queensland university of technology an ontology-based mining approach for user search intent...
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Queensland University of Technology
An Ontology-based Mining Approach for User Search Intent Discovery
Yan Shen, Yuefeng Li, Yue Xu, Renato Iannella, Abdulmohsen Algarni and Xiaohui Tao
ADCS 2011, 2nd Dec, Canberra
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Outline
• Introduction• Related work• Proposed Approach
– An overview of the architecture – World knowledge base– Personalized ontology construction– In-levels ontology mining method
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Outline
• Evaluation– Data collections – Measures & Baseline model– Results and findings– Discussion
• Conclusion and future work
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Introduction
• Retrieving desired information to a user is the primary objective of an effective search engines
• Many efforts are spent to improve search capabilities, e.g….
• No doubt that they are helpful, however, they are commonly encountering an issue – information mismatch (ambiguity)
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Introduction
• To overcome the issue, more and more researchers have taken ontologies into account
• The ontologies can classify diverse knowledge into a well-structured way, which facilitate users to assess information items
• Moreover, semantic relations can be considered to enhance information navigation
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Introduction
• Note that user search intent is a significant aspect to return desired information
• We study search intents into two means: Specificity and Exhaustivity intent
• A hierarchical concept level-finding technique is proposed to discover and characterize user search intents
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Introduction
• an ontology-based approach is introduced
• Library of Congress Subject Headings is applied as a world knowledge base for learning personalized ontologies
• In-levels ontology mining method is fully described
• Evaluated by 100 RCV1 topics in TREC 2002 Filtering Track
• The results indicate that the performance of top precision is improved dramatically.
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Related work
• Ontology-based techniques– Zhong proposes a learning approach for task (or domain-specic)
ontology, which employs various mining techniques and natural-language understanding methods.
– Li and Zhong present an automatic ontology learning method, in which a class is called a compound concept, assembled by primitive classes that are the smallest concepts and cannot be divided any further.
– …
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Related work
• Ontology-based techniques
– They don't consider the purpose of discovering and characterizing user search intents in a concept level.
– To extend the previous methods, the paper uses “Is-A“ relation to build a real hierarchical structure for the backbone of personalized ontologies
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Related work
• User information needs– Jiang and Tan aim to represent and capture users' interests in target
domain. Subsequently, a method, they called Spreading Activation Theory (SAT), is employed for providing personalized services.
– Tao et al. propose an ontology-based knowledge retrieval framework to capture user information needs by considering user knowledge background and user's local instance repository with association roles and data mining techniques.
– …
– They are normally either expensive in extraction or inaccurate in description.
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Proposed approach
• The paper first holds a hypothesis that a user search intent should exist somewhere in an ontology.
• The intent could be general or specific, and can be represented in a range of extent
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Proposed approach
• World knowledge base (LCSH)– In the LCSH, subject headings are basic semantic units for conveying
domain knowledge and concepts, they have three main types of references: Broader Term, Narrower Term and Related Term.
– Refine to ancestor and descendant lexical relations respectively in our approach
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Proposed approach
• World knowledge base (cont.)– Definitions
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Proposed approach
• Personalized ontology learning– Concept hierarchy is an essential object of ontology learning
– Here, we create an abstract hieratical structure
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Proposed approach
• Personalized ontology learning (cont.)– Definitions
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Proposed approach
• Personalized ontology learning (cont.)– An example
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Proposed approach
• In-Levels ontology mining method– Represent feature in levels (two objectives)
• 1) to decide subjects and weights for the pilot level;
• 2) to represent it as a query
After that, do a query expansion. Then, obtain a feature as:
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Proposed approach
• In-Levels ontology mining method (cont.)– Determine the best level for user search intents
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Evaluation
• Data collections– A LCSH (QUT Library data in 2008) database 719 mega bytes data
stored in Microsoft Office Access Database (.mdb), totally 491,250 subjects associated with semantic relations
– TREC-11 2002 Filtering Track, RCV1, totally 806,791 xml documents in training and testing sets.
– All of them are processed by the pre-processing approach (stopwords removal, stemming)
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Evaluation
• Measures & Baseline model– Top 20 precision (pr@20), the precision averages at 11 standard recall
levels (11-points), the Mean Average Precision (MAP), and the F1-Measure.
– ONTO model (Tao et al., 2010)
– Two uniform level settings in upper level 7 and lower level 2 respectively.
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Evaluation
• Discussion– The approach by only containing new terms has better performance than
the one keeps all the terms in levels
– Demonstrate the validity of the hierarchical backbone
– The experimental results are indistinct for all the measures, and those specific terms might be able to reduce recall
– The approach is suitable to situations when precision is be considered more important than others
– LCSH is difficult to keep up to date
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Conclusion
• The paper introduces an ontology-based approach to discover user search intents
• The approach involves a subject-based search model, a world knowledge base, and a in-levels ontology mining method
• The empirical results indicate that our approach works remarkable on top precision
• The main intellectual contribution is the hierarchical level-finding technique
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Future work
• Investigate the usage of the rest of semantic relations in LCSH
• Combine with pattern mining methods
• Test the approach with other world knowledge base, like WordNet or Amazon