August 2009): 113–34. http://dx.doi.org/10.1142/s0218488509006054.
Full textStriedter, Georg F., and R. Glenn Northcutt. "Biological Hierarchies and the Concept of Homology." Brain, Behavior and Evolution 38, no. 4-5 (1991): 177–89. http://dx.doi.org/10.1159/000114387.
Full textDissertations / Theses on the topic "Concept hierarchies"
Barnes, Christopher A. "Concept hierarchies for extensible databases." Thesis, Monterey, California : Naval Postgraduate School, 1990. http://handle.dtic.mil/100.2/ADA241353.
Full textThesis Advisor(s): Dolk, Daniel R. Second Reader: Bradley, Gordon H. "September 1990." Description based on title screen viewed on December 16, 2009. DTIC Descriptor(s): Systems engineering, semantics, data bases, computers, numerical analysis, efficiency, theses, consistency, interrogation, numbers, hierarchies, sizes(dimensions), data management, dictionaries, measurement. Author(s) subject terms: Database design, data manipulation, semantic networks. Includes bibliographical references (p. 73-74). Also available in print.
Šebek, Michal. "Dolování víceúrovňových sekvenčních vzorů." Doctoral thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2017. http://www.nusl.cz/ntk/nusl-412593.
Full textFaure, Ghislaine. "Planification hierarchisee en avenir incertain : le concept de robustesse." Toulouse 3, 1986. http://www.theses.fr/1986TOU30093.
Full textLaurila, Linda. "Neuropsychology of Semantic Memory: Theories, Models, and Tests." Thesis, University of Skövde, School of Humanities and Informatics, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-67.
Full textSemantic memory is part of the long-term memory system, and there are several theories concerning this type of memory. Some of these will be described in this essay. There are also several types of neuropsychological semantic memory deficits. For example, test results have shown that patients tend to have more difficulties naming living than nonliving things, and one probable explanation is that living things are more dependent on sensory than on functional features. Description of concrete concepts is a new test of semantic memory, in which cueing is used, both to capture the maximum performance of patients, and to give insight into the access versus storage problem. The theoretical ideas and empirical results relating to this new test will be described in detail. Furthermore, other tests of semantic memory that have been commonly used will also be briefly described. In conclusion semantic memory is a complex cognitive system that needs to be studied further.
Malinowski, Gajda Elzbieta. "Designing conventional, spatial, and temporal data warehouses: concepts and methodological framework." Doctoral thesis, Universite Libre de Bruxelles, 2006. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210837.
Full textA data warehouse is a database that allows to store high volume of historical data required for analytical purposes. This data is extracted from operational databases, transformed into a coherent whole, and loaded into a DW during the extraction-transformation-loading (ETL) process.
DW data can be dynamically manipulated using on-line analytical processing (OLAP) systems. DW and OLAP systems rely on a multidimensional model that includes measures, dimensions, and hierarchies. Measures are usually numeric additive values that are used for quantitative evaluation of different aspects about organization. Dimensions provide different analysis perspectives while hierarchies allow to analyze measures on different levels of detail.
Nevertheless, currently, designers as well as users find difficult to specify multidimensional elements required for analysis. One reason for that is the lack of conceptual models for DW and OLAP system design, which would allow to express data requirements on an abstract level without considering implementation details. Another problem is that many kinds of complex hierarchies arising in real-world situations are not addressed by current DW and OLAP systems.
In order to help designers to build conceptual models for decision-support systems and to help users in better understanding the data to be analyzed, in this thesis we propose the MultiDimER model - a conceptual model used for representing multidimensional data for DW and OLAP applications. Our model is mainly based on the existing ER constructs, for example, entity types, attributes, relationship types with their usual semantics, allowing to represent the common concepts of dimensions, hierarchies, and measures. It also includes a conceptual classification of different kinds of hierarchies existing in real-world situations and proposes graphical notations for them.
On the other hand, currently users of DW and OLAP systems demand also the inclusion of spatial data, visualization of which allows to reveal patterns that are difficult to discover otherwise. The advantage of using spatial data in the analysis process is widely recognized since it allows to reveal patterns that are difficult to discover otherwise.
However, although DWs typically include a spatial or a location dimension, this dimension is usually represented in an alphanumeric format. Furthermore, there is still a lack of a systematic study that analyze the inclusion as well as the management of hierarchies and measures that are represented using spatial data.
