TY - CHAP AU - Niebler, Thomas AU - Singer, Philipp AU - Benz, Dominik AU - Körner, Christian AU - Strohmaier, Markus AU - Hotho, Andreas A2 - Serdyukov, Pavel A2 - Braslavski, Pavel A2 - Kuznetsov, SergeiO. A2 - Kamps, Jaap A2 - Rüger, Stefan A2 - Agichtein, Eugene A2 - Segalovich, Ilya A2 - Yilmaz, Emine T1 - How Tagging Pragmatics Influence Tag Sense Discovery in Social Annotation Systems T2 - Advances in Information Retrieval PB - Springer Berlin Heidelberg CY - PY - 2013/ VL - 7814 IS - SP - 86 EP - 97 UR - http://dx.doi.org/10.1007/978-3-642-36973-5_8 M3 - 10.1007/978-3-642-36973-5_8 KW - pragmtics KW - tagging KW - itegpub KW - ecir13 KW - discovery KW - l3s KW - 2013 KW - iteg KW - sense L1 - SN - 978-3-642-36972-8 N1 - N1 - AB - The presence of emergent semantics in social annotation systems has been reported in numerous studies. Two important problems in this context are the induction of semantic relations among tags and the discovery of different senses of a given tag. While a number of approaches for discovering tag senses exist, little is known about which ER - TY - CHAP AU - Niebler, Thomas AU - Singer, Philipp AU - Benz, Dominik AU - Körner, Christian AU - Strohmaier, Markus AU - Hotho, Andreas A2 - Serdyukov, Pavel A2 - Braslavski, Pavel A2 - Kuznetsov, SergeiO. A2 - Kamps, Jaap A2 - Rüger, Stefan A2 - Agichtein, Eugene A2 - Segalovich, Ilya A2 - Yilmaz, Emine T1 - How Tagging Pragmatics Influence Tag Sense Discovery in Social Annotation Systems T2 - Advances in Information Retrieval PB - Springer Berlin Heidelberg CY - PY - 2013/ VL - 7814 IS - SP - 86 EP - 97 UR - http://dx.doi.org/10.1007/978-3-642-36973-5_8 M3 - 10.1007/978-3-642-36973-5_8 KW - pragmtics KW - tagging KW - ecir13 KW - discovery KW - 2013 KW - myown KW - sense L1 - SN - 978-3-642-36972-8 N1 - How Tagging Pragmatics Influence Tag Sense Discovery in Social Annotation Systems - Springer N1 - AB - The presence of emergent semantics in social annotation systems has been reported in numerous studies. Two important problems in this context are the induction of semantic relations among tags and the discovery of different senses of a given tag. While a number of approaches for discovering tag senses exist, little is known about which ER - TY - JOUR AU - Strohmaier, Markus AU - Helic, Denis AU - Benz, Dominik AU - Körner, Christian AU - Kern, Roman T1 - Evaluation of Folksonomy Induction Algorithms JO - Transactions on Intelligent Systems and Technology PY - 2012/ VL - IS - SP - EP - UR - http://tist.acm.org/index.html M3 - KW - itegpub KW - folksonomies KW - ontology_learning KW - evaluation L1 - SN - N1 - N1 - AB - ER - TY - JOUR AU - Strohmaier, Markus AU - Helic, Denis AU - Benz, Dominik AU - Körner, Christian AU - Kern, Roman T1 - Evaluation of Folksonomy Induction Algorithms JO - Transactions on Intelligent Systems and Technology PY - 2012/ VL - IS - SP - EP - UR - http://tist.acm.org/index.html M3 - KW - folksonomies KW - ontology_learning KW - myown KW - 2012 KW - evaluation L1 - SN - N1 - to appear N1 - AB - ER - TY - JOUR AU - Strohmaier, Markus AU - Körner, Christian AU - Kern, Roman T1 - Understanding why users tag: A survey of tagging motivation literature and results from an empirical study JO - Web Semantics: Science, Services and Agents on the World Wide Web PY - 2012/ VL - 17 IS - 0 SP - 1 EP - 11 UR - http://www.sciencedirect.com/science/article/pii/S1570826812000820 M3 - 10.1016/j.websem.2012.09.003 KW - tagging KW - survey KW - understanding KW - motivation KW - weblog KW - mining KW - users L1 - SN - N1 - ScienceDirect.com - Web Semantics: Science, Services and Agents on the World Wide Web - Understanding why users tag: A survey of tagging motivation literature and results from an empirical study N1 - AB - While recent progress has been achieved in understanding the structure and dynamics of social tagging systems, we