Networks of Names: Visual Exploration and Semi-Automatic Tagging of Social Networks from Newspaper Articles

Understanding relationships between people and organizations by reading newspaper articles is difficult to manage for humans due to the large amount of data. To address this problem, we present and evaluate a new visual analytics system, which offers interactive exploration and tagging of social net...

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Published in:	Computer graphics forum Vol. 33; no. 3; pp. 211 - 220
Main Authors:	Kochtchi, A., Landesberger, T. von, Biemann, C.
Format:	Journal Article
Language:	English
Published:	Oxford Blackwell Publishing Ltd 01.06.2014
Subjects:	(D.2.2, H.1.2, I.3.6)—Interaction styles (e. g., commands, menus, forms, direct manipulation) Analysis Categories and Subject Descriptors (according to ACM CCS) Classifiers commands D.2.2 direct manipulation Exploration forms H.1.2 H.5.2 [Information Interfaces and Presentation]: User Interfaces I.3.6 [Computer Graphics]: Methodology and Techniques-Graphics data structures and data types I.3.6)-Interaction styles (e. g Information science Interactive Marking menus Networks Social networks Studies Visual Visualization Web analytics
ISSN:	0167-7055, 1467-8659
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Abstract	Understanding relationships between people and organizations by reading newspaper articles is difficult to manage for humans due to the large amount of data. To address this problem, we present and evaluate a new visual analytics system, which offers interactive exploration and tagging of social networks extracted from newspapers. For the visual exploration of the network, we extract “interesting” neighbourhoods of nodes, using a new degree of interest (DOI) measure based on edges instead of nodes. It improves the seminal definition of DOI, which we find to produce the same “globally interesting” neighbourhoods in our use case, regardless of the query. Our approach allows answering different user queries appropriately, avoiding uniform search results. We propose a user‐driven pattern‐based classifier for discovery and tagging of non‐taxonomic semantic relations. Our approach does not require any a‐priori user knowledge, such as expertise in syntax or pattern creation. An evaluation shows that our classifier is capable of identifying known lexico‐syntactic patterns as well as various domain‐specific patters. Our classifier yields good results already with a small amount of training, and continuously improves through user feedback. We conduct a user study to evaluate whether our visual interactive system has an impact on how users tag relationships, as compared to traditional text‐based interfaces. Study results suggest that users of the visual system tend to tag more concisely, avoiding too or overly specific relationship labels.
AbstractList	Understanding relationships between people and organizations by reading newspaper articles is difficult to manage for humans due to the large amount of data. To address this problem, we present and evaluate a new visual analytics system, which offers interactive exploration and tagging of social networks extracted from newspapers. For the visual exploration of the network, we extract "interesting" neighbourhoods of nodes, using a new degree of interest (DOI) measure based on edges instead of nodes. It improves the seminal definition of DOI, which we find to produce the same "globally interesting" neighbourhoods in our use case, regardless of the query. Our approach allows answering different user queries appropriately, avoiding uniform search results. We propose a user-driven pattern-based classifier for discovery and tagging of non-taxonomic semantic relations. Our approach does not require any a-priori user knowledge, such as expertise in syntax or pattern creation. An evaluation shows that our classifier is capable of identifying known lexico-syntactic patterns as well as various domain-specific patters. Our classifier yields good results already with a small amount of training, and continuously improves through user feedback. We conduct a user study to evaluate whether our visual interactive system has an impact on how users tag relationships, as compared to traditional text-based interfaces. Study results suggest that users of the visual system tend to tag more concisely, avoiding too abstract or overly specific relationship labels. Understanding relationships between people and organizations by reading newspaper articles is difficult to manage for humans due to the large amount of data. To address this problem, we present and evaluate a new visual analytics system, which offers interactive exploration and tagging of social