Heuristic semantic walk for concept chaining in collaborative networks

Purpose – In this work, a new general framework is proposed to guide navigation over a collaborative concept network, in order to discover paths between concepts. Finding semantic chains between concepts over a semantic network is an issue of great interest for many applications, such as explanation generation and query expansion. Collaborative concept networks over the web tend to have features such as large dimensions, high connectivity degree, dynamically evolution over the time, which represent special challenges for efficient graph search methods, since they result in huge memory requirements, high branching factors, unknown dimensions and high cost for accessing nodes. The paper aims to discuss these issues. Design/methodology/approach – The proposed framework is based on the novel notion of heuristic semantic walk (HSW). In the HSW framework, a semantic proximity measure among concepts, reflecting the collective knowledge embedded in search engines or other statistical sources, is used as a heurist...

[1]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  Nicu Sebe,et al.  Content-based multimedia information retrieval: State of the art and challenges , 2006, TOMCCAP.

[3]  Kristina Toutanova,et al.  Extracting Parallel Sentences from Comparable Corpora using Document Level Alignment , 2010, NAACL.

[4]  Peter D. Turney.  Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL , 2001, ECML.

[5]  Ian H. Witten,et al.  An effective, low-cost measure of semantic relatedness obtained from Wikipedia links , 2008 .

[6]  Valentina Franzoni,et al.  Collective Evolutionary Concept Distance Based Query Expansion for Effective Web Document Retrieval , 2013, ICCSA.

[7]  Marco Gori,et al.  A random-walk based scoring algorithm with application to recommender systems for large-scale e-commerce , 2006, KDD 2006.

[8]  Danushka Bollegala,et al.  A Web Search Engine-Based Approach to Measure Semantic Similarity between Words , 2011, IEEE Transactions on Knowledge and Data Engineering.

[9]  Carla Limongelli,et al.  Linear temporal logic as an executable semantics for planning languages , 2007, J. Log. Lang. Inf..

[10]  Jie Yu,et al.  Measuring semantic similarity between words by removing noise and redundancy in web snippets , 2011, Concurr. Comput. Pract. Exp..

[11]  Valentina Franzoni,et al.  Heuristic Semantic Walk - Browsing a Collaborative Network with a Search Engine-Based Heuristic , 2013, ICCSA.

[12]  Yuanxi Li,et al.  Intelligent Social Media Indexing and Sharing Using an Adaptive Indexing Search Engine , 2012, TIST.

[13]  Edward Y. Chang,et al.  Discovery of a perceptual distance function for measuring image similarity , 2003, Multimedia Systems.

[14]  Oren Etzioni,et al.  Moving Up the Information Food Chain: Deploying Softbots on the World Wide Web , 1996, AI Mag..

[15]  Eneko Agirre,et al.  WikiWalk: Random walks on Wikipedia for Semantic Relatedness , 2009, Graph-based Methods for Natural Language Processing.

[16]  Kuldip Singh,et al.  A Bidirectional Heuristic Search for web service composition with costs , 2010, Int. J. Web Grid Serv..

[17]  Jianfeng Gao,et al.  Extending query translation to cross-language query expansion with markov chain models , 2007, CIKM '07.

[18]  Alfredo Milani,et al.  Optimal Design of Web Information Contents for E-Commerce Applications , 2010, ISCIS.

[19]  F. G. Crookshank,et al.  The meaning of meaning : a study of the influence of language upon thought and of the science of symbolism , 1924 .

[20]  Paul M. B. Vitányi,et al.  The Google Similarity Distance , 2004, IEEE Transactions on Knowledge and Data Engineering.

[21]  Kenneth Ward Church,et al.  Word Association Norms, Mutual Information, and Lexicography , 1989, ACL.

[22]  Marco Gori,et al.  Exact and approximate graph matching using random walks , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Valentina Franzoni,et al.  PMING Distance: A Collaborative Semantic Proximity Measure , 2012, 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

[24]  Evgeniy Gabrilovich,et al.  Computing Semantic Relatedness Using Wikipedia-based Explicit Semantic Analysis , 2007, IJCAI.