know little about the underlying user motivations for tagging, and how they influence resulting folksonomies and tags. This paper addresses three issues related to this question. (1) What distinctions of user motivations are identified by previous research, and in what ways are the motivations of users amenable to quantitative analysis? (2) To what extent does tagging motivation vary across different social tagging systems? (3) How does variability in user motivation influence resulting tags and folksonomies? In this paper, we present measures to detect whether a tagger is primarily motivated by categorizing or describing resources, and apply these measures to datasets from seven different tagging systems. Our results show that (a) users’ motivation for tagging varies not only across, but also within tagging systems, and that (b) tag agreement among users who are motivated by categorizing resources is significantly lower than among users who are motivated by describing resources. Our findings are relevant for (1) the development of tag-based user interfaces, (2) the analysis of tag semantics and (3) the design of search algorithms for social tagging systems. ER - TY - CONF AU - Benz, Dominik AU - Körner, Christian AU - Hotho, Andreas AU - Stumme, Gerd AU - Strohmaier, Markus A2 - Antoniou, Grigoris A2 - Grobelnik, Marko A2 - Simperl, Elena A2 - Parsia, Bijan A2 - Plexousakis, Dimitris A2 - Pan, Jeff A2 - Leenheer, Pieter De T1 - One Tag to Bind Them All : Measuring Term Abstractness in Social Metadata T2 - Proceedings of the 8th Extended Semantic Web Conference (ESWC 2011) PB - CY - Heraklion, Crete PY - 2011/05 M2 - VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/pub/pdf/benz2011measuring.pdf M3 - KW - tagging KW - itegpub KW - abstractness KW - 2011 KW - l3s KW - myown KW - seminar KW - summer L1 - SN - N1 - N1 - AB - Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tag abstractness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of word abstractness, we first use several large-scale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and Wikitaxonomy. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data. ER - TY - CONF AU - Benz, Dominik AU - Körner, Christian AU - Hotho, Andreas AU - Stumme, Gerd AU - Strohmaier, Markus A2 - Antoniou, Grigoris A2 - Grobelnik, Marko A2 - Simperl, Elena A2 - Parsia, Bijan A2 - Plexousakis, Dimitris A2 - Pan, Jeff A2 - Leenheer, Pieter De T1 - One Tag to Bind Them All : Measuring Term Abstractness in Social Metadata T2 - Proceedings of the 8th Extended Semantic Web Conference (ESWC 2011) PB - CY - Heraklion, Crete PY - 2011/05 M2 - VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/pub/pdf/benz2011measuring.pdf M3 - KW - abstractness KW - 2011 KW - social KW - myown KW - measuring KW - term L1 - SN - N1 - N1 - AB - Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tag abstractness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of word abstractness, we first use several large-scale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and Wikitaxonomy. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data. ER - TY - CONF AU - Benz, Dominik AU - Körner, Christian AU - Hotho, Andreas AU - Stumme, Gerd AU - Strohmaier, Markus A2 - Antoniou, Grigoris A2 - Grobelnik, Marko A2 - Simperl, Elena A2 - Parsia, Bijan A2 - Plexousakis, Dimitris A2 - Pan, Jeff A2 - Leenheer, Pieter De T1 - One Tag to Bind Them All : Measuring Term Abstractness in Social Metadata T2 - Proceedings of the 8th Extended Semantic Web Conference (ESWC 2011) PB - CY - Heraklion, Crete PY - 2011/05 M2 - VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/pub/pdf/benz2011measuring.pdf M3 - KW - venus KW - term_abstractness KW - 2011 KW - eswc KW - myown KW - tag_abstractness L1 - SN - N1 - N1 - AB - Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tag abstractness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of word abstractness, we first use several large-scale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and Wikitaxonomy. