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Journal articles on the topic 'Bases of systems of knowledge'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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1

Subrahmanian, V. S. "Amalgamating knowledge bases." ACM Transactions on Database Systems 19, no. 2 (1994): 291–331. http://dx.doi.org/10.1145/176567.176571.

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2

Šilerová, E., and L. Kučírková. "Knowledge and information systems." Agricultural Economics (Zemědělská ekonomika) 54, No. 5 (2008): 217–23. http://dx.doi.org/10.17221/245-agricecon.

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We can see that the today’s management level of informations is not quite fully controlled in most businesses. Nowadays generation of information systems creates support for business processes with the aim of optimalization of these processes on the bases of experiences with similar business processes. Today’s information systems are at the level of data management or information management. Quite a few manage information processes in the area of tactical and strategic planning and almost no one manages processes in the areas of gathering, distribution and sharing of knowledge in business. We

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3

Evgenev, G. B. "Synergetic knowledge bases." Ontology of designing 11, no. 1 (2021): 76–88. http://dx.doi.org/10.18287/2223-9537-2021-11-1-76-88.

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Modern knowledge bases must largely correspond to human thinking and the reality of the world. Synergetic knowledge bases create the possibility of joint use of both "hard" computing, which require the accuracy and unique-ness of the solution, and "soft" computing, allowing a given error and uncertainty for a specific problem. A methodolo-gy for creating synergetic systems for the representation of knowledge using artificial intelligence technologies is pro-posed. The methodology is based on knowledge base methods and can be used to develop design and management systems in industries. A model

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4

Lorentzos, N. A., C. P. Yialouris, and A. B. Sideridis. "Time-evolving rule-based knowledge bases." Data & Knowledge Engineering 29, no. 3 (1999): 313–35. http://dx.doi.org/10.1016/s0169-023x(98)00048-2.

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5

García-Manotas, Ignacio, Eduardo Lupiani, Francisco García-Sánchez, and Rafael Valencia-García. "Populating Knowledge Based Decision Support Systems." International Journal of Decision Support System Technology 2, no. 1 (2010): 1–20. http://dx.doi.org/10.4018/jdsst.2010101601.

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Knowledge-based decision support systems (KBDSS) hold up business and organizational decision-making activities on the basis of the knowledge available concerning the domain under question. One of the main problems with knowledge bases is that their construction is a time-consuming task. A number of methodologies have been proposed in the context of the Semantic Web to assist in the development of ontology-based knowledge bases. In this paper, we present a technique for populating knowledge bases from semi-structured text which take advantage of the semantic underpinnings provided by ontologie

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6

Vostrovský, V. "Expert systems utilization in knowledge management ." Agricultural Economics (Zemědělská ekonomika) 52, No. 10 (2012): 451–55. http://dx.doi.org/10.17221/5050-agricecon.

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Knowledge and information are the key to successful pursuit of business activity. For the management knowledge, there can be employed among others the expert systems and eLearning. The article provides the expert systems utilization in knowledge management as the expert eLearning component. This solution then makes it possible to preserve the appropriate set of knowledge for a given business activity in knowledge bases and to pass this knowledge in the moment of need in the form of multimedia teaching applications to the appropriate employees. &nbsp;

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7

Santos, Eugene, and Hang T. Dinh. "On automatic knowledge validation for Bayesian knowledge bases." Data & Knowledge Engineering 64, no. 1 (2008): 218–41. http://dx.doi.org/10.1016/j.datak.2007.07.004.

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8

Nowak-Brzezińska, Agnieszka, and Alicja Wakulicz-Deja. "Exploration of rule-based knowledge bases: A knowledge engineer’s support." Information Sciences 485 (June 2019): 301–18. http://dx.doi.org/10.1016/j.ins.2019.02.019.

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9

Hao, Shuang, Nan Tang, Guoliang Li, Jian Li, and Jianhua Feng. "Distilling relations using knowledge bases." VLDB Journal 27, no. 4 (2018): 497–519. http://dx.doi.org/10.1007/s00778-018-0506-9.