networks extracted from newspapers. For the visual exploration of the network, we extract “interesting” neighbourhoods of nodes, using a new degree of interest (DOI) measure based on edges instead of nodes. It improves the seminal definition of DOI, which we find to produce the same “globally interesting” neighbourhoods in our use case, regardless of the query. Our approach allows answering different user queries appropriately, avoiding uniform search results. We propose a user‐driven pattern‐based classifier for discovery and tagging of non‐taxonomic semantic relations. Our approach does not require any a‐priori user knowledge, such as expertise in syntax or pattern creation. An evaluation shows that our classifier is capable of identifying known lexico‐syntactic patterns as well as various domain‐specific patters. Our classifier yields good results already with a small amount of training, and continuously improves through user feedback. We conduct a user study to evaluate whether our visual interactive system has an impact on how users tag relationships, as compared to traditional text‐based interfaces. Study results suggest that users of the visual system tend to tag more concisely, avoiding too or overly specific relationship labels. Understanding relationships between people and organizations by reading newspaper articles is difficult to manage for humans due to the large amount of data. To address this problem, we present and evaluate a new visual analytics system, which offers interactive exploration and tagging of social networks extracted from newspapers. For the visual exploration of the network, we extract “interesting” neighbourhoods of nodes, using a new degree of interest (DOI) measure based on edges instead of nodes. It improves the seminal definition of DOI, which we find to produce the same “globally interesting” neighbourhoods in our use case, regardless of the query. Our approach allows answering different user queries appropriately, avoiding uniform search results. We propose a user‐driven pattern‐based classifier for discovery and tagging of non‐taxonomic semantic relations. Our approach does not require any a‐priori user knowledge, such as expertise in syntax or pattern creation. An evaluation shows that our classifier is capable of identifying known lexico‐syntactic patterns as well as various domain‐specific patters. Our classifier yields good results already with a small amount of training, and continuously improves through user feedback. We conduct a user study to evaluate whether our visual interactive system has an impact on how users tag relationships, as compared to traditional text‐based interfaces. Study results suggest that users of the visual system tend to tag more concisely, avoiding too abstract or overly specific relationship labels. Understanding relationships between people and organizations by reading newspaper articles is difficult to manage for humans due to the large amount of data. To address this problem, we present and evaluate a new visual analytics system, which offers interactive exploration and tagging of social networks extracted from newspapers. For the visual exploration of the network, we extract "interesting" neighbourhoods of nodes, using a new degree of interest (DOI) measure based on edges instead of nodes. It improves the seminal definition of DOI, which we find to produce the same "globally interesting" neighbourhoods in our use case, regardless of the query. Our approach allows answering different user queries appropriately, avoiding uniform search results. We propose a user-driven pattern-based classifier for discovery and tagging of non-taxonomic semantic relations. Our approach does not require any a-priori user knowledge, such as expertise in syntax or pattern creation. An evaluation shows that our classifier is capable of identifying known lexico-syntactic patterns as well as various domain-specific patters. Our classifier yields good results already with a small amount of training, and continuously improves through user feedback. We conduct a user study to evaluate whether our visual interactive system has an impact on how users tag relationships, as compared to traditional text-based interfaces. Study results suggest that users of the visual system tend to tag more concisely, avoiding too abstract or overly specific relationship labels. [PUBLICATION ABSTRACT]
Author	Kochtchi, A. Biemann, C. Landesberger, T. von
