Journal of Algorithms
Annotea: an open RDF infrastructure for shared Web annotations
Proceedings of the 10th international conference on World Wide Web
Greedy approximation algorithms for finding dense components in a graph
APPROX '00 Proceedings of the Third International Workshop on Approximation Algorithms for Combinatorial Optimization
Finding a Maximum Density Subgraph
Finding a Maximum Density Subgraph
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
Graph summarization with bounded error
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Introduction to Information Retrieval
Introduction to Information Retrieval
ICALP '09 Proceedings of the 36th International Colloquium on Automata, Languages and Programming: Part I
Molecular Function Prediction Using Neighborhood Features
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
The link prediction problem in bipartite networks
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Supervised Machine Learning Applied to Link Prediction in Bipartite Social Networks
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Linking and building ontologies of linked data
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
Contextual ontology alignment of LOD with an upper ontology: a case study with proton
ESWC'11 Proceedings of the 8th extended semantic web conference on The semantic web: research and applications - Volume Part I
A supervised machine learning link prediction approach for tag recommendation
OCSC'11 Proceedings of the 4th international conference on Online communities and social computing
Dense subgraphs with restrictions and applications to gene annotation graphs
RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
An unsupervised approach for acquiring ontologies and RDF data from online life science databases
ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part II
Improving Wikipedia with DBpedia
Proceedings of the 21st international conference companion on World Wide Web
Data Mining and Knowledge Discovery
PAnG: finding patterns in annotation graphs
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
Finding cross genome patterns in annotation graphs
DILS'12 Proceedings of the 8th international conference on Data Integration in the Life Sciences
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Annotation graph datasets are a natural representation of scientific knowledge. They are common in the life sciences where genes or proteins are annotated with controlled vocabulary terms (CV terms) from ontologies. The W3C Linking Open Data (LOD) initiative and semantic Web technologies are playing a leading role in making such datasets widely available. Scientists can mine these datasets to discover patterns of annotation. While ontology alignment and integration across datasets has been explored in the context of the semantic Web, there is no current approach to mine such patterns in annotation graph datasets. In this paper, we propose a novel approach for link prediction; it is a preliminary task when discovering more complex patterns. Our prediction is based on a complementary methodology of graph summarization (GS) and dense subgraphs (DSG). GS can exploit and summarize knowledge captured within the ontologies and in the annotation patterns. DSG uses the ontology structure, in particular the distance between CV terms, to filter the graph, and to find promising subgraphs. We develop a scoring function based on multiple heuristics to rank the predictions. We perform an extensive evaluation on Arabidopsis thaliana genes.