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10

Bouzar-Benlabiod, Lydia, Salem Benferhat, and Thouraya Bouabana-Tebibel. "Comparing and Weakening Possibilistic Knowledge Bases." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 22, no. 02 (2014): 181–203. http://dx.doi.org/10.1142/s0218488514500081.

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Weighted logics are important tools for reasoning under uncertain knowledge. A weighted knowledge base is a set of weighted formulas of the form (φi, αi) where φi is a propositional formula and αi is the certainty degree associated with φi. This paper addresses two issues in the possibility theory framework: i) how to compare two weighted knowledge bases with respect to their information contents? and ii) how to syntactically define the concept of forgetting variables? We show that comparing knowledge bases is the counterpart of the concept of the specificity relation defined between possibili

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11

Esteves, Diego, Anisa Rula, Aniketh Janardhan Reddy, and Jens Lehmann. "Toward Veracity Assessment in RDF Knowledge Bases." Journal of Data and Information Quality 9, no. 3 (2018): 1–26. http://dx.doi.org/10.1145/3177873.

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12

Ţăndăreanu, Nicolae. "Knowledge Bases with Output." Knowledge and Information Systems 2, no. 4 (2000): 438–60. http://dx.doi.org/10.1007/pl00011651.

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13

Sheu, P. C.-Y., and W. Sull. "Knowledge management in deductive object bases." Data & Knowledge Engineering 5, no. 1 (1990): 39–58. http://dx.doi.org/10.1016/0169-023x(90)90032-9.

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14

Bolotova, E. E., Yu I. Butenko, and N. I. Sidnyaev. "Predicate logic in information processing systems in knowledge bases." Физические основы приборостроения 9, no. 2 (2020): 37–47. http://dx.doi.org/10.25210/jfop-2002-037047.

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15

Asheim, Bjørn T., and Lars Coenen. "Knowledge bases and regional innovation systems: Comparing Nordic clusters." Research Policy 34, no. 8 (2005): 1173–90. http://dx.doi.org/10.1016/j.respol.2005.03.013.

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16

Asheim, Bjørn. "DIFFERENTIATED KNOWLEDGE BASES AND VARIETIES OF REGIONAL INNOVATION SYSTEMS." Innovation: The European Journal of Social Science Research 20, no. 3 (2007): 223–41. http://dx.doi.org/10.1080/13511610701722846.

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17

Zhang, Danchen, and Daqing He. "Enhancing Clinical Decision Support Systems with Public Knowledge Bases." Data and Information Management 1, no. 1 (2017): 49–60. http://dx.doi.org/10.1515/dim-2017-0005.

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Abstract With vast amount of biomedical literature available online, doctors have the benefits of consulting the literature before making clinical decisions, but they are facing the daunting task of finding needles in haystacks. In this situation, it would be of great use to the doctors if an effective clinical decision support system is available to generate accurate queries and return a manageable size of highly useful articles. Existing studies showed the usefulness of patients’ diagnosis information in supporting effective retrieval of relevant literature, but such diagnosis information is

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18

Morris, Anne K., and James Hiebert. "Introduction: Building Knowledge Bases and Improving Systems of Practice." Elementary School Journal 109, no. 5 (2009): 429–41. http://dx.doi.org/10.1086/596994.

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19

PRADHAN, SHEKHAR, and JACK MINKER. "USING PRIORITIES TO COMBINE KNOWLEDGE BASES." International Journal of Cooperative Information Systems 05, no. 02n03 (1996): 333–64. http://dx.doi.org/10.1142/s0218843096000130.

