We consider the problem of information retrieval evaluation and the methods and metrics used for such evaluations. We propose a probabilistic framework for evaluation which we use to develop new information-theoretic evaluation metrics. We demonstrate that these new metrics are powerful and generalizable, enabling evaluations heretofore not possible. We introduce four preliminary uses of our framework: (1) a measure of conditional rank correlation, information tau, a powerful meta-evaluation tool whose use we demonstrate on understanding novelty and diversity evaluation; (2) a new evaluation measure, relevance information correlation, which is correlated with traditional evaluation measures and can be used to (3) evaluate a collection of systems simultaneously, which provides a natural upper bound on metasearch performance; and (4) a measure of the similarity between rankers on judged documents, information difference, which allows us to determine whether systems with similar performance are in fact different.
我们考虑了信息检索评估的问题以及用于此类评估的方法和度量。我们提出了一个评估的概率框架，我们使用它来开发新的信息论评估指标。我们证明了这些新的度量标准是强大的和可推广的，使得以前不可能的评估成为可能。我们介绍了我们的框架的四种初步用途:(1)条件等级相关性的测量，信息tau，一个强大的元评估工具，我们展示了它在理解新颖性和多样性评估方面的用途;(2)一种新的评价指标——关联信息相关性(relevance information correlation)，它与传统的评价指标相关联，可用于(3)同时评价一组系统，这为元搜索性能提供了一个自然的上限;(4)衡量被评判文件的排名者之间的相似性，信息差异，这使我们能够确定具有相似性能的系统是否实际上不同。
{"title":"A mutual information-based framework for the analysis of information retrieval systems","authors":"Peter B. Golbus, J. Aslam","doi":"10.1145/2484028.2484073","DOIUrl":"https://doi.org/10.1145/2484028.2484073","url":null,"abstract":"We consider the problem of information retrieval evaluation and the methods and metrics used for such evaluations. We propose a probabilistic framework for evaluation which we use to develop new information-theoretic evaluation metrics. We demonstrate that these new metrics are powerful and generalizable, enabling evaluations heretofore not possible. We introduce four preliminary uses of our framework: (1) a measure of conditional rank correlation, information tau, a powerful meta-evaluation tool whose use we demonstrate on understanding novelty and diversity evaluation; (2) a new evaluation measure, relevance information correlation, which is correlated with traditional evaluation measures and can be used to (3) evaluate a collection of systems simultaneously, which provides a natural upper bound on metasearch performance; and (4) a measure of the similarity between rankers on judged documents, information difference, which allows us to determine whether systems with similar performance are in fact different.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123557820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we present a medical record search system which is useful for identifying cohorts required in clinical studies. In particular, we propose a query-adaptive weighting method that can dynamically aggregate and score evidence in multiple medical reports (from different hospital departments or from different tests within the same department) of a patient. Furthermore, we explore several informative features for learning our retrieval model.
{"title":"An adaptive evidence weighting method for medical record search","authors":"Dongqing Zhu, Ben Carterette","doi":"10.1145/2484028.2484175","DOIUrl":"https://doi.org/10.1145/2484028.2484175","url":null,"abstract":"In this paper, we present a medical record search system which is useful for identifying cohorts required in clinical studies. In particular, we propose a query-adaptive weighting method that can dynamically aggregate and score evidence in multiple medical reports (from different hospital departments or from different tests within the same department) of a patient. Furthermore, we explore several informative features for learning our retrieval model.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122112287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Filtering a time-ordered corpus for documents that are highly relevant to an entity is a task receiving more and more attention over the years. One application is to reduce the delay between the moment an information about an entity is being first observed and the moment the entity entry in a knowledge base is being updated. Current state-of-the-art approaches are highly supervised and require training examples for each entity monitored. We propose an approach which does not require new training data when processing a new entity. To capture intrinsic characteristics of highly relevant documents our approach relies on three types of features: document centric features, entity profile related features and time features. Evaluated within the framework of the "Knowledge Base Acceleration" track at TREC 2012, it outperforms current state-of-the-art approaches.
