The structure-mapping engine: algorithm and examples
Artificial Intelligence
DIRT @SBT@discovery of inference rules from text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Coupled clustering: a method for detecting structural correspondence
The Journal of Machine Learning Research
Kernel methods for relation extraction
The Journal of Machine Learning Research
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Semi-automatic recognition of noun modifier relationships
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Finding parts in very large corpora
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Corpus-based Learning of Analogies and Semantic Relations
Machine Learning
Unsupervised named-entity extraction from the web: an experimental study
Artificial Intelligence
Similarity of Semantic Relations
Computational Linguistics
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Expressing implicit semantic relations without supervision
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Structured metric learning for high dimensional problems
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
WWW sits the SAT: Measuring Relational Similarity on the Web
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Measuring semantic similarity by latent relational analysis
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Query by analogical example: relational search using web search engine indices
Proceedings of the 18th ACM conference on Information and knowledge management
Relational duality: unsupervised extraction of semantic relations between entities on the web
Proceedings of the 19th international conference on World wide web
Exploiting macro and micro relations toward web intelligence
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Exploiting symmetry in relational similarity for ranking relational search results
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Multi-view bootstrapping for relation extraction by exploring web features and linguistic features
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
Relation adaptation: learning to extract novel relations with minimum supervision
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Cross-Language Latent Relational Search between Japanese and English Languages Using a Web Corpus
ACM Transactions on Asian Language Information Processing (TALIP)
Domain and function: a dual-space model of semantic relations and compositions
Journal of Artificial Intelligence Research
Effective Tag Recommendation System Based on Topic Ontology Using Wikipedia and WordNet
International Journal of Intelligent Systems
Computational approaches to sentence completion
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Improving relational similarity measurement using symmetries in proportional word analogies
Information Processing and Management: an International Journal
Mining for analogous tuples from an entity-relation graph
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Automatic Topic Ontology Construction Using Semantic Relations from WordNet and Wikipedia
International Journal of Intelligent Information Technologies
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Measuring the similarity between semantic relations that hold among entities is an important and necessary step in various Web related tasks such as relation extraction, information retrieval and analogy detection. For example, consider the case in which a person knows a pair of entities (e.g. Google, YouTube), between which a particular relation holds (e.g. acquisition). The person is interested in retrieving other such pairs with similar relations (e.g. Microsoft, Powerset). Existing keyword-based search engines cannot be applied directly in this case because, in keyword-based search, the goal is to retrieve documents that are relevant to the words used in a query -- not necessarily to the relations implied by a pair of words. We propose a relational similarity measure, using a Web search engine, to compute the similarity between semantic relations implied by two pairs of words. Our method has three components: representing the various semantic relations that exist between a pair of words using automatically extracted lexical patterns, clustering the extracted lexical patterns to identify the different patterns that express a particular semantic relation, and measuring the similarity between semantic relations using a metric learning approach. We evaluate the proposed method in two tasks: classifying semantic relations between named entities, and solving word-analogy questions. The proposed method outperforms all baselines in a relation classification task with a statistically significant average precision score of 0.74. Moreover, it reduces the time taken by Latent Relational Analysis to process 374 word-analogy questions from 9 days to less than 6 hours, with an SAT score of 51%.