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References	Manning C.D., Raghavan P., Schütze H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge, 2008. 6, 7 Sánchez D., Moreno A.: Learning Non-taxonomic Relationships from Web Documents for Domain Ontology Construction. Data & Knowledge Engineering 64, 3 (2008), 600-623. 2 Ohshima H., Tanaka K.: High-speed Detection of Ontological Knowledge and Bi-directional Lexico-Syntactic Patterns from the Web. JSW 5, 2 (2010), 195-205. 9 Clauset A., Shalizi C.R., Newman M.E.: Power-law Distributions in Empirical Data. SIAM Review 51, 4 (2009), 661-703. 4 van Dongen S. M.: Graph Clustering by Flow Simulation. PhD thesis, University of Utrecht, 2000. 6 Smith R.: Mark Lombardi, 48, an Artist Who Was Inspired by Scandals. The New York Times, 2000. http://www.nytimes.com/2000/03/25/arts/mark-lombardi-48-an-artist-who-was-inspired-by-scandals.html, accessed on September 23rd 2013. 1 Nastase V., Nakov P., Séaghdha D. Ó., Szpakowicz S.: Semantic relations between nominals. Synthesis Lectures on Human Language Technologies 6, 1 (2013), 1-119. 2 van Ham F., Wattenberg M., Viégas F. B.: Mapping Text with Phrase Nets. IEEE Transactions on Visualization and Computer Graphics 15, 6 (2009), 1169-1176. 2 Malpani N., Chen J.: A Note on Practical Construction of Maximum Bandwidth Paths. Information Processing Letters 83, 3 (2002), 175-180. 6 Biemann C.: Ontology Learning from Text: A Survey of Methods. LDV Forum 20, 2 (2005), 75-93. 2 Sarawagi S.: Information Extraction. Foundations and Trends in Databases 1, 3 (2008), 261-377. 2, 3, 7, 9 Grobelnik M., Mladenić D.: Visualization of News Articles. Informatica 28, 4 (2004). 2 Punnen A.P.: A Linear Time Algorithm for the Maximum Capacity Path Problem. European Journal of Operational Research 53, 3 (1991), 402-404. 6 van Ham F., Perer A.: "Search, Show Context, Expand on Demand": Supporting Large Graph Exploration with Degree-of-Interest. IEEE Transactions on Visualization and Computer Graphics 15, 6 (2009), 953-960. 2, 5, 9 2009; 51 2011 2000 2010 2002; 83 2004; 28 1991; 53 2009 1986 2008 2007 2005; 20 2006 2005 2004 1992 2013 2002 2008; 64 2008; 1 2010; 5 2013; 6 2009; 15 e_1_2_7_5_2 van Dongen S. M. (e_1_2_7_30_2) 2000 e_1_2_7_3_2 e_1_2_7_2_2 e_1_2_7_9_2 e_1_2_7_8_2 Biemann C. (e_1_2_7_4_2) 2005; 20 e_1_2_7_7_2 e_1_2_7_6_2 e_1_2_7_19_2 e_1_2_7_17_2 e_1_2_7_16_2 e_1_2_7_15_2 Smith R. (e_1_2_7_29_2) 2000 e_1_2_7_13_2 e_1_2_7_12_2 e_1_2_7_11_2 e_1_2_7_10_2 e_1_2_7_26_2 Grobelnik M. (e_1_2_7_14_2) 2004; 28 e_1_2_7_28_2 Kavalec M. (e_1_2_7_18_2) 2005 e_1_2_7_25_2 e_1_2_7_24_2 e_1_2_7_23_2 e_1_2_7_31_2 e_1_2_7_22_2 e_1_2_7_32_2 e_1_2_7_21_2 e_1_2_7_20_2 Snow R. (e_1_2_7_27_2) 2005
References_xml	– reference: Biemann C.: Ontology Learning from Text: A Survey of Methods. LDV Forum 20, 2 (2005), 75-93. 2 – reference: Malpani N., Chen J.: A Note on Practical Construction of Maximum Bandwidth Paths. Information Processing Letters 83, 3 (2002), 175-180. 6 – reference: Nastase V., Nakov P., Séaghdha D. Ó., Szpakowicz S.: Semantic relations between nominals. Synthesis Lectures on Human Language Technologies 6, 1 (2013), 1-119. 2 – reference: Punnen A.P.: A Linear Time Algorithm for the Maximum Capacity Path Problem. European Journal of Operational Research 53, 3 (1991), 402-404. 6 – reference: Grobelnik M., Mladenić D.: Visualization of News Articles. Informatica 28, 4 (2004). 2 – reference: Clauset A., Shalizi C.R., Newman M.E.: Power-law Distributions in Empirical Data. SIAM Review 51, 4 (2009), 661-703. 4 – reference: Smith R.: Mark Lombardi, 48, an Artist Who Was Inspired by Scandals. The New York Times, 2000. http://www.nytimes.com/2000/03/25/arts/mark-lombardi-48-an-artist-who-was-inspired-by-scandals.html, accessed on September 23rd 2013. 1 – reference: van Ham F., Perer A.: "Search, Show Context, Expand on Demand": Supporting Large Graph Exploration with Degree-of-Interest. IEEE Transactions on Visualization and Computer Graphics 15, 6 (2009), 953-960. 2, 5, 9 – reference: Sarawagi S.: Information Extraction. Foundations and Trends in Databases 1, 3 (2008), 261-377. 2, 3, 7, 9 – reference: Manning C.D., Raghavan P., Schütze H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge, 2008. 6, 7 – reference: van Ham F., Wattenberg M., Viégas F. B.: Mapping Text with Phrase Nets. IEEE Transactions on Visualization and Computer Graphics 15, 6 (2009), 1169-1176. 2 – reference: Sánchez D., Moreno A.: Learning Non-taxonomic Relationships from Web Documents for Domain Ontology Construction. Data & Knowledge Engineering 64, 3 (2008), 600-623. 2 – reference: van Dongen S. M.: Graph Clustering by Flow Simulation. PhD thesis, University of Utrecht, 2000. 6 – reference: Ohshima H., Tanaka K.: High-speed Detection of Ontological Knowledge and Bi-directional Lexico-Syntactic Patterns from the Web. 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Title	Networks of Names: Visual Exploration and Semi-Automatic Tagging of Social Networks from Newspaper Articles
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