With the aim of satisfying the growing requirements of decision-making users, we extend the MultiDimER model by allowing to include spatial data in the different elements composing the multidimensional model. The novelty of our contribution lays in the fact that a multidimensional model is seldom used for representing spatial data. To succeed with our proposal, we applied the research achievements in the field of spatial databases to the specific features of a multidimensional model. The spatial extension of a multidimensional model raises several issues, to which we refer in this thesis, such as the influence of different topological relationships between spatial objects forming a hierarchy on the procedures required for measure aggregations, aggregations of spatial measures, the inclusion of spatial measures without the presence of spatial dimensions, among others.
Moreover, one of the important characteristics of multidimensional models is the presence of a time dimension for keeping track of changes in measures. However, this dimension cannot be used to model changes in other dimensions.
Therefore, usual multidimensional models are not symmetric in the way of representing changes for measures and dimensions. Further, there is still a lack of analysis indicating which concepts already developed for providing temporal support in conventional databases can be applied and be useful for different elements composing a multidimensional model.
In order to handle in a similar manner temporal changes to all elements of a multidimensional model, we introduce a temporal extension for the MultiDimER model. This extension is based on the research in the area of temporal databases, which have been successfully used for modeling time-varying information for several decades. We propose the inclusion of different temporal types, such as valid and transaction time, which are obtained from source systems, in addition to the DW loading time generated in DWs. We use this temporal support for a conceptual representation of time-varying dimensions, hierarchies, and measures. We also refer to specific constraints that should be imposed on time-varying hierarchies and to the problem of handling multiple time granularities between source systems and DWs.
Furthermore, the design of DWs is not an easy task. It requires to consider all phases from the requirements specification to the final implementation including the ETL process. It should also take into account that the inclusion of different data items in a DW depends on both, users' needs and data availability in source systems. However, currently, designers must rely on their experience due to the lack of a methodological framework that considers above-mentioned aspects.
In order to assist developers during the DW design process, we propose a methodology for the design of conventional, spatial, and temporal DWs. We refer to different phases, such as requirements specification, conceptual, logical, and physical modeling. We include three different methods for requirements specification depending on whether users, operational data sources, or both are the driving force in the process of requirement gathering. We show how each method leads to the creation of a conceptual multidimensional model. We also present logical and physical design phases that refer to DW structures and the ETL process.
To ensure the correctness of the proposed conceptual models, i.e. with conventional data, with the spatial data, and with time-varying data, we formally define them providing their syntax and semantics. With the aim of assessing the usability of our conceptual model including representation of different kinds of hierarchies as well as spatial and temporal support, we present real-world examples. Pursuing the goal that the proposed conceptual solutions can be implemented, we include their logical representations using relational and object-relational databases.
Doctorat en sciences appliquées
info:eu-repo/semantics/nonPublished
Hu, Chih-Hung, and 胡志弘. "Ontology-based Information Retrieval Using Fuzzy Concept Hierarchies." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/84006543286607161571.
Full text義守大學
資訊工程學系碩士班
94
Ontology is a conceptualization of a knowledge domain into a representation of human readable and machine-readable format. Embedding ontology into information retrieval is to apply knowledge structures to filter and search the relevant information users needed. However, constructing good, correct and complete ontology for a large amount of information documents is very difficult and impossible. We proposed an ontology-based information retrieval model to improve effectiveness of information retrieval. The ontology embedded in the proposed model is a fuzzy concept hierarchy generated automatically from the documents. Based on the proposed model, we also implement an ontology-based information retrieval system. An ontology used in the system include general semantic ontology, domain knowledge and automatic generated fuzzy concept hierarchy. We also estimate the system follow the evaluation method of CLEF’04. According to the experimental result, the embedded ontology indeed improved the performance of information retrieval.
Liao, Su-Yu, and 廖素玉. "Mining Concept Hierarchies based on Rough Entropy for Large Categorical Databases." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/43598580216260904695.
Full text義守大學
資訊工程學系
91
Knowledge discovery from a large database is an important research topic of late years. A concept hierarchy is a kind of concise and general form of concept description that organizes relationships of data and expresses knowledge in databases. In this thesis, we propose the approaches to generate concept hierarchies automatically for a given data set with nominal attributes. The proposed methods are first based on rough entropy to generate a coarse concept hierarchy with strong concept level for the set of attributes. Then, for obtaining a finer concept hierarchy, we develop refinement algorithms to generate weak concepts among strong concept levels. Since the number of attributes will be reduced after each strong concept level is generated, the number of scanning data sets is decreasing and the efficiency is improved. The experiments use several well-known datasets selected from UCI data repository to evaluate the feasibility and performance of the proposed methods. The experimental results show that the proposed approaches not only can mine important and meaningful concept hierarchies effectively but also have lower time complexity in comparison with previous researches. The resultant hierarchy structures can thus be applied for supporting knowledge discovery in large databases.
