An ontology enhanced parallel SVM for scalable spam filter training

This is the post-print version of the final paper published in Neurocomputing. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Spam, under a variety of shapes and forms, continues to inflict increased damage. Varying approaches including Support Vector Machine (SVM) techniques have been proposed for spam filter training and classification. However, SVM training is a computationally intensive process. This paper presents a MapReduce based parallel SVM algorithm for scalable spam filter training. By distributing, processing and optimizing the subsets of the training data across multiple participating computer nodes, the parallel SVM reduces the training time significantly. Ontology semantics are employed to minimize the impact of accuracy degradation when distributing the training data among a number of SVM classifiers. Experimental results show that ontology based augmentation improves the accuracy level of the parallel SVM beyond the original sequential counterpart

Knowledge society arguments revisited in the semantic technologies era

In the light of high profile governmental and international efforts to realise the knowledge society, I review the arguments made for and against it from a technology standpoint. I focus on advanced knowledge technologies with applications on a large scale and in open- ended environments like the World Wide Web and its ambitious extension, the Semantic Web. I argue for a greater role of social networks in a knowledge society and I explore the recent developments in mechanised trust, knowledge certification, and speculate on their blending with traditional societal institutions. These form the basis of a sketched roadmap for enabling technologies for a knowledge society

Towards the development of the framework for inter sensing enterprise architecture

