A unified knowledge compiler to provide support the scientific community

Abstract The scientific community represents an insatiable network with important needs related to the searching of information. The ever-broadening amount of domain-scientific on-line information that can be found requires increasingly sophisticated frameworks to manage it. Nevertheless, these frameworks are usually focused on specific useful functionalities and work more like expert systems than general purpose approaches. In order to ease the research process to the scientific community, the Unified Knowledge Compiler (UNIKO) framework is presented. This framework includes, among other functionalities, the recommendation of articles and authors related to a specific field of application, the evaluation of reputation scores of articles and authors, and the sentiment analysis of the texts of the articles. UNIKO is built as a hybrid framework based on Knowledge-Based Systems and Content-Based Recommendation Systems. In order to evaluate the performance of the system, several experiments have been done. The first experiment is developed to illustrate the reputation scoring task. The second one addresses the sentiment scores calculation based on a lexicon which is supported by a Convolutional Neural Network. The last experiment shows the recommendation tasks based on specific similarity measures and unsupervised learning.

[1]  Alex A. Freitas,et al.  A survey of evolutionary algorithms for data mining and knowledge discovery , 2003 .

[2]  Luca de Alfaro,et al.  A content-driven reputation system for the wikipedia , 2007, WWW '07.

[3]  Ryan Mitchell,et al.  Web Scraping with Python: Collecting Data from the Modern Web , 2015 .

[4]  Jason J. Jung,et al.  SciRecSys: A Recommendation System for Scientific Publication by Discovering Keyword Relationships , 2014, ICCCI.

[5]  Vadlamani Ravi,et al.  Soft computing system for bank performance prediction , 2008, Appl. Soft Comput..

[6]  Alexander van Deursen,et al.  Internet skill-related problems in accessing online health information , 2012, Int. J. Medical Informatics.

[7]  C. Leckie,et al.  A peer-to-peer collaborative intrusion detection system , 2005, 2005 13th IEEE International Conference on Networks Jointly held with the 2005 IEEE 7th Malaysia International Conf on Communic.

[8]  Todd A. Anderson,et al.  Toward Internet Distributed Computing , 2003, Computer.

[9]  Paul Resnick,et al.  Reputation systems , 2000, CACM.

[10]  Andrea Esuli,et al.  SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining , 2010, LREC.

[11]  NICHOLAS R. JENNINGS,et al.  An agent-based approach for building complex software systems , 2001, CACM.

[12]  J. E. Hirsch,et al.  An index to quantify an individual's scientific research output , 2005, Proc. Natl. Acad. Sci. USA.

[13]  Marc Langston,et al.  Linking to journal articles in an online teaching environment: The persistent link, DOI, and OpenURL , 2004 .

[14]  Dipti Srinivasan,et al.  Distributed Geometric Fuzzy Multiagent Urban Traffic Signal Control , 2010, IEEE Transactions on Intelligent Transportation Systems.

[15]  Ming Zhou,et al.  Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification , 2014, ACL.

[16]  Andrew Y. Ng,et al.  Parsing Natural Scenes and Natural Language with Recursive Neural Networks , 2011, ICML.

[17]  Erik Cambria,et al.  Affective Computing and Sentiment Analysis , 2016, IEEE Intelligent Systems.

[18]  Cristina Baroglio,et al.  From Tags to Emotions: Ontology-driven Sentimental Analysis in the Social Semantic Web , 2011, DART@AI*IA.

[19]  Osmar R. Zaïane,et al.  Building a Recommender Agent for e-Learning Systems , 2002, ICCE.

[20]  Sam Ruby,et al.  RESTful Web Services , 2007 .

[21]  Lillian Lee,et al.  Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..

[22]  Jürgen Schmidhuber,et al.  Multi-column deep neural networks for image classification , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Pasquale Lops,et al.  Content-based Recommender Systems: State of the Art and Trends , 2011, Recommender Systems Handbook.

[24]  Aurora Vizcaíno,et al.  Collaboration Tools for Global Software Engineering , 2010, IEEE Software.

[25]  Daniel J. Rosenkrantz,et al.  NP-Complete Problems , 2019, Primers in Electronics and Computer Science.

[26]  Sankar K. Pal,et al.  Web mining in soft computing framework: relevance, state of the art and future directions , 2002, IEEE Trans. Neural Networks.

[27]  Saif Mohammad,et al.  CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON , 2013, Comput. Intell..

[28]  Robin Burke,et al.  Knowledge-based recommender systems , 2000 .

