A Pheromone-Like Model for Semantic Context Extraction from Collaborative Networks

The extraction of semantic contexts is a relevant issue in information retrieval to provide high quality query results. This paper introduces the semantic context underlying a set of given input concepts as defined by the relevant multiple explanation paths connecting the input concepts in a collaborative network. A pheromone-like model based on this approach is introduced for the detection and the extraction of multiple paths of explanation between seed concepts. The exploration of the online collaborative network of explanation uses a heuristic driven random walk, based on semantic proximity measures. Random walks distribute pheromone on the traversed arcs used to evaluate the relevance of concepts in the multiple explanatory paths to be extracted. Experimental results obtained on accepted datasets and contexts extracted from the Wikipedia collaborative network show that the proposed algorithm can extract contexts with high relevance degree, which outperforms other methods. The approach has a general applicability and can be extended to other explanation-based online collaborative networks.

[1]  Athina Markopoulou,et al.  On the bias of BFS , 2010, ArXiv.

[2]  Valentina Franzoni,et al.  Semantic context extraction from collaborative networks , 2015, 2015 IEEE 19th International Conference on Computer Supported Cooperative Work in Design (CSCWD).

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

[4]  Valentina Franzoni,et al.  Heuristic semantic walk for concept chaining in collaborative networks , 2014, Int. J. Web Inf. Syst..

[5]  J. A. NEWELLt,et al.  Empirical Explorations of the Logic Theory Machine : A Case Study in Heuristic , .

[6]  Rong Yan,et al.  Semantic concept-based query expansion and re-ranking for multimedia retrieval , 2007, ACM Multimedia.

[7]  Stefano Ceri,et al.  Model-driven development of context-aware Web applications , 2007, TOIT.

[8]  Thomas Stützle,et al.  Ant Colony Optimization Theory , 2004 .

[9]  Hamideh Afsarmanesh,et al.  Collaborative Networks: Reference Modeling , 2008 .

[10]  Valentina Franzoni,et al.  Semantic Heuristic Search in Collaborative Networks: Measures and Contexts , 2014, 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT).

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

[12]  Valentina Franzoni,et al.  Multi-path traces in semantic graphs for latent knowledge elicitation , 2015, 2015 11th International Conference on Natural Computation (ICNC).

[13]  Rajdeep Niyogi,et al.  Improving Link Ranking Quality by Quasi-Common Neighbourhood , 2015, 2015 15th International Conference on Computational Science and Its Applications.

[14]  Anind K. Dey,et al.  Understanding and Using Context , 2001, Personal and Ubiquitous Computing.

[15]  Gregory D. Abowd,et al.  Towards a Better Understanding of Context and Context-Awareness , 1999, HUC.

[16]  Alfredo Milani,et al.  Optimal Planning with ACO , 2009, AI*IA.

[17]  Valentina Franzoni,et al.  Heuristics for Semantic Path Search in Wikipedia , 2014, ICCSA.

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

[19]  Antonio Torralba,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence 1 80 Million Tiny Images: a Large Dataset for Non-parametric Object and Scene Recognition , 2022 .

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

[21]  Bill N. Schilit,et al.  Context-aware computing applications , 1994, Workshop on Mobile Computing Systems and Applications.

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

[23]  Henry Lieberman,et al.  Letizia: An Agent That Assists Web Browsing , 1995, IJCAI.

[24]  Duncan J. Watts,et al.  The Structure and Dynamics of Networks: (Princeton Studies in Complexity) , 2006 .

[25]  Ehud Rivlin,et al.  Placing search in context: the concept revisited , 2002, TOIS.

[26]  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.

[27]  Valentina Franzoni,et al.  Set Similarity Measures for Images Based on Collective Knowledge , 2015, ICCSA.

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

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

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

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

[32]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[33]  Robert A. Kowalski,et al.  A Proof Procedure Using Connection Graphs , 1975, JACM.

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

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