[EN] Inter-enterprise architecture (IEA) is a new concept that seeks to apply the tools and methodologies of enterprise architecture (EA) in a collaborative context, in order to model collaborative organizations in an inclusive manner. According to the main enterprise architectures proposed to this point, an EA should be conformed at least for a framework, a methodology and a modelling language. Sensing enterprise (SE) is an attribute of an enterprise or a network that allows it to react to business stimuli originating on the Internet. These fields have come into focus recently, and there is not evidence of the use of IEA for modelling a SE, while finding an interesting gap to work on. Thus, this paper proposes an initial framework for inter sensing enterprise architecture (FISEA), which seeks to classify, organize, store and communicate, at the conceptual level, all the elements for inter-sensing enterprise architectures and their relationships, ensuring their consistency and integrity. This FISEA provides a clear idea about the elements and views that create collaborative network and their inter-relationships, based on the support of Future Internet.This work was supported by the European Commission FP7 UNITE Project, through its Secondment Programme and the Universitat Politecnica de Valencia ADENPRO-PJP project (ref. SP20120703).Vargas, A.; Cuenca, L.; Boza, A.; Sacala, I.; Moisescu, M. (2016). Towards the development of the framework for inter sensing enterprise architecture. Journal of Intelligent Manufacturing. 27(1):55-72. https://doi.org/10.1007/s10845-014-0901-zS5572271Adaba, G., Rusu, L., & Mekawy, M. (2010). Business-IT alignment in trade facilitation: A case study. In organizational, business, and technological aspects of the knowledge society. Communications in Computer and Information. Science, 44(112), 146–154.Afsarmanesh, H., & Msanjila, S. (2008). Inter-organizational trust in VBEs. In L. Camarinha-Matos, H. Afsarmanesh, & M. Ollus (Eds.), Methods and tools for collaborative networked organizations (pp. 91–118). New York: Springer.Afsarmanesh, H., Camarinha-Matos, L., & Ermilova, E. (2008). VBE reference framework. In L. Camarinha-Matos, H. Afsarmanesh, & M. Ollus (Eds.), Methods and tools for collaborative networked organizations (pp. 35–68). New York: Springer.Arango, M., Londoño, J., & Zapata, J. (2010). Arquitectura empresarial- Una visión general. Revista Ingenierías Universidad de Medellín, 9(16), 101–111.Audy, J., Lehoux, N., & D’Amours, S. (2012). A framework for an efficient implementation of logistics collaborations. International Transactions in Operational Research, 19(5), 633–657.Boza, A., Cuenca, L., Poler, R., Michaelides, Z., & Systems, Enterprise Information. (2014). The interoperability force in the ERP field. Enterprise Information Systems,. doi: 10.1080/17517575.2013.866697Camarinha-Matos, L., & Afsarmanesh, H. (2005). Collaborative networks: A new scientific discipline. Journal of Intelligent Manufacturing, 16(4–5), 439–452.Camarinha-Matos, L., & Afsarmanesh, H. (2008). Collaborative networks: Reference modeling. New York: Springer.Camarinha-Matos, L., Afsarmanesh, H., & Ollus, M. (2008). ECOLEAD and CNO base concepts. In L. M. Camarinha-Matos, H. Afsarmanesh, & M. Ollus (Eds.), Methods and tools for collaborative networked organizations (pp. 35–68). New York: Springer.Chalmeta, R., & Grangel, R. (2003). ARDIN extension for virtual enterprise integration. The Journal of Systems and Software, 67(3), 141–152.Chen, D., Vallespir, B., & Doumeingts, G. (1997). GRAI integrated methodology and its mapping onto generic enterprise reference architecture and methodology. Computers in Industry, 33(2), 387–394.Choi, Y., Kang, D., Chae, H., & Kim, K. (2008). An enterprise architecture framework for collaboration of virtual enterprise chains. The International Journal of Advanced Manufacturing Technology, 35(11–12), 1065–1078.CIMOSA Asociation. (1996). CIMOSA Primer on key concepts, purpose and business value.Council of Supply Chain Management Professionals CSCMP. (2010). CSCMP. Glosary of terms. from http://cscmp.org/resources-research/glossary-terms . Accessed 9 February 2013Coutinho, C., Cretan, A., Ferreira, C., Ghodous, P., & Jardim-Goncalves, R. (2014). Service-based negotiation for advanced collaboration in enterprise networks. Journal of Intelligent Manufacturing,. doi: 10.1007/s10845-013-0857-4Cuenca, L. (2009). Marco arquitectónico