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Two or more companies, each with its own knowledge base, may merge. In that case one option is to merge the knowledge bases into one knowledge base. It can happen that some of the information contained in one or more knowledge bases may be in conflict with information in the other knowledge bases. There may be several such points of conflict and any information may be involved in several different such points of conflict. In that case, the integrator of the knowledge bases may prefer a certain claim to another in one conflict-point without necessarily preferring that claim in another conflict-

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20

ADALI, SIBEL, and V. S. SUBRAHMANIAN. "AMALGAMATING KNOWLEDGE BASES II: DISTRIBUTED MEDIATORS." International Journal of Cooperative Information Systems 03, no. 04 (1994): 349–83. http://dx.doi.org/10.1142/s0218215794000193.

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Integrating knowledge from multiple sources is an important aspect of automated reasoning systems. In [23], we presented a uniform declarative and operational framework, based on annotated logics, for amalgamating multiple knowledge bases and data structures (e.g. relational, object-oriented, spatial, and temporal structures) when these knowledge bases (possibly) contain inconsistencies, uncertainties and non-monotonic modes of negation. We showed that annotated logics may be used, with some modifications, to mediate between different knowledge bases. The multiple knowledge bases are amalgamat

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21

Tommelein, I. D., B. Hayes-Roth, and R. E. Levitt. "Altering the SightPlan knowledge-based systems." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 6, no. 1 (1992): 19–37. http://dx.doi.org/10.1017/s0890060400002924.

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SightPlan refers to several knowledge-based systems that address construction site layout. Five different versions were implemented and their components of expertise are described here. These systems are alterations of one another, differing either in the problems they solve, the problem-solving methods they apply, or the tasks they address. Because they share either control knowledge, domain concepts, or heuristics, and such knowledge is implemented in well-defined modular knowledge bases, these systems could easily re-use parts of one another. Experiments like those presented here may clarif

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22

Zarri, Gian Piero. "A knowledge representation language for large knowledge bases and “intelligent” information retrieval systems." Information Processing & Management 26, no. 3 (1990): 349–70. http://dx.doi.org/10.1016/0306-4573(90)90096-k.

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23

Trice, Andrew, and Randall Davis. "Heuristics for Reconciling Independent Knowledge Bases." Information Systems Research 4, no. 3 (1993): 262–88. http://dx.doi.org/10.1287/isre.4.3.262.

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24

Santos, Eugene. "Verification and validation of Bayesian knowledge-bases." Data & Knowledge Engineering 37, no. 3 (2001): 307–29. http://dx.doi.org/10.1016/s0169-023x(01)00011-8.

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25

Gribova, V. V., Ph M. Moskalenko, V. A. Timchenko, and E. A. Shalfeeva. "Control Graph Based Development of Problem Solvers for Systems with Knowledge Bases." Programmnaya Ingeneria 12, no. 3 (2021): 115–26. http://dx.doi.org/10.17587/prin.12.115-126.

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Reducing the complexity of developing software systems that solve complex problems of science and practice, as well as ensuring their viability are among urgent tasks of software engineering. For systems with knowledge bases, as noted in many literature sources, this problem is most acute, despite the fact that significant results have been achieved in its solving. This problem is especially critical for those systems in which one of the requirements is a time limit for decision-making. Given that the used solution methods belong to the class of exhaustive ones that have a large computational

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26

Rivero, Carlos R., Inma Hernández, David Ruiz, and Rafael Corchuelo. "Mapping RDF knowledge bases using exchange samples." Knowledge-Based Systems 93 (February 2016): 47–66. http://dx.doi.org/10.1016/j.knosys.2015.11.001.

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27

Moulin, Bernard, and Daniel Rousseau. "Designing deontic knowledge bases from regulation texts." Knowledge-Based Systems 3, no. 2 (1990): 108–20. http://dx.doi.org/10.1016/0950-7051(90)90004-2.

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28

Hempelmann, Christian F., Unal Sakoglu, Varadraj P. Gurupur, and Seetaramaraju Jampana. "An entropy-based evaluation method for knowledge bases of medical information systems." Expert Systems with Applications 46 (March 2016): 262–73. http://dx.doi.org/10.1016/j.eswa.2015.10.023.

