Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
Learning Probabilistic Models of Relational Structure
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A Markov random field model for term dependencies
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A shortest path dependency kernel for relation extraction
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Can we derive general world knowledge from texts?
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Open information extraction from the web
Communications of the ACM - Surviving the data deluge
Populating the Semantic Web by Macro-reading Internet Text
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Coupled semi-supervised learning for information extraction
Proceedings of the third ACM international conference on Web search and data mining
Relational duality: unsupervised extraction of semantic relations between entities on the web
Proceedings of the 19th international conference on World wide web
Automatically generating extraction patterns from untagged text
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Identifying relations for open information extraction
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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The ability to extract new knowledge from large datasets is one of the most significant challenges facing society. The problem spans across domains from intelligence analysis and scientific research to basic web search. Current information extraction and retrieval tools either lack the flexibility to adapt to evolving information needs or require users to sift through search results and piece together relevant information. With so much data compounded by the criticality of finding relevant information, new tools and methods are needed to discover and relate relevant pieces of information in ever expanding repositories of data. We posit that user-driven relational models are needed to collectively learn and discover fine-grained entities and relations that are relevant to a user's information need. To meet this need, we present a ranked retrieval and extraction framework for collectively learning and integrating evidence of entities and relational dependencies to predict at query time, a ranking of sentences containing the most relevant entities and relational dependencies. By using a relational model, evidence can be leveraged across entity and relation instances. By performing joint inference at query time, NLP pipeline errors are minimized, and more adaptive and discriminative models that meet the specific knowledge discovery needs of the user can be developed. Our goal is to develop user-driven relational models of entities and their relational dependencies, and a search system based on these models that allow users to search for known entities and relations, discover new relations from known entities, and discover new entities from known relations. Preliminary qualitative and quantitative evaluations demonstrate the efficacy and potential of the proposed relational modeling approach.