The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
The information-seeking practices of engineers: searching for documents as well as for people
Information Processing and Management: an International Journal
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Information Retrieval
Learning to Probabilistically Identify Authoritative Documents
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
The Journal of Machine Learning Research
Retrieval evaluation with incomplete information
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
ICML '05 Proceedings of the 22nd international conference on Machine learning
Text mining techniques for patent analysis
Information Processing and Management: an International Journal
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A Topic Modeling Approach and Its Integration into the Random Walk Framework for Academic Search
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A Combination Approach to Web User Profiling
ACM Transactions on Knowledge Discovery from Data (TKDD)
Topic level expertise search over heterogeneous networks
Machine Learning
Mining competitive relationships by learning across heterogeneous networks
Proceedings of the 21st ACM international conference on Information and knowledge management
Patent partner recommendation in enterprise social networks
Proceedings of the sixth ACM international conference on Web search and data mining
SAE: social analytic engine for large networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploratory analysis of highly heterogeneous document collections
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining structural hole spanners through information diffusion in social networks
Proceedings of the 22nd international conference on World Wide Web
CV-PCR: a context-guided value-driven framework for patent citation recommendation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Hi-index | 0.00 |
Patenting is one of the most important ways to protect company's core business concepts and proprietary technologies. Analyzing large volume of patent data can uncover the potential competitive or collaborative relations among companies in certain areas, which can provide valuable information to develop strategies for intellectual property (IP), R&D, and marketing. In this paper, we present a novel topic-driven patent analysis and mining system. Instead of merely searching over patent content, we focus on studying the heterogeneous patent network derived from the patent database, which is represented by several types of objects (companies, inventors, and technical content) jointly evolving over time. We design and implement a general topic-driven framework for analyzing and mining the heterogeneous patent network. Specifically, we propose a dynamic probabilistic model to characterize the topical evolution of these objects within the patent network. Based on this modeling framework, we derive several patent analytics tools that can be directly used for IP and R&D strategy planning, including a heterogeneous network co-ranking method, a topic-level competitor evolution analysis algorithm, and a method to summarize the search results. We evaluate the proposed methods on a real-world patent database. The experimental results show that the proposed techniques clearly outperform the corresponding baseline methods.