{"title":"A weakly-supervised detection of entity central documents in a stream","authors":"L. Bonnefoy, Vincent Bouvier, P. Bellot","doi":"10.1145/2484028.2484180","DOIUrl":"https://doi.org/10.1145/2484028.2484180","url":null,"abstract":"Filtering a time-ordered corpus for documents that are highly relevant to an entity is a task receiving more and more attention over the years. One application is to reduce the delay between the moment an information about an entity is being first observed and the moment the entity entry in a knowledge base is being updated. Current state-of-the-art approaches are highly supervised and require training examples for each entity monitored. We propose an approach which does not require new training data when processing a new entity. To capture intrinsic characteristics of highly relevant documents our approach relies on three types of features: document centric features, entity profile related features and time features. Evaluated within the framework of the \"Knowledge Base Acceleration\" track at TREC 2012, it outperforms current state-of-the-art approaches.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124425232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
1. ABSTRACT Predicting when online searchers terminate search for information tasks without obvious end-points is a challenging task. Previous research concludes that people stop based on intuitions of enough [4], yet few studies have systematically examined online search stopping behavior. For open-ended search tasks, searchers often have to reformulate their queries in order to obtain a sufficient amount of information, which means that before searchers quit searching for a task entirely (stopping at the task level), they also stop result evaluation for different queries during the search (stopping at the query level).
{"title":"How far will you go?: characterizing and predicting online search stopping behavior using information scent and need for cognition","authors":"Wan-Ching Wu","doi":"10.1145/2484028.2484232","DOIUrl":"https://doi.org/10.1145/2484028.2484232","url":null,"abstract":"1. ABSTRACT Predicting when online searchers terminate search for information tasks without obvious end-points is a challenging task. Previous research concludes that people stop based on intuitions of enough [4], yet few studies have systematically examined online search stopping behavior. For open-ended search tasks, searchers often have to reformulate their queries in order to obtain a sufficient amount of information, which means that before searchers quit searching for a task entirely (stopping at the task level), they also stop result evaluation for different queries during the search (stopping at the query level).","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129441596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper we present the IRSA framework that enables the automatic creation of search term suggestion or recommendation systems (TS). Such TS are used to operationalize interactive query expansion and help users in refining their information need in the query formulation phase. Our recent research has shown TS to be more effective when specific to a certain domain. The presented technical framework allows owners of Digital Libraries to create their own specific TS constructed via OAI-harvested metadata with very little effort.
{"title":"A framework for specific term recommendation systems","authors":"Thomas Lüke, Philipp Schaer, Philipp Mayr","doi":"10.1145/2484028.2484207","DOIUrl":"https://doi.org/10.1145/2484028.2484207","url":null,"abstract":"In this paper we present the IRSA framework that enables the automatic creation of search term suggestion or recommendation systems (TS). Such TS are used to operationalize interactive query expansion and help users in refining their information need in the query formulation phase. Our recent research has shown TS to be more effective when specific to a certain domain. The presented technical framework allows owners of Digital Libraries to create their own specific TS constructed via OAI-harvested metadata with very little effort.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129754807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Web search queries are often ambiguous or multi-faceted, which makes a simple ranked list of results inadequate. To assist information finding for such faceted queries, we explore a technique that explicitly represents interesting facets of a query using groups of semantically related terms extracted from search results. As an example, for the query ``baggage allowance'', these groups might be different airlines, different flight types (domestic, international), or different travel classes (first, business, economy). We name these groups query facets and the terms in these groups facet terms. We develop a supervised approach based on a graphical model to recognize query facets from the noisy candidates found. The graphical model learns how likely a candidate term is to be a facet term as well as how likely two terms are to be grouped together in a query facet, and captures the dependencies between the two factors. We propose two algorithms for approximate inference on the graphical model since exact inference is intractable. Our evaluation combines recall and precision of the facet terms with the grouping quality. Experimental results on a sample of web queries show that the supervised method significantly outperforms existing approaches, which are mostly unsupervised, suggesting that query facet extraction can be effectively learned.
{"title":"Extracting query facets from search results","authors":"Weize Kong, James Allan","doi":"10.1145/2484028.2484097","DOIUrl":"https://doi.org/10.1145/2484028.2484097","url":null,"abstract":"Web search queries are often ambiguous or multi-faceted, which makes a simple ranked list of results inadequate. To assist information finding for such faceted queries, we explore a technique that explicitly represents interesting facets of a query using groups of semantically related terms extracted from search results. As an example, for the query ``baggage allowance'', these groups might be different airlines, different flight types (domestic, international), or different travel classes (first, business, economy). We name these groups query facets and the terms in these groups facet terms. We develop a supervised approach based on a graphical model to recognize query facets from the noisy candidates found. The graphical model learns how likely a candidate term is to be a facet term as well as how likely two terms are to be grouped together in a query facet, and captures the dependencies between the two factors. We propose two algorithms for approximate inference on the graphical model since exact inference is intractable. Our evaluation combines recall and precision of the facet terms with the grouping quality. Experimental results on a sample of web queries show that the supervised method significantly outperforms existing approaches, which are mostly unsupervised, suggesting that query facet extraction can be effectively learned.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129910067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Quan Yuan, G. Cong, Zongyang Ma, Aixin Sun, N. Magnenat-Thalmann
The availability of user check-in data in large volume from the rapid growing location based social networks (LBSNs) enables many important location-aware services to users. Point-of-interest (POI) recommendation is one of such services, which is to recommend places where users have not visited before. Several techniques have been recently proposed for the recommendation service. However, no existing work has considered the temporal information for POI recommendations in LBSNs. We believe that time plays an important role in POI recommendations because most users tend to visit different places at different time in a day, eg visiting a restaurant at noon and visiting a bar at night. In this paper, we define a new problem, namely, the time-aware POI recommendation, to recommend POIs for a given user at a specified time in a day. To solve the problem, we develop a collaborative recommendation model that is able to incorporate temporal information. Moreover, based on the observation that users tend to visit nearby POIs, we further enhance the recommendation model by considering geographical information. Our experimental results on two real-world datasets show that the proposed approach outperforms the state-of-the-art POI recommendation methods substantially.