Hsu, Tzuyu, and 徐梓育. "User Profiling Based on the Concept Hierarchies Derived from Search Engine Queries." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/74456915271145369955.
Full text輔仁大學
資訊管理學系
99
User profiling in recent years become increasingly interested in the rise of research in the field of web data mining issues. In the past, the building method is that extract the words that represent user interests from user’s browser or search history, and then find out the similarity between words by other words. However, in the past, the statistic-based method requires a lot of file contents to find the file keyword with related terms. For calculating the word similarity, we also need a large number of calculations. Furthermore, if we calculate terms similarity with statistic-based approach and calculate the interest strength, we need to consider the word itself will be affected by the strength of the statistical properties. According to the background of user profiling in web data mining field and the motivation for improvement of traditional building methods, this study take advantage of the web snippet which returned by web search engines, Chinese grammar and the word appearance to build a hierarchical network. Then consider the distance of words that in hierarchical network to calculate the interest strength. In order to solve the problem of traditional approach of user profiling in statistic-based methods and improve the quality of user profile for personalized search, recommendations and other personalized applications. Experiments show that our approach can build the user profile which average interest strength higher than the one build by traditional method. For execution time, our approach required significantly less time than traditional method.
Gelnarová, Jitka. "Právo i dobro Argumentace a diskurs českých aktivistek za volební právo pro ženy." Doctoral thesis, 2013. http://www.nusl.cz/ntk/nusl-327219.
Full textChun, Hsu Fang, and 許芳郡. "Concepts Hierarchies Based on S-P Chart and Polytomous Ordering Theory with Application in Fraction Addition and Subtraction." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/54990143152618541727.
Full text國立臺中教育大學
教育測驗統計研究所
98
The purpose of this study is to integrate the S-P chart and polytomous ordering theory in analyzing the concepts of fraction addition and subtraction for fifth and sixth graders. Teachers can acquire the information of students-learning by the hierarchical structures of items provided by the caution index of problems and the caution index of students from S-P chart and polytomous ordering theory. This study shows that the hierarchical structures of concepts on various types students are different. There are type A, type B, and type C which exist exactly hierarchical structure of concepts. Unlike type A, type B, and type C, it exists a lot of variations in type A', type B', and type C' to bring about many differences on hierarchical structure of concepts. The integrated analysis of S-P chart and polytomous ordering theory can not only present the different concepts hierarchies based on various types students but also provide unabridged information of students-learning to deal with individual differences. The findings of this study could provide references in cognitive diagnosis and remedial instruction.
Books on the topic "Concept hierarchies"
De Zordo, Ornella, and Fiorenzo Fantaccini, eds. altri canoni / canoni altri. Florence: Firenze University Press, 2011. http://dx.doi.org/10.36253/978-88-6453-012-3.
Full textHierarchic electrodynamics and free electron lasers: Concepts, calculations, and practical applications. Boca Raton, FL: Taylor & Francis, 2011.
Find full textChristopher, Kennard, Swash Michael, Association of British Neurologists, and American Neurological Association, eds. Hierarchies in neurology: A reappraisal of a Jacksonian concept. London: Springer-Verlag, 1989.
Find full textKennard, Christopher. Hierarchies in Neurology: A Reappraisal Of A Jacksonian Concept. Springer, 2011.
Find full textKennard, Christopher. Hierarchies in Neurology: A Reappraisal of a Jacksonian Concept (Clinical Medicine and the Nervous System). Springer-Verlag, 1989.
Find full textShepherd, Laura J. The Concept and Practice of Peacebuilding at the UN and Beyond. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199982721.003.0002.
Full textBell, Daniel, and Wang Pei. Just Hierarchy. Princeton University Press, 2020. http://dx.doi.org/10.23943/princeton/9780691200897.001.0001.
Full textPerry, Imani. The Flowers Are Vexed. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190456368.003.0015.
Full textAlkemeyer, Thomas, Kristina Brümmer, and Thomas Pille. Intercorporeality at the Motor Block. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190210465.003.0008.
Full textBateman, Benjamin. Forster’s Queer Invitation. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190676537.003.0004.
Full textBook chapters on the topic "Concept hierarchies"
Wille, Rudolf. "Formal Concept Analysis as Mathematical Theory of Concepts and Concept Hierarchies." In Formal Concept Analysis, 1–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11528784_1.