[29]  Marc Bertin,et al.  K-means and Hierarchical Clustering Method to Improve our Understanding of Citation Contexts , 2017, BIRNDL@SIGIR.

[30]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[31]  Beatrice Santorini,et al.  Building a Large Annotated Corpus of English: The Penn Treebank , 1993, CL.

[32]  Dan I. Moldovan,et al.  Lexical Chains on WordNet and Extensions , 2013, FLAIRS Conference.

[33]  George Karypis,et al.  A Comparison of Document Clustering Techniques , 2000 .

[34]  Francesco Luna,et al.  Economic simulations in swarm: Agent - based modelling and object , 2000 .

[35]  Neha Narkhede,et al.  Kafka: The definitive guide , 2017 .

[36]  Francisco Herrera,et al.  Hybridizing genetic algorithms with sharing scheme and evolution strategies for designing approximate fuzzy rule-based systems , 2001, Fuzzy Sets Syst..

[37]  Erik Cambria,et al.  SeNTU: Sentiment Analysis of Tweets by Combining a Rule-based Classifier with Supervised Learning , 2015, *SEMEVAL.

[38]  Jie Liu,et al.  uEmergency: a collaborative system for emergency management on very large tabletop , 2012, ITS '12.

[39]  Sue Newell,et al.  Knowledge management and innovation: networks and networking , 1999, J. Knowl. Manag..

[40]  S Sendhilkumar,et al.  Citation Semantic Based Approaches to Identify Article Quality , 2013 .

[41]  Pablo Moreno-Ger,et al.  : Introducing educational games in the learning process , 2010, IEEE EDUCON 2010 Conference.

[42]  Erik Cambria,et al.  Deep Convolutional Neural Network Textual Features and Multiple Kernel Learning for Utterance-level Multimodal Sentiment Analysis , 2015, EMNLP.

[43]  Yanjun Qi,et al.  Sentiment Classification with Supervised Sequence Embedding , 2012, ECML/PKDD.

[44]  Kratika Gupta,et al.  Survey of Web Crawler Algorithms , 2017 .

[45]  Michael J. Pazzani,et al.  Content-Based Recommendation Systems , 2007, The Adaptive Web.

[46]  Maneesh Singh,et al.  A methodology for risk-based inspection planning of oil and gas pipes based on fuzzy logic framework , 2009 .

[47]  Marc J. Rosenberg,et al.  E-Learning: Strategies for Delivering Knowledge in the Digital Age , 2000 .

[48]  Satoshi Sekine,et al.  Named entities : recognition, classification and use , 2009 .

[49]  Judit Bar-Ilan,et al.  Which h-index? — A comparison of WoS, Scopus and Google Scholar , 2008, Scientometrics.

[50]  Enrique J. Gómez,et al.  A broadband multimedia collaborative system for advanced teleradiology and medical imaging diagnosis , 1998, IEEE Transactions on Information Technology in Biomedicine.

[51]  Wei Li,et al.  Using Genetic Algorithm for Network Intrusion Detection , 2004 .

[52]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[53]  Dorothy E. Leidner,et al.  Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues , 2001, MIS Q..

[54]  Anastasios I. Dounis,et al.  Intelligent control system for reconciliation of the energy savings with comfort in buildings using soft computing techniques , 2011 .

[55]  van Deursen Aj.  Internet skill-related problems in accessing online health information. , 2012 .

[56]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[57]  Thomas Hofmann,et al.  Probabilistic Latent Semantic Indexing , 1999, SIGIR Forum.

[58]  Reynold Xin,et al.  Apache Spark , 2016 .

[59]  Craig MacDonald,et al.  An effective statistical approach to blog post opinion retrieval , 2008, CIKM '08.

[60]  Alberto Fernández-Isabel,et al.  Extending a generic traffic model to specific agent platform requirements , 2017, Comput. Sci. Inf. Syst..

[61]  David R. Karger,et al.  Looking up data in P2P systems , 2003, CACM.

[62]  Trevor Hastie,et al.  Overview of Supervised Learning , 2001 .

[63]  Samir El-Masri,et al.  A survey of agent-based intelligent decision support systems to support clinical management and research , 2005, AAMAS 2005.

[64]  Zhendong Niu,et al.  A survey on sentiment analysis of scientific citations , 2019, Artificial Intelligence Review.

[65]  L. Zadeh,et al.  An Introduction to Fuzzy Logic Applications in Intelligent Systems , 1992 .