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Knowledge Retrieval seeks to return information in a structured form, consistent with human cognitive processes as opposed to simple lists of data items. It draws on a range of fields including epistemology (theory of knowledge), cognitive psychology, cognitive neuroscience, logic and inference, machine learning and knowledge discovery, linguistics, and information technology.

Overview Edit

In the field of retrieval systems, established approaches include:

Both approaches require a user to read and analyze often long lists of data sets or documents in order to extract meaning.

The goal of knowledge retrieval systems is to reduce the burden of those processes by improved search and representation. This improvement is needed to leverage the increasing data volumes available on the Internet.[1][2][3][4][5][6][7][8][9][10][11]

Comparison of Data, Information and Knowledge Retrieval Edit

Data Retrieval and Information Retrieval are earlier and more basic forms of information access. [12]

Data Retrieval Information Retrieval Knowledge Retrieval
Match Boolean match partial match, best match partial match, best match
Inference deductive inference inductive inference deductive inference, inductive inference, associative reasoning, analogical reasoning
Model deterministic model statistical and probabilistic model semantic model, inference model
Query artificial language natural language knowledge structure, natural language
Organization table, index table, index knowledge unit, knowledge structure
Representation number, rule natural language, markup language concept graph, predicate logic, production rule, frame, semantic network, ontology
Storage database document collections knowledge base
Retrieved Results data set sections or documents a set of knowledge unit

Knowledge retrieval (KR) focuses on the knowledge level. We need to examine how to extract, represent, and use the knowledge in data and information.[13] Knowledge retrieval systems provide knowledge to users in a structured way. Compared to data retrieval and information retrieval, they use different inference models, retrieval methods, result organization, etc. Table 1, extending van Rijsbergen’s comparison of the difference between data retrieval and information retrieval,[14] summarizes the main characteristics of data retrieval, information retrieval, and knowledge retrieval.[15] The core of data retrieval and information retrieval is retrieval subsystems. Data retrieval gets results through Boolean match.[16] Information retrieval uses partial match and best match. Knowledge retrieval is also based on partial match and best match.

From an inference perspective, data retrieval uses deductive inference, and information retrieval uses inductive inference.[14] Considering the limitations from the assumptions of different logics, traditional logic systems (e.g., Horn subset of first order logic) cannot reasoning efficiently.[17] Associative reasoning, analogical reasoning and the idea of unifying reasoning and search may be effective methods of reasoning at the web scale.[17][18]

From the retrieval perspective, knowledge retrieval systems focus on semantics and better organization of information. Data retrieval and information retrieval organize the data and documents by indexing, while knowledge retrieval organize information by indicating connections between elements in those documents.

Frameworks for Knowledge Retrieval systems Edit

From computer science perspective, a logic framework concentrating on fuzziness of knowledge queries has been proposed and investegated in detail.[19] Markup languages for knowledge reasoning and relevant strategies have been investigated, which may serve as possible logic reasoning foundations for text based knowledge retrieval.[3]

From cognitive science perspective, especially from cognitive psychology and cognitive neuroscience perspective, the neurobiological basis for knowledge retrieval in the human brain has been investigated, and may serve as a cognitive model for knowledge retrieval.[20][21]

Knowledge Retrieval related Disciplines Edit

Knowledge retrieval can draw results from the following related theories and technologies:[12]

- Theory of Knowledge: knowledge acquisition, knowledge organization, knowledge representation, knowledge validation, knowledge management.

- Cognitive Science: cognitive psychology, cognitive neuroscience, cognitive informatics, concept formation and learning, decision making, human–computer interaction.

- Machine Learning and Knowledge Discovery: preprocessing, classification, clustering, prediction, postprocessing, statistical learning theory.

- Logic and Inference: propositional logic, predicate logic, attribute logic, universal logic, inductive inference, deductive inference, associative reasoning, analogical reasoning, approximate reasoning.

- Information Technology: information theory, information science, information retrieval, database systems, knowledge-based systems, rule-based systems, expert systems, decision support systems, intelligent agent technology.

- Linguistics: computational linguistics, natural language understanding, natural language processing.