Full textDi Beneditto, Marco Eugênio Madeira, and Leliane Nunes de Barros. "Using Concept Hierarchies in Knowledge Discovery." In Advances in Artificial Intelligence – SBIA 2004, 255–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28645-5_26.
Full textWall, Bob, Neal Richter, and Rafal Angryk. "Generating Concept Hierarchies from User Queries." In Data Mining: Foundations and Practice, 423–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78488-3_25.
Full textIba, Wayne, and Pat Langley. "Unsupervised Learning of Probabilistic Concept Hierarchies." In Machine Learning and Its Applications, 39–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44673-7_3.
Full textWille, Rudolf. "RESTRUCTURING LATTICE THEORY: AN APPROACH BASED ON HIERARCHIES OF CONCEPTS." In Formal Concept Analysis, 314–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01815-2_23.
Full textSen, Soumya, Agostino Cortesi, and Nabendu Chaki. "Generating Co-operative Queries Over Concept Hierarchies." In Hyper-lattice Algebraic Model for Data Warehousing, 35–59. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28044-8_3.
Full textStuckenschmidt, Heiner, and Michel Klein. "Structure-Based Partitioning of Large Concept Hierarchies." In The Semantic Web – ISWC 2004, 289–303. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30475-3_21.
Full textHitzler, Pascal. "Default Reasoning over Domains and Concept Hierarchies." In KI 2004: Advances in Artificial Intelligence, 351–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30221-6_27.
Full textKanzaki, Kyoko, Qing Ma, Eiko Yamamoto, Tamotsu Shirado, and Hitoshi Isahara. "Acquiring Concept Hierarchies of Adjectives from Corpora." In Computer Processing of Oriental Languages. Beyond the Orient: The Research Challenges Ahead, 430–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11940098_45.
Full textNica, Cristina, Agnès Braud, and Florence Le Ber. "Hierarchies of Weighted Closed Partially-Ordered Patterns for Enhancing Sequential Data Analysis." In Formal Concept Analysis, 138–54. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59271-8_9.
Full textConference papers on the topic "Concept hierarchies"
Aguinaga, Salvador, Aditya Nambiar, Zuozhu Liu, and Tim Weninger. "Concept hierarchies and human navigation." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363739.
Full textSanderson, Mark, and Bruce Croft. "Deriving concept hierarchies from text." In the 22nd annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 1999. http://dx.doi.org/10.1145/312624.312679.
Full textSnelting, Gregor, and Frank Tip. "Reengineering class hierarchies using concept analysis." In the 6th ACM SIGSOFT international symposium. New York, New York, USA: ACM Press, 1998. http://dx.doi.org/10.1145/288195.288273.
Full textAsiki, Athanasia, Katerina Doka, Dimitrios Tsoumakos, and Nectarios Koziris. "Support for Concept Hierarchies in DHTs." In 2008 Eighth International Conference on Peer-to-Peer Computing (P2P). IEEE, 2008. http://dx.doi.org/10.1109/p2p.2008.26.
Full textCrampes, Michel, and Michel Plantié. "Visualizing and Interacting with Concept Hierarchies." In the 4th International Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2611040.2611046.
Full textEckert, Kai, Mathias Niepert, Christof Niemann, Cameron Buckner, Colin Allen, and Heiner Stuckenschmidt. "Crowdsourcing the assembly of concept hierarchies." In the 10th annual joint conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1816123.1816143.
Full textZhou, Wen, Zongtian Liu, and Yan Zhao. "Concept Hierarchies Generation for Classification using Fuzzy Formal Concept Analysis." In Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007). IEEE, 2007. http://dx.doi.org/10.1109/snpd.2007.229.
Full textTsay, Li-Shiang, and Seunghyun Im. "Mining Generalized Actionable Rules Using Concept Hierarchies." In 2009 Fifth International Joint Conference on INC, IMS and IDC. IEEE, 2009. http://dx.doi.org/10.1109/ncm.2009.410.
Full textXavier, Sofia Francis, Lakshmi Priyanka Selvaraj, and Vidhya Balasubramanian. "Enhancing statistical semantic networks with concept hierarchies." In 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2015. http://dx.doi.org/10.1109/icacci.2015.7275792.
Full textHuchard, Marianne. "Analyzing inheritance hierarchies through Formal Concept Analysis." In ECOOP '15: European Conference on Object-Oriented Programming ECOOP 2015. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2786555.2786557.
Full textReports on the topic "Concept hierarchies"
Sanderson, Mark, and Bruce Croft. Deriving Concept Hierarchies From Text. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada439413.
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