para la propuesta IE-GIP. Extensión de la arquitectura CIMOSA. Aplicación a una empresa del sector cerámico. Tesis Doctoral Universidad Politecnica de Valencia.Cuenca, L., Boza, A., & Ortiz, A. (2011). An enterprise engineering approach for the alignment of business and information technology strategy. International Journal of Computer Integrated Manufacturing, 24(11), 974–992.Cuenca, L., Ortiz, A., & Boza, A. (2005). Arquitectura de Empresa. Visión General. Gijón: IX Congreso de Ingeniería de Organización.Dong, X., Liu, Q., & Yin, D. (2008). Business performance, business strategy, and information system strategic alignment: An empirical study on Chinese firms. Tsinghua Science and Technology, 13(3), 348–354.Ermilova, E., & Afsarmanesh, H. (2007). Modeling and management of profiles and competencies in VBEs. Journal of Intelligent Manufacturing, 18(5), 561–586.Estimali, H., Gardesh, H., & Sikari, S. (2010). Validating ITIL maturity to strategic business-IT alignment. In 2nd International conference on computer technology and development (ICCTD 2010).European Commission European Society and Media. (2007). Dygital Business Ecosystems. In F. Nachira, P. Dini, A. Nicolai, M. Le Louarn, & L. Rivera (Eds.). http://www.digital-ecosystems.org/book/ . Luxembourg: Office for Official Publications of the European Communities. Accessed 15 October 2012Executive Branch of the U.S. Federal Government. (2012). A Common Approach to Federal Enterprise Architecture. The White House. http://www.whitehouse.gov/sites/default/files/omb/assets/egov_docs/common_approach_to_federal_ea.pdf . Accessed 13 August 2013Franco, R., Gomez, P., Ortiz, A., & Navarro, R. (2012). Integrated approach for interoperability in collaborative networs and service-based ecosystems. In R. Poler, G. Doumeingts, B. Katzy, & R. Chalmeta (Eds.), Enterpise interoperability V (pp. 329–339). London: Springer.Force, I. F. I. P.-I. F. A. C. Task. (1998). GERAM: Generalised enterprise reference architecture and methodology. International Federation for Information Processing, 1(2), 30.Future Internet Enterprise Systems (FInES) Cluster. (2012). FInES Research Roadmap 2025. From http://cordis.europa.eu/fp7/ict/enet/documents/fines-research-roadmap-v30_en.pdf . Accessed 13 October 2013Henderson, J., & Venkatraman, N. (1993). Strategic alignment: Leveraging information technology for transforming organizations. IBM Systems Journal, 32(1), 472–484.Hu, Q., & Huang, D. (2006). Using the balanced scorecard to achieve sustained IT-business alignment: A case study. Communications of the Association for Information Systems, 17, 181–204.ISO 15704. (2000). Industrial automation systems–Requirements for enterprise-reference architectures and methodologies.ISO/CEN 19439. (2006). Enterprise integration–Framework for enterprise modelling.Kilger, C., Reuter, B., & Stadtler, H. (2008). Collaborative planning. In H. Stadtler & C. Kilger (Eds.), Supply chain management and advanced planning-concepts, models software and case studies (pp. 263–284). Berlin, Heidelberg: Springer.Kosanke, K., Vernadat, F., & Zelm, M. (1999). CIMOSA: Enterprise engineering and integration. Computers in Industry, 40(2), 83– 97.Lankhorst, M. (2009). Enterprise architecture at work: Modelling, communication and analysis. New York: Springer.Luftman, J. (2004). Assessing business-IT alignment maturity. Communications of the Association for Information Systems, 4, 99.Maes, R. (1999). Reconsidering information management through a generic framework. Amsterdam: Universiteit van Amsterdam, Department of Accountancy & Information Management.Mehandjiev, N., & Grefen, P. (2010). Dynamic business process formation for instant virtual enterprises. London.Mekawy, M., Rusu, L., & Ahmed, N. (2009). Business and IT alignment: An evaluation of strategic alignment models. In best practices for the knowledge society. Knowledge, Learning, Development and Technology for All, 49, 447–455.Moisescu, M., Sacala, I., Stanescu, A., & Serbanescu, C. (2012). Towards integration of knowledge extraction form process interoperability in future internet enterprise systems. Information Control Problems in Manufacturing, 14(1), 1458–1463.Ortiz, A. (1998). Propuesta para el Desarrollo de Programas de Integración Empresarial en Empresas Industriales. Aplicación a una Empresa del Sector Cerámico: Tesis Doctoral Universidad Politécnica de Valencia.Palmer, C., Harding, J., Swarnkar, R., Das, B., & Young, R. (2013). Generating