{"title":"Time-aware point-of-interest recommendation","authors":"Quan Yuan, G. Cong, Zongyang Ma, Aixin Sun, N. Magnenat-Thalmann","doi":"10.1145/2484028.2484030","DOIUrl":"https://doi.org/10.1145/2484028.2484030","url":null,"abstract":"The availability of user check-in data in large volume from the rapid growing location based social networks (LBSNs) enables many important location-aware services to users. Point-of-interest (POI) recommendation is one of such services, which is to recommend places where users have not visited before. Several techniques have been recently proposed for the recommendation service. However, no existing work has considered the temporal information for POI recommendations in LBSNs. We believe that time plays an important role in POI recommendations because most users tend to visit different places at different time in a day, eg visiting a restaurant at noon and visiting a bar at night. In this paper, we define a new problem, namely, the time-aware POI recommendation, to recommend POIs for a given user at a specified time in a day. To solve the problem, we develop a collaborative recommendation model that is able to incorporate temporal information. Moreover, based on the observation that users tend to visit nearby POIs, we further enhance the recommendation model by considering geographical information. Our experimental results on two real-world datasets show that the proposed approach outperforms the state-of-the-art POI recommendation methods substantially.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128519015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There is a long history of developing efficient algorithms for set intersection, which is a fundamental operation in information retrieval and databases. In this paper, we describe a new data structure, a Cardinality Filter, to quickly compute an upper bound on the size of a set intersection. Knowing an upper bound of the size can be used to accelerate many applications such as top-k query processing in text mining. Given finite sets A and B, the expected computation time for the upper bound of the size of the intersection |A cap B| is O( (|A| + |B|) w), where w is the machine word length. This is much faster than the current best algorithm for the exact intersection, which runs in O((|A| + |B|) / √w + |A cap B|) expected time. Our performance studies show that our implementations of Cardinality Filters are from 2 to 10 times faster than existing set intersection algorithms, and the time for a top-k query in a text mining application can be reduced by half.
集合交集是信息检索和数据库中的一项基本操作，其高效算法的开发已有很长的历史。在本文中，我们描述了一种新的数据结构，即基数过滤器，用于快速计算集合交集大小的上界。知道大小的上界可以用来加速许多应用程序，例如文本挖掘中的top-k查询处理。给定有限集合A和B，交集|A cap B|大小的上界的期望计算时间为O((|A| + |B|) w)，其中w为机器字长。这比目前最好的精确交集算法要快得多，后者的预期时间为O((|A| + |B|) /√w + |A cap B|)。我们的性能研究表明，我们的Cardinality Filters的实现比现有的集合交集算法快2到10倍，并且文本挖掘应用程序中top-k查询的时间可以减少一半。
{"title":"Faster upper bounding of intersection sizes","authors":"Daisuke Takuma, H. Yanagisawa","doi":"10.1145/2484028.2484065","DOIUrl":"https://doi.org/10.1145/2484028.2484065","url":null,"abstract":"There is a long history of developing efficient algorithms for set intersection, which is a fundamental operation in information retrieval and databases. In this paper, we describe a new data structure, a Cardinality Filter, to quickly compute an upper bound on the size of a set intersection. Knowing an upper bound of the size can be used to accelerate many applications such as top-k query processing in text mining. Given finite sets A and B, the expected computation time for the upper bound of the size of the intersection |A cap B| is O( (|A| + |B|) w), where w is the machine word length. This is much faster than the current best algorithm for the exact intersection, which runs in O((|A| + |B|) / √w + |A cap B|) expected time. Our performance studies show that our implementations of Cardinality Filters are from 2 to 10 times faster than existing set intersection algorithms, and the time for a top-k query in a text mining application can be reduced by half.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128589684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. H. Dalip, Marcos André Gonçalves, Marco Cristo, P. Calado
Collaborative web sites, such as collaborative encyclopedias, blogs, and forums, are characterized by a loose edit control, which allows anyone to freely edit their content. As a consequence, the quality of this content raises much concern. To deal with this, many sites adopt manual quality control mechanisms. However, given their size and change rate, manual assessment strategies do not scale and content that is new or unpopular is seldom reviewed. This has a negative impact on the many services provided, such as ranking and recommendation. To tackle with this problem, we propose a learning to rank (L2R) approach for ranking answers in Q&A forums. In particular, we adopt an approach based on Random Forests and represent query and answer pairs using eight different groups of features. Some of these features are used in the Q&A domain for the first time. Our L2R method was trained to learn the answer rating, based on the feedback users give to answers in Q&A forums. Using the proposed method, we were able (i) to outperform a state of the art baseline with gains of up to 21% in NDCG, a metric used to evaluate rankings; we also conducted a comprehensive study of the features, showing that (ii) review and user features are the most important in the Q&A domain although text features are useful for assessing quality of new answers; and (iii) the best set of new features we proposed was able to yield the best quality rankings.