Topics listed under each entry serve as examples and do not form a complete list. And many related disciplines should be added as the field grows mature.

References Edit

  1. Frisch, A.M. Knowledge Retrieval as Specialized Inference, Ph.D thesis, University of Rochester, 1986.
  2. Kame, M. and Quintana, Y. A graph based knowledge retrieval system, Proceedings of the 1990 IEEE International Conference on Systems, Man and Cybernetics, 1990: 269-275.
  3. 3.0 3.1 Martin, P. and Eklund, P.W. Knowledge retrieval and the World Wide Web, IEEE Intelligent Systems, 2000, 15(3): 18-25.
  4. Oertel, P. and Amir, E. A framework for commonsence knowledge retrieval, Proceedings of the 7th International Symposium on Logic Formalizations of Commonsense Reasoning, 2005.
  5. Travers, M. A visual representation for knowledge structures, Proceedings of the 2nd annual ACM conference on Hypertext and Hypermedia, 1989: 147-158.
  6. Yao, Y.Y. Information retrieval support systems, Proceedings of the 2002 IEEE International Conference on Fuzzy Systems, 2002, 1092-1097.
  7. Zhou, N., Zhang, Y.F. and Zhang, L.Y. Information Visualization and Knowledge Retrieval [In Chinese], Science Press, 2005.
  8. Robert Loew, Katrin Kuemmel, Judith Ruprecht, Udo Bleimann, Paul Walsh. Approaches for personalised knowledge retrieval, Internet Research, 17(1), 2007
  9. Stefania Mariano, Andrea Casey. The process of knowledge retrieval: A case study of an American high-technology research, engineering and consulting company. VINE: The journal of information and knowledge management systems, 37(3), 2007.
  10. Jens Gammelgaard, Thomas Ritter. The knowledge retrieval matrix: codification and personification as separate strategies, Journal of Knowledge Management, 9(4), 133-143, 2005.
  11. J.E.L. Farradane. Analysis and organization of knowledge for retrieval, Aslib Proceedings, 22(12), 607-616,1970.
  12. 12.0 12.1 Yiyu Yao, Yi Zeng, Ning Zhong, Xiangji Huang. Knowledge Retrieval (KR) . In: Proceedings of the 2007 IEEE/WIC/ACM International Conference on Web Intelligence, IEEE Computer Society, Silicon Valley, USA, November 2–5, 2007, 729-735.
  13. Bellinger, G., Castro, D. and Mills, A. Data, Information, Knowledge, and Wisdom, http://www.systemsthinking.org/dikw/dikw.htm
  14. 14.0 14.1 van Rijsbergen, C.J. Information Retrieval, Butterworths, 1979.
  15. Zeng, Y., Yao, Y.Y. and Zhong, N. Granular structurebased knowledge retrieval [In Chinese], Proceedings of the Joint Conference of the Seventh Conference of Rough Set and Soft Computing, the First Forum of Granular Computing, and the First Forum of Web Intelligence, 2007.
  16. Baeza-Yates, R. and Ribeiro-Neto, B. Modern Information Retrieval, AddisonWesley, 1999.
  17. 17.0 17.1 Fensel, D. and van Harmelen, F. Unifying reasoning and search to web scale, IEEE Internet Computing, 2007, 11(2): 96, 94-95.
  18. Berners-Lee, T., Hall, W., Hendler, J.A., O’Hara, K., Shadbolt, N. and Weitzner, D.J. A Framework for Web science, Foundations and Trends in Web Science, 2006, 1(1): 1-130.
  19. Chen, B.C. and Hsiang, J. A logic framework of knowledge retrieval with fuzziness, Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence, 2004: 524-528.
  20. Tranel, Daniel, Damasio, Antonio. The neurobiology of knowledge retrieval. Behavioral and Brain Science, 22(2): 303-303, 1999.
  21. Jennifer H. Pfeifer, Matthew D. Lieberman, Mirella Dapretto. “I Know You Are But What Am I?!”: Neural Bases of Self-and Social Knowledge Retrieval in Children and Adults, Journal of Cognitive Neuroscience, 19(8), MIT Press, August 2007.

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