rules from data mining for collaboration moderator services. Journal of Intelligent Manufacturing, 24, 313–330.Pijpers, V., Gordijn, J., & Akkermans, H. (2009). Aligning information system design and business strategy–A starting internet company. In the practice of enterprise modeling. Lecture Notes in Business Information Processing, 15, 47–61.Plaza, J., Burgos, J., & Carmona, E. (2010). Measuring stakeholder integration: Knowledge, interaction and adaptational behavior dimensions. Journal of Business Ethics, 93, 419–442.Romero, D., Galeano, N., & Molina, A. (2010). Virtual organisation breeding environments value system and its elements. Journal of Intelligent Manufacturing, 21(3), 267–286.Sacala, I., Moisescu, M., & Repta, D. (2013). Towards the development of the future internet based enterprise in the context of cyber-physical systems. In 2013 19th International conference on control systems and computer science (CSCS), (pp. 405–412).Santana, R., Daneva, M., van Eck, P., & Wieringa, R. (2008). Towards a business-IT aligned maturity model for collaborative networked organizations. In 12th International conference on advanced information systems engineering.Schekkerman, J. (2004). Enterprise architecture validation. Achieving business-aligned and validated entreprise architectures. Institute For Enterprise Architecture Developments (IFEAD). http://enterprise-architecture.info/Images/Extended%20Enterprise/Extended%20Enterprise%20Architecture2.htm . Accessed 10 October 2011Schekkerman, J. (2006). Extended enterprise architecture framework essentials guide. Retrieved 2012 from Institute For Enterprise Architecture Developments (IFEAD). http://enterprise-architecture.info/Images/E2AF/Extended%20Enterprise%20Architecture%20Framework%20Essentials%20Guide%20v1.5.pdf . Accessed 23 September 2012Stadtler, H. (2009). A framework for collaborative planning and state-of-the-art. OR Spectrum, 31, 5–30.Stelzer, D. (2010). Enterprise architecture principles: Literature review and research directions. Service-Oriented Computing. Lecture Notes in Computer Science, pp. 12–21.Swarnkar, R., Choudhary, A., Harding, J., Das, B., & Young, R. (2012). A framework for collaboration moderator services to support knowledge based collaboration. Journal of Intelligent Manufacturing, 23, 2003–2023.The, OPEN GROUP. (2011). TOGAF. http://www.opengroup.org/togaf/ . Accessed 18 November 2011U.S. Department of the Treasury. (2000). Treasury enterprise architecture framework. http://www.treasury.gov/Pages/default.aspx . Accessed 23 November 2011United States Department of Defense. (2010). Department of defense architecture framework. http://dodcio.defense.gov/dodaf20/dodaf20_capability.aspx . Accessed 28 November 2011Vargas, A., Boza, A., & Cuenca, L. (2011). Towards interoperability through Inter-enterprise collaboration architectures. In R. Meersman, T. Dillon, & P. Herrero (Eds.), On the move to meaningful internet systems: OTM 2011 workshops. Lecture Notes in Computer Science, Vol. 7046, pp. 102–111.Vargas, A., Boza, A., Cuenca, L., & Sacala, I. (2013a). Inter-enterprise architecture and internet of the future. Technological Innovation for the Internet of Things, 394, 25–32.Vargas, A., Boza, A., Cuenca, A., & Ortiz, A. (2013b). Towards a framework for inter-enterprise architecture to boost collaborative networks. In On the move to meaningful internet systems: OTM 2013 workshops. Lecture Notes in Computer Science, 8186, 179–188.Vargas, A., Boza, A., Cuenca, L., & Ortiz, A. (2014). The importance of strategic alignment in enterprise collaboration. In J. C. Prado-Prado & J. García-Arca (Eds.), Annals of industrial engineering 2012 (pp. 71–78). London: Springer.Vernadat, F. (2003). Enterprise modelling and integration: From fact modelling to enterprise interoperability. Enterprise inter- and intra-organizational integration: Building international consensus. Series: IFIP Advances in Information and Communication Technology, 108.Vesterager, J., Tølle, M., & Bernus, P. (2002). VERA: Virtual enterprise reference. GLOBEMEN final plenary: In GMNBook.Wang, X., Zhou, X., & Jiang, L. (2008). A method of business and IT alignment based on enterprise architecture. In IEEE international conference on service operations and logistics, and informatics, pp. 740–745.Zachman, J. (1997). Enterprise architecture: The issue of the century. Database Programming and Design, 1–13

