Approximate string-matching with q-grams and maximal matches
Theoretical Computer Science - Selected papers of the Combinatorial Pattern Matching School
A New Algorithm for Error-Tolerant Subgraph Isomorphism Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A graph distance metric based on the maximal common subgraph
Pattern Recognition Letters
An Algorithm for Subgraph Isomorphism
Journal of the ACM (JACM)
A vector space model for automatic indexing
Communications of the ACM
Algorithmics and applications of tree and graph searching
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
An Empirical Study of Domain Knowledge and Its Benefits to Substructure Discovery
IEEE Transactions on Knowledge and Data Engineering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Similarity evaluation on tree-structured data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Substructure similarity search in graph databases
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Graph indexing based on discriminative frequent structure analysis
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2004
Novel approaches for small biomolecule classification and structural similarity search
ACM SIGKDD Explorations Newsletter - Special issue on data mining for health informatics
A comparison of three maximum common subgraph algorithms on a large database of labeled graphs
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
RecipeCrawler: collecting recipe data from WWW incrementally
WAIM '06 Proceedings of the 7th international conference on Advances in Web-Age Information Management
A comparative analysis of similarity measurement techniques through SimReq framework
Proceedings of the 7th International Conference on Frontiers of Information Technology
Deriving a recipe similarity measure for recommending healthful meals
Proceedings of the 16th international conference on Intelligent user interfaces
Exploring folksonomy and cooking procedures to boost cooking recipe recommendation
APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
Community-based recipe recommendation and adaptation in peer-to-peer networks
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
EGDIM: evolving graph database indexing method
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
Recipe recommendation using ingredient networks
Proceedings of the 3rd Annual ACM Web Science Conference
Intelligent menu planning: recommending set of recipes by ingredients
Proceedings of the ACM multimedia 2012 workshop on Multimedia for cooking and eating activities
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Improving the precision of information retrieval has been a challenging issue on Chinese Web. As exemplified by Chinese recipes on the Web, it is not easy/natural for people to use keywords (e.g. recipe names) to search recipes, since the names can be literally so abstract that they do not bear much, if any, information on the underlying ingredients or cooking methods. In this paper, we investigate the underlying features of Chinese recipes, and based on workflow-like cooking procedures, we model recipes as graphs. We further propose a novel similarity measurement based on the frequent patterns, and devise an effective filtering algorithm to prune unrelated data so as to support efficient on-line searching. Benefiting from the characteristics of graphs, frequent common patterns can be mined from a cooking graph database. So in our prototype system called RecipeView, we extend the subgraph mining algorithm FSG to cooking graphs and combine it with our proposed similarity measurement, resulting in an approach that well caters for specific users' needs. Our initial experimental studies show that the filtering algorithm can efficiently prune unrelated cooking graphs without affecting the retrieval performance and the similarity measurement gets a relatively higher precision/recall against its counterparts