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data. ER - TY - CONF AU - Benz, Dominik AU - Körner, Christian AU - Hotho, Andreas AU - Stumme, Gerd AU - Strohmaier, Markus A2 - Antoniou, Grigoris A2 - Grobelnik, Marko A2 - Simperl, Elena A2 - Parsia, Bijan A2 - Plexousakis, Dimitris A2 - Pan, Jeff A2 - Leenheer, Pieter De T1 - One Tag to Bind Them All: Measuring Term Abstractness in Social Metadata T2 - Proceedings of the 8th Extended Semantic Web Conference (ESWC 2011) PB - CY - Heraklion, Crete PY - 2011/05 M2 - VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/pub/pdf/benz2011measuring.pdf M3 - KW - itegpub KW - abstractness KW - 2011 KW - social KW - bind KW - myown KW - measuring KW - term L1 - SN - N1 - N1 - AB - Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tag abstractness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of word abstractness, we first use several large-scale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and Wikitaxonomy. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data. ER - TY - CONF AU - Benz, Dominik AU - Körner, Christian AU - Hotho, Andreas AU - Stumme, Gerd AU - Strohmaier, Markus A2 - T1 - One Tag to Bind Them All: Measuring Term Abstractness in Social Metadata T2 - Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation PB - CY - PY - 2011/ M2 - VL - IS - SP - EP - UR - M3 - KW - itegpub KW - abstractness KW - metadata KW - 2011 KW - social KW - myown KW - measuring L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Benz, Dominik AU - Körner, Christian AU - Hotho, Andreas AU - Stumme, Gerd AU - Strohmaier, Markus A2 - T1 - One Tag to Bind Them All: Measuring Term Abstractness in Social Metadata T2 - Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation PB - CY - PY - 2011/ M2 - VL - IS - SP - EP - UR - M3 - KW - abstractness KW - metadata KW - 2011 KW - social KW - myown KW - measuring L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Körner, Christian AU - Kern, Roman AU - Grahsl, Hans-Peter AU - Strohmaier, Markus A2 - T1 - Of categorizers and describers: an evaluation of quantitative measures for tagging motivation T2 - HT '10: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia PB - ACM CY - New York, NY, USA PY - 2010/ M2 - VL - IS - SP - 157 EP - 166 UR - http://portal.acm.org/citation.cfm?id=1810617.1810645 M3 - 10.1145/1810617.1810645 KW - tagging KW - folksonomy KW - motivation KW - categorizer KW - describer L1 - SN - 978-1-4503-0041-4 N1 - N1 - AB - While recent research has advanced our understanding about the structure and dynamics of social tagging systems, we know little about (i) the underlying motivations for tagging (why users tag), and (ii) how they influence the properties of resulting tags and folksonomies. In this paper, we focus on problem (i) based on a distinction between two types of user motivations that we have identified in earlier work: Categorizers vs. Describers. To that end, we systematically define and evaluate a number of measures designed to discriminate between describers, i.e. users who use tags for describing resources as opposed to categorizers, i.e. users who use tags for categorizing resources. Subsequently, we present empirical findings from qualitative and quantitative evaluations of the measures on real world tagging behavior. In addition, we conducted a recommender evaluation in which we study the effectiveness of each of the presented measures and found the measure based on the tag content to be the most accurate in predicting the user behavior closely followed by a content independent measure. The overall contribution of this paper is the presentation of empirical evidence that tagging motivation can be approximated with simple statistical