This paper describes a novel approach to the semantic relation detection problem . Instead of relying only on the training instances for a new relation , we leverage the knowledge learned from previously trained relation detectors . Specifically , we detect a new semantic relation by projecting the new relation 's training instances onto a lower dimension topic space constructed from existing relation detectors through a three step process . First , we construct a large relation repository of more than 7,000 relations from Wikipedia . Second , we construct a set of non-redundant relation topics defined at multiple scales from the relation repository to characterize the existing relations . Similar to the topics defined over words , each relation topic is an interpretable multinomial distribution over the existing relations . Third , we integrate the relation topics in a kernel function , and use it together with SVM to construct detectors for new relations . The experimental results on Wikipedia and ACE data have confirmed that backgroundknowledge-based topics generated from the Wikipedia relation repository can significantly improve the performance over the state-of-theart relation detection approaches . Detecting semantic relations in text is very useful in both information retrieval and question answering because it enables knowledge bases to be leveraged to score passages and retrieve candidate answers . To extract semantic relations from text , three types of approaches have been applied . Rule-based methods ( Miller et al . , 2000 ) employ a number of linguistic rules to capture relation patterns . Featurebased methods ( Kambhatla , 2004 ; Zhao and Grishman , 2005 ) transform relation instances into a large amount of linguistic features like lexical , syntactic and semantic features , and capture the similarity between these feature vectors . Recent results mainly rely on kernel-based approaches . Many of them focus on using tree kernels to learn parse tree structure related features ( Collins and Duffy , 2001 ; Culotta and Sorensen , 2004 ; Bunescu and Mooney , 2005 ) . Other researchers study how different approaches can be combined to improve the extraction performance . For example , by combining tree kernels and convolution string kernels , ( Zhang et al . , 2006 ) achieved the state of the art performance on ACE ( ACE , 2004 ) , which is a benchmark dataset for relation extraction . Although a large set of relations have been identified , adapting the knowledge extracted from these relations for new semantic relations is still a challenging task . Most of the work on domain adaptation of relation detection has focused on how to create detectors from ground up with as little training data as possible through techniques such as bootstrapping ( Etzioni et al . , 2005 ) . We take a different approach , focusing on how the knowledge extracted from the existing relations can be reused to help build detectors for new relations . We believe by reusing knowledge one can build a more cost effective relation detector , but there are several challenges associated with reusing knowledge . The first challenge to address in this approach is how to construct a relation repository that has suffi-cient coverage . In this paper , we introduce a method that automatically extracts the knowledge characterizing more than 7,000 relations from Wikipedia . Wikipedia is comprehensive , containing a diverse body of content with significant depth and grows rapidly . Wikipedia 's infoboxes are particularly interesting for relation extraction . They are short , manually-created , and often have a relational summary of an article : a set of attribute / value pairs describing the article 's subject . Another challenge is how to deal with overlap of relations in the repository . For example , Wikipedia authors may make up a name when a new relation is needed without checking if a similar relation has already been created . This leads to relation duplication . We refine the relation repository based on an unsupervised multiscale analysis of the correlations between existing relations . This method is parameter free , and able to produce a set of non-redundant relation topics defined at multiple scales . Similar to the topics defined over words ( Blei et al . , 2003 ) , we define relation topics as multinomial distributions over the existing relations . The relation topics extracted in our approach are interpretable , orthonormal to each other , and can be used as basis relations to re-represent the new relation instances . The third challenge is how to use the relation topics for a relation detector . We map relation instances in the new domains to the relation topic space , resulting in a set of new features characterizing the relationship between the relation instances and existing relations . By doing so , background knowledge from the existing relations can be introduced into the new relations , which overcomes the limitations of the existing approaches when the training data is not sufficient . Our work fits in to a class of relation extraction research based on " distant supervision " , which studies how knowledge and resources external to the target domain can be used to improve relation extraction . ( Mintz et al . , 2009 ; Jiang , 2009 ; Chan and Roth , 2010 ) . One distinction between our approach and other existing approaches is that we represent the knowledge from distant supervision using automatically constructed topics . When we test on new instances , we do not need to search against the knowledge base . In addition , our topics also model the indirect relationship between relations . Such information can not be directly found from the knowledge base . The contributions of this paper are three-fold . Firstly , we extract a large amount of training data for more than 7,000 semantic relations from Wikipedia ( Wikipedia , 2011 ) and DBpedia ( Auer et al . , 2007 ) . A key part of this step is how we handle noisy data with little human effort . Secondly , we present an unsupervised way to construct a set of relation topics at multiple scales . This step is parameter free , and results in a nonredundant , multiscale relation topic space . Thirdly , we design a new kernel for relation detection by integrating the relation topics into the relation detector construction . The experimental results on Wikipedia and ACE data ( ACE , 2004 ) have confirmed that background-knowledge-based features generated from the Wikipedia relation repository can significantly improve the performance over the state-of-the-art relation detection approaches . This paper proposes a novel approach to create detectors for new relations integrating the knowledge extracted from the existing relations . The contributions of this paper are three-fold . Firstly , we pro- vide an automatic way to collect training data for more than 7,000 relations from Wikipedia and DBpedia . Secondly , we present an unsupervised way to construct a set of relation topics at multiple scales . Different from the topics defined over words , relation topics are defined over the existing relations . Thirdly , we design a new kernel for relation detection by integrating the relation topics in the representation of the relation instances . By leveraging the knowledge extracted from the Wikipedia relation repository , our approach significantly improves the performance over the state-of-the-art approaches on ACE data . This paper makes use of all DBpedia relations to create relation topics . It is possible that using a subset of them ( more related to the target relations ) might improve the performance . We will explore this in future work .

Challenge: Semantic relation detection is useful for information retrieval and question answering however adapting the knowledge extracted for new semantic relations remains as a challenge.
Approach: They propose to create detectors for new relations from existing relations by projecting new instances onto a lower topic space constructed from existing relation extractors.
Outcome: The proposed background-knowledge-based features generated from the Wikipedia relation repository outperform the state-of-the-art approaches on Wikipedia and ACE data.