Measuring concept similarities in multimedia ontologies: analysis and evaluations

The recent development of large-scale multimedia concept ontologies has provided a new momentum for research in the semantic analysis of multimedia repositories. Different methods for generic concept detection have been extensively studied, but the question of how to exploit the structure of a multimedia ontology and existing inter-concept relations has not received similar attention. In this paper, we present a clustering-based method for modeling semantic concepts on low-level feature spaces and study the evaluation of the quality of such models with entropy-based methods. We cover a variety of methods for assessing the similarity of different concepts in a multimedia ontology. We study three ontologies and apply the proposed techniques in experiments involving the visual and semantic similarities, manual annotation of video, and concept detection. The results show that modeling inter-concept relations can provide a promising resource for many different application areas in semantic multimedia processing

Doctor of Philosophy

dissertationDisease-specific ontologies, designed to structure and represent the medical knowledge about disease etiology, diagnosis, treatment, and prognosis, are essential for many advanced applications, such as predictive modeling, cohort identification, and clinical decision support. However, manually building disease-specific ontologies is very labor-intensive, especially in the process of knowledge acquisition. On the other hand, medical knowledge has been documented in a variety of biomedical knowledge resources, such as textbook, clinical guidelines, research articles, and clinical data repositories, which offers a great opportunity for an automated knowledge acquisition. In this dissertation, we aim to facilitate the large-scale development of disease-specific ontologies through automated extraction of disease-specific vocabularies from existing biomedical knowledge resources. Three separate studies presented in this dissertation explored both manual and automated vocabulary extraction. The first study addresses the question of whether disease-specific reference vocabularies derived from manual concept acquisition can achieve a near-saturated coverage (or near the greatest possible amount of disease-pertinent concepts) by using a small number of literature sources. Using a general-purpose, manual acquisition approach we developed, this study concludes that a small number of expert-curated biomedical literature resources can prove sufficient for acquiring near-saturated disease-specific vocabularies. The second and third studies introduce automated techniques for extracting disease-specific vocabularies from both MEDLINE citations (title and abstract) and a clinical data repository. In the second study, we developed and assessed a pipeline-based system which extracts disease-specific treatments from PubMed citations. The system has achieved a mean precision of 0.8 for the top 100 extracted treatment concepts. In the third study, we applied classification models to reduce irrelevant disease-concepts associations extracted from MEDLINE citations and electronic medical records. This study suggested the combination of measures of relevance from disparate sources to improve the identification of true-relevant concepts through classification and also demonstrated the generalizability of the studied classification model to new diseases. With the studies, we concluded that existing biomedical knowledge resources are valuable sources for extracting disease-concept associations, from which classification based on statistical measures of relevance could assist a semi-automated generation of disease-specific vocabularies

VITALAS at TRECVID-2008

In this paper, we present our experiments in TRECVID 2008 about High-Level feature extraction task. This is the first year for our participation in TRECVID, our system adopts some popular approaches that other workgroups proposed before. We proposed 2 advanced low-level features NEW Gabor texture descriptor and the Compact-SIFT Codeword histogram. Our system applied well-known LIBSVM to train the SVM classifier for the basic classifier. In fusion step, some methods were employed such as the Voting, SVM-base, HCRF and Bootstrap Average AdaBoost(BAAB)

Guided generation of pedagogical concept maps from the Wikipedia

We propose a new method for guided generation of concept maps from open accessonline knowledge resources such as Wikies. Based on this method we have implemented aprototype extracting semantic relations from sentences surrounding hyperlinks in the Wikipedia’sarticles and letting a learner to create customized learning objects in real-time based oncollaborative recommendations considering her earlier knowledge. Open source modules enablepedagogically motivated exploration in Wiki spaces, corresponding to an intelligent tutoringsystem. The method extracted compact noun–verb–noun phrases, suggested for labeling arcsbetween nodes that were labeled with article titles. On average, 80 percent of these phrases wereuseful while their length was only 20 percent of the length of the original sentences. Experimentsindicate that even simple analysis algorithms can well support user-initiated information retrievaland building intuitive learning objects that follow the learner’s needs.Peer reviewe

Towards a framework for comparing automatic term recognition methods

Automatic Term Recognition focuses on the extraction of words and multi-word expressions that are significant for a given domain. There is a considerable interest in using ATR for automatic metadata generation, creation of thesauri and terminological glossaries, keyword extraction, ontology building, etc. In this paper, we build upon the work done at the University of Sheffield, where a library with a few algorithms for ATR was recently developed. We enrich this library with new ATR algorithms and tools for evaluation. Our aim is to perform an experimental study comparing the base ATR methods as well as their combinations under various conditions. The results of the study indicate that better precision can be usually reached by combining ATR methods using foreground and ATR methods using background knowledge. The created platform is freely available and prepared for extensions by other researchers

LEVERAGING TEXT MINING FOR THE DESIGN OF A LEGAL KNOWLEDGE MANAGEMENT SYSTEM

In today’s globalized world, companies are faced with numerous and continuously changing legal requirements. To ensure that these companies are compliant with legal regulations, law and consulting firms use open legal data published by governments worldwide. With this data pool growing rapidly, the complexity of legal research is strongly increasing. Despite this fact, only few research papers consider the application of information systems in the legal domain. Against this backdrop, we pro-pose a knowledge management (KM) system that aims at supporting legal research processes. To this end, we leverage the potentials of text mining techniques to extract valuable information from legal documents. This information is stored in a graph database, which enables us to capture the relation-ships between these documents and users of the system. These relationships and the information from the documents are then fed into a recommendation system which aims at facilitating knowledge transfer within companies. The prototypical implementation of the proposed KM system is based on 20,000 legal documents and is currently evaluated in cooperation with a Big 4 accounting company