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29

Tam, Kar yan. "Applying conceptual clustering to knowledge-bases construction." Decision Support Systems 10, no. 2 (1993): 173–98. http://dx.doi.org/10.1016/0167-9236(93)90037-4.

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30

Kahn, M. G. "Knowledge-Based Systems." Yearbook of Medical Informatics 02, no. 01 (1993): 399–401. http://dx.doi.org/10.1055/s-0038-1637984.

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31

Buchanan, Bruce G., Daniel Bobrow, Randall Davis, John McDermott, and Edward H. Shortliffe. "Knowledge-Based Systems." Annual Review of Computer Science 4, no. 1 (1990): 395–416. http://dx.doi.org/10.1146/annurev.cs.04.060190.002143.

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32

DAVIS, R. "Knowledge-Based Systems." Science 231, no. 4741 (1986): 957–63. http://dx.doi.org/10.1126/science.231.4741.957.

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33

Mainzer, Klaus. "Knowledge-based systems." Journal for General Philosophy of Science 21, no. 1 (1990): 47–74. http://dx.doi.org/10.1007/bf01801416.

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34

Wellman, Michael P., John S. Breese, and Robert P. Goldman. "From knowledge bases to decision models." Knowledge Engineering Review 7, no. 1 (1992): 35–53. http://dx.doi.org/10.1017/s0269888900006147.

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AbstractIn recent years there has been a growing interest among AI researchers in probabilistic and decision modelling, spurred by significant advances in representation and computation with network modelling formalisms. In applying these techniques to decision support tasks, fixed network models have proven to be inadequately expressive when a broad range of situations must be handled. Hence many researchers have sought to combine the strengths of flexible knowledge representation languages with the normative status and well-understood computational properties of decision-modelling formalisms

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35

Shin, Donghoon, and P. Bruce Berra. "Computer architectures for logic-oriented data/knowledge bases." Knowledge Engineering Review 4, no. 1 (1989): 1–29. http://dx.doi.org/10.1017/s0269888900004720.

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AbstractKnowledge base management systems (KBMS) are designed to efficiently retrieve and manipulate large shared knowledge bases. A significant subclass of KBMS consisting of a combination of logic programming and database is often called a logic oriented knowledge base system (LOKBS). These systems must possess considerable processing and I/O capabilities so many approaches have been taken to the improvement of their performance. In this paper we review the current performance enhancing hardware approaches for LOKBS. We include parallelism, both in processing and I/O, algorithms, caching, an

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36

Buchheit, M., F. M. Donini, and A. Schaerf. "Decidable Reasoning in Terminological Knowledge Representation Systems." Journal of Artificial Intelligence Research 1 (December 1, 1993): 109–38. http://dx.doi.org/10.1613/jair.21.

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Terminological knowledge representation systems (TKRSs) are tools for designing and using knowledge bases that make use of terminological languages (or concept languages). We analyze from a theoretical point of view a TKRS whose capabilities go beyond the ones of presently available TKRSs. The new features studied, often required in practical applications, can be summarized in three main points. First, we consider a highly expressive terminological language, called ALCNR, including general complements of concepts, number restrictions and role conjunction. Second, we allow to express inclusion

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37

Bast, Hannah, Björn Buchhold, and Elmar Haussmann. "Semantic Search on Text and Knowledge Bases." Foundations and Trends® in Information Retrieval 10, no. 1 (2016): 119–271. http://dx.doi.org/10.1561/1500000032.

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38

Teniente, Ernest, and Antoni Oliv�. "Updating knowledge bases while maintaining their consistency." VLDB Journal 4, no. 2 (1995): 193–241. http://dx.doi.org/10.1007/bf01237920.

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39

Cheremisina, L. "USE OF ONTOLOGIES FOR KNOWLEDGE BASES CREATION TUTORING COMPUTER SYSTEMS." Information Technologies in Education, no. 21 (December 24, 2014): 96–101. http://dx.doi.org/10.14308/ite000515.

