A Semantic Framework for Graph-Based Enterprise Search

• Autorzy: Modoni G. E. , Sacco M. , Terkaj W.
• Języki publikacji: EN

Abstrakty

EN

Various recent studies have shown that in many companies workers can spend near half of their time looking for information. Effective internal search tools could make their job more efficient. However, a killer application for this type of solutions is still not available. This paper introduces an envisioned architecture, which should represent the foundations of a new generation of tools for searching information within enterprises.

Słowa kluczowe

EN

Enterprise Search; Graph Knowledge; semantic web

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2014
• Tom: Vol. 10, no 4
• Strony: 66--74
• Opis fizyczny: Bibliogr. 24 poz., fig.

Twórcy

autor

Modoni G. E.

• Institute of Industrial Technologies and Automation - National Research Council, via Lembo 38 Bari, Italy

autor

Sacco M.

• Institute of Industrial Technologies and Automation - National Research Council, via Bassini 15 Milano, Italy

autor

Terkaj W.

• Institute of Industrial Technologies and Automation - National Research Council, via Bassini 15 Milano, Italy

Bibliografia

• [1] Singhal A.: Introducing the Knowledge Graph: things, not strings. Official Google Blog, http://googleblog.blogspot.com/2012/05/introducing-knowledge-graph-things-not.html, May 2012.
• [2] Drew O., Constine J., Taylor C., Lunden I.: Facebook Announces Its Third Pillar “Graph Search” That Gives You Answers, Not Links Like Google., TechCrunch. AOL Tech, http://techcrunch.com/2013/01/15/facebook-announces-its-third-pillar-graph-search., 2013.
• [3] Spivack N.: “http://www.novaspivack.com/technology/diagram-beyond-keyword-and-natural-language-search”, 2007.
• [4] Enterprise Findability Without the Complexity. White paper on Google Search Appliance, Google Inc., http://goo.gl/aFpSD0, 2008.
• [5] Guha R., Mccool R., Miller E.: Semantic search. Proceedings of the 12th international conference on World Wide Web, ACM Press , 2003, pp. 700-709.
• [6] Klos S.: The impact of ERP system on economic situation of enterprise: case study. Applied Computer Science, Vol. 3, no 2, 2007, pp. 93-101.
• [7] Tran T., Cimiano P., Rudolph S., Studer R.: Ontology-based interpretation of keywords for semantic search. In ISWC/ASWC, 2007, pp. 523-536.
• [8] IEEE Standard Computer Dictionary, A Compilation of IEEE Standard Computer Glossaries. IEEE, 1990.
• [9] Milicic A., Perdikakis A., El Kadiri S., Kiritsis D., Terzi S., Fiordi P., Sadocco S.: Specialization of a Fundamental Ontology for Manufacturing Product Lifecycle Applications: A Case Study for Lifecycle Cost Assessment. OTM Workshops 2012, 2012, pp. 69-72.
• [10] Kadar B., Terkaj W., Sacco M.: Semantic Virtual Factory supporting interoperable modelling and evaluation of production systems. CIRP Annals – Manufacturing Technology 2013; 62(1), 2013, pp. 443-446.
• [11] Terkaj W., Pedrielli G., Sacco M.: Virtual Factory Data Model. Proceedings of the Workshop on Ontology and Semantic Web for Manufacturing, Graz, Austria, 2012, pp. 29-43.
• [12] Berners-Lee T., Hendler J., Lassila O.: The Semantic Web, Scientific American, 284(5), 2001, pp. 34-43.
• [13] Mena E., Illarramendi A., Kashyap V., Sheth A.: Observer: An Appro-ach for Query Processing in Global Information Systems Based on Interoperation Across Pre-Existing Ontologies. Distrib. Parallel Databases 8(2), 2000, pp. 223-271.
• [14] Klyne G., Carroll J.J.: Resource Description Framework (RDF): Concepts and Abstract Syntax. (W3C Recommendation 10 February 2004), World Wide Web Consortium, 2004.
• [15] W3C OWL 2 Web Ontology Language – Document Overview. http://www.w3.org/TR/ owl2-overview/, 2012.
• [16] Aggarwal C., Zhai C.: An introduction to text mining, In Mining Text Data, Springer, 2012, pp. 1-10.
• [17] Fayyad U. M., Piatetsky-Shapiro G., Smyth P., Uthuruswamy R.: Advances in Knowledge Discovery and Data Mining, Menlo Park, CA: AAAI/MIT Press, 1996.
• [18] Choudhary A.K., Harding, J.A., Tiwari M.K: Data mining in manufacturing: a review based on the kind of knowledge. Journal of Intelligent Manufacturing, 20 (5), 2009 pp. 501-521.
• [19] Yunyao L., Ziyang L., Huaiyu Z.: Enterprise Search in the Big Data Era: Recent Developments and Open Challenges. PVLDB 7(13), 2014, pp. 1717-1718.
• [20] Modoni G., Sacco M., Terkaj W.: A survey of RDF store solutions. Proceedings of the 20th International Conference on Engineering, Technology and Innovation, Bergamo, 2014.
• [21] Manyika J., Chui M., Brown B., Bughin J., Dobbs R., Roxburgh C., Hung Byers A.: Big data: The next frontier for innovation, competition, and produc-tivity. McKinsey Global Institute, 2011.
• [22] W3C: SPARQL Query Language for RDF. W3C Semantic Web Activity RDF Data Access Working Group, 2008.
• [23] Obitko M., Vrba P., Marik V., Radakovic M.: The impacts of semantic technologies on industrial systems. In 17th IFAC World Congress, Seoul, Korea, 2008, pp. 13880-13887.
• [24] Hearst M.: Design recommendations for hierarchical faceted search interfaces, In: ACM SIGIR workshop on faceted search, 2006, pp. 1-5.