measures. Our research is relevant for (a) designers of tagging systems aiming to better understand the motivations of their users and (b) researchers interested in studying the effects of users' tagging motivation on the properties of resulting tags and emergent structures in social tagging systems ER - TY - CONF AU - Körner, Christian AU - Benz, Dominik AU - Hotho, Andreas AU - Strohmaier, Markus AU - Stumme, Gerd AU - C. AU - Benz AU - D. AU - Hotho AU - A. AU - Strohmaier AU - M. AU - Stumme AU - G. A2 - T1 - Social Bookmarking Systems: Verbosity Improves Semantics T2 - Proceedings of INSNA Sunbelt XXX PB - CY - Riva del Garda Fierecongressi, Trento, Italy PY - 2010/ M2 - VL - IS - SP - EP - UR - M3 - KW - 2010 KW - myown L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Körner, Christian AU - Benz, Dominik AU - Hotho, Andreas AU - Strohmaier, Markus AU - Stumme, Gerd A2 - T1 - Social Bookmarking Systems: Verbosity Improves Semantics T2 - Proceedings of INSNA Sunbelt XXX PB - CY - Riva del Garda Fierecongressi, Trento, Italy PY - 2010/ M2 - VL - IS - SP - EP - UR - M3 - KW - 2010 KW - myown L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Körner, Christian AU - Benz, Dominik AU - Strohmaier, Markus AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity T2 - Proceedings of the 19th International World Wide Web Conference (WWW 2010) PB - ACM CY - Raleigh, NC, USA PY - 2010/04 M2 - VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/pub/pdf/koerner2010stop.pdf M3 - KW - ol_web2.0 KW - itegpub KW - www2010 KW - ibm-kde-tagging KW - collaborative_verbosity KW - 2010 KW - myown KW - emergentsemantics_factors KW - www L1 - SN - N1 - N1 - AB - Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of a real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise??, and (iii) in learning ontologies. ER - TY - CONF AU - Körner, Christian AU - Benz, Dominik AU - Strohmaier, Markus AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity T2 - Proceedings of the 19th International World Wide Web Conference (WWW 2010) PB - ACM CY - Raleigh, NC, USA PY - 2010/04 M2 - VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/pub/pdf/koerner2010stop.pdf M3 - KW - ol_web2.0 KW - itegpub KW - www2010 KW - ibm-kde-tagging KW - collaborative_verbosity KW - 2010 KW - myown KW - emergentsemantics_factors KW - www L1 - SN - N1 - N1 - AB - Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of a real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise�?, and (iii) in learning ontologies. ER - TY - CONF AU - Körner, Christian AU - Benz, Dominik AU - Strohmaier, Markus AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity T2 - Proceedings of the 19th International World Wide Web Conference (WWW 2010) PB - ACM CY - Raleigh, NC, USA PY - 2010/04 M2 - VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/benz/papers/2010/koerner2010thinking.pdf M3 - KW - tagging KW - semantic KW - l3s KW - myown KW - delicious KW - bibsonomy KW - thinking KW - web KW - emerge KW - itegpub KW - social KW - start KW - semantics L1 - SN - N1 - N1 - AB - Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of a real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise”, and (iii) in learning ontologies. ER - TY - CONF AU - Körner, Christian AU - Benz, Dominik AU - Strohmaier, Markus AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity T2 - Proceedings of the 19th International World Wide Web Conference (WWW 2010) PB - ACM CY - Raleigh, NC, USA PY - 2010/04 M2 - VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/benz/papers/2010/koerner2010thinking.pdf M3 - KW - tagging KW - taggingsurvey KW - www2010 KW - collaborative KW - 2010 KW - myown KW - www L1 - SN - N1 - N1 - AB - Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of a real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise”, and (iii) in learning ontologies. ER -