Spreading Activation Way of Knowledge Integration

Search and recommender systems benefit from effective integration of two different kinds of knowledge. The first is introspective knowledge, typically available in feature-theoretic representations of objects. The second is external knowledge, which could be obtained from how users rate or annotate items, or collaborate over a social network. This paper presents a spreading activation model that is aimed at a principled integration of these two sources of knowledge. In order to empirically evaluate our approach, we restrict the scope to text classification tasks, where we use the category knowledge of the labeled set of examples as an external knowledge source. Our experiments show a significantly improved classification effectiveness on hard datasets, where feature value representations, on their own, are inadequate in discriminating between classes.

[1]  Gene H. Golub,et al.  Matrix computations (3rd ed.) , 1996 .

[2]  Sutanu Chakraborti,et al.  Linking Cases Up: An Extension to the Case Retrieval Network , 2014, ICCBR.

[3]  Juan M. Corchado,et al.  Applying lazy learning algorithms to tackle concept drift in spam filtering , 2007, Expert Syst. Appl..

[4]  Mario Lenz,et al.  Case Retrieval Nets: Basic Ideas and Extensions , 1996, KI.

[5]  Mario Lenz,et al.  CBR for Document Retrieval: The FALLQ Project , 1997, ICCBR.

[6]  John R. Anderson.  A spreading activation theory of memory. , 1983 .

[7]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[8]  Berthier A. Ribeiro-Neto,et al.  Combining link-based and content-based methods for web document classification , 2003, CIKM '03.

[9]  Sutanu Chakraborti,et al.  Visualizing and Evaluating Complexity of Textual Case Bases , 2008, ECCBR.

[10]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[11]  Lise Getoor,et al.  Link-Based Classification , 2003, Encyclopedia of Machine Learning and Data Mining.

[12]  Allan Collins,et al.  A spreading-activation theory of semantic processing , 1975 .

[13]  Janet L. Kolodner,et al.  Case-Based Reasoning , 1989, IJCAI 1989.

[14]  Ulrik Brandes,et al.  Pure spreading activation is pointless , 2009, CIKM.

[15]  Ludmila I. Kuncheva,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2004 .

[16]  Sutanu Chakraborti,et al.  Satisfying Varying Retrieval Requirements in Case Based Intelligent Directory Assistance , 2004, FLAIRS.

[17]  Brian D. Davison,et al.  Knowing a web page by the company it keeps , 2006, CIKM '06.

[18]  Soumen Chakrabarti,et al.  Mining the web - discovering knowledge from hypertext data , 2002 .

[19]  Avrim Blum,et al.  The Bottleneck , 2021, Monopsony Capitalism.

[20]  Fabio Crestani,et al.  Application of Spreading Activation Techniques in Information Retrieval , 1997, Artificial Intelligence Review.