{"title":"Exploiting user feedback to learn to rank answers in q&a forums: a case study with stack overflow","authors":"D. H. Dalip, Marcos André Gonçalves, Marco Cristo, P. Calado","doi":"10.1145/2484028.2484072","DOIUrl":"https://doi.org/10.1145/2484028.2484072","url":null,"abstract":"Collaborative web sites, such as collaborative encyclopedias, blogs, and forums, are characterized by a loose edit control, which allows anyone to freely edit their content. As a consequence, the quality of this content raises much concern. To deal with this, many sites adopt manual quality control mechanisms. However, given their size and change rate, manual assessment strategies do not scale and content that is new or unpopular is seldom reviewed. This has a negative impact on the many services provided, such as ranking and recommendation. To tackle with this problem, we propose a learning to rank (L2R) approach for ranking answers in Q&A forums. In particular, we adopt an approach based on Random Forests and represent query and answer pairs using eight different groups of features. Some of these features are used in the Q&A domain for the first time. Our L2R method was trained to learn the answer rating, based on the feedback users give to answers in Q&A forums. Using the proposed method, we were able (i) to outperform a state of the art baseline with gains of up to 21% in NDCG, a metric used to evaluate rankings; we also conducted a comprehensive study of the features, showing that (ii) review and user features are the most important in the Q&A domain although text features are useful for assessing quality of new answers; and (iii) the best set of new features we proposed was able to yield the best quality rankings.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126858544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Current approaches for search result diversification have been categorized as either implicit or explicit. The implicit approach assumes each document represents its own topic, and promotes diversity by selecting documents for different topics based on the difference of their vocabulary. On the other hand, the explicit approach models the set of query topics, or aspects. While the former approach is generally less effective, the latter usually depends on a manually created description of the query aspects, the automatic construction of which has proven difficult. This paper introduces a new approach: term-level diversification. Instead of modeling the set of query aspects, which are typically represented as coherent groups of terms, our approach uses terms without the grouping. Our results on the ClueWeb collection show that the grouping of topic terms provides very little benefit to diversification compared to simply using the terms themselves. Consequently, we demonstrate that term-level diversification, with topic terms identified automatically from the search results using a simple greedy algorithm, significantly outperforms methods that attempt to create a full topic structure for diversification.
{"title":"Term level search result diversification","authors":"Van Dang, W. Bruce Croft","doi":"10.1145/2484028.2484095","DOIUrl":"https://doi.org/10.1145/2484028.2484095","url":null,"abstract":"Current approaches for search result diversification have been categorized as either implicit or explicit. The implicit approach assumes each document represents its own topic, and promotes diversity by selecting documents for different topics based on the difference of their vocabulary. On the other hand, the explicit approach models the set of query topics, or aspects. While the former approach is generally less effective, the latter usually depends on a manually created description of the query aspects, the automatic construction of which has proven difficult. This paper introduces a new approach: term-level diversification. Instead of modeling the set of query aspects, which are typically represented as coherent groups of terms, our approach uses terms without the grouping. Our results on the ClueWeb collection show that the grouping of topic terms provides very little benefit to diversification compared to simply using the terms themselves. Consequently, we demonstrate that term-level diversification, with topic terms identified automatically from the search results using a simple greedy algorithm, significantly outperforms methods that attempt to create a full topic structure for diversification.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123416611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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