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40

Fujita, Hamido, and Péter Baranyi. "Special issue on: Knowledge-Bases for Cognitive Infocommunications Systems (KBCICS)." Knowledge-Based Systems 71 (November 2014): 1–2. http://dx.doi.org/10.1016/j.knosys.2014.08.017.

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41

Gress, Douglas R. "Knowledge bases, regional innovation systems, and Korea's solar PV industry." Environment and Planning C: Government and Policy 33, no. 6 (2015): 1432–49. http://dx.doi.org/10.1177/0263774x15614464.

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42

Sharma, Abhishek, and Keith M. Goolsbey. "Simulation-Based Approach to Efficient Commonsense Reasoning in Very Large Knowledge Bases." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1360–67. http://dx.doi.org/10.1609/aaai.v33i01.33011360.

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Cognitive systems must reason with large bodies of general knowledge to perform complex tasks in the real world. However, due to the intractability of reasoning in large, expressive knowledge bases (KBs), many AI systems have limited reasoning capabilities. Successful cognitive systems have used a variety of machine learning and axiom selection methods to improve inference. In this paper, we describe a search heuristic that uses a Monte-Carlo simulation technique to choose inference steps. We test the efficacy of this approach on a very large and expressive KB, Cyc. Experimental results on hun

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43

Vaux, Janet. "Knowledge bases: who needs standards?" Expert Systems 10, no. 4 (1993): 251–52. http://dx.doi.org/10.1111/j.1468-0394.1993.tb00105.x.

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44

Jiang, Zhengbao, Zhicheng Dou, and Ji-Rong Wen. "Generating Query Facets Using Knowledge Bases." IEEE Transactions on Knowledge and Data Engineering 29, no. 2 (2017): 315–29. http://dx.doi.org/10.1109/tkde.2016.2623782.

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45

Motoda, H., R. Mizoguchi, J. Boose, and B. Gaines. "Knowledge acquisition for knowledge-based systems." IEEE Expert 6, no. 4 (1991): 53–64. http://dx.doi.org/10.1109/64.85921.

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46

Müller-Kolck, Ulrich. "Knowledge acquisition for knowledge-based systems." Artificial Intelligence in Medicine 2, no. 2 (1990): 119–20. http://dx.doi.org/10.1016/0933-3657(90)90033-n.

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47

Aranda-Corral, Gonzalo A., Joaquín Borrego-Díaz, Juan Galán-Páez, and Daniel Rodríguez-Chavarría. "Towards a Notion of Basis for Knowledge-Based Systems—Applications." Mathematics 9, no. 3 (2021): 252. http://dx.doi.org/10.3390/math9030252.

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In the paradigm of Knowledge-Based Systems (KBS), the design of methods to simplify the reasoning leads to more efficient processes. A point of view that provides valuable insights is the algebraic one. In this work, a notion of basis (and dimension) for Knowledge Bases in Propositional Logic associated with knowledge forgetting is introduced. It is based on ideas that come from the translation of such logic in (Computer) Algebra, particularly from the interpretation of variable forgetting. In this paper, the concept of weak base is defined as a set of variables sufficient to decide the consis

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48

Moily, Jaya P., and Thomas J. Murray. "A modularization approach for PROLOG knowledge bases." Information Systems 18, no. 6 (1993): 405–17. http://dx.doi.org/10.1016/0306-4379(93)90016-t.

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49

Tam, Kar Yan. "Automated Construction of Knowledge-Bases from Examples." Information Systems Research 1, no. 2 (1990): 144–67. http://dx.doi.org/10.1287/isre.1.2.144.

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50

Sheu, P., and R. L. Kashyap. "Object-based knowledge bases in automatic manufacturing environments." International Journal of Advanced Manufacturing Technology 3, no. 3 (1988): 39–52. http://dx.doi.org/10.1007/bf02601589.

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