00628nas a2200181   4500008004100000245008600041210006900127260000900196300001200205490000700217653001500224100001800239700001900257700001900276700001800295700001700313856011600330       2014                        eng d00aThe information content of mandatory risk factor disclosures in corporate filings0 ainformation content of mandatory risk factor disclosures in corp  c2014  a396-4550 v1910aAccounting1 aSteele, Logan1 aCampbell, John1 aChen, Hsinchun1 aDhaliwal, Dan1 aLu, Hsin-min  u/biblio/information-content-mandatory-risk-factor-disclosures-corporate-filings00530nas a2200157   4500008004100000245006600041210006600107260000900173300001200182490000700194653000800201100001500209700002100224700001900245856010800264       2011                        eng d00aIdentity matching using personal and social identity features0 aIdentity matching using personal and social identity features  c2011  a101-1130 v1310aBIS1 aLi, Jiexun1 aWang, Alan, Gang1 aChen, Hsinchun  u/biblio/identity-matching-using-personal-and-social-identity-features-002250nas a2200193   4500008004100000245009000041210006900131260000900200300001200209490000700221520168100228653001501909653000801924100002001932700001701952700002001969700001901989856004802008       2009                        eng d00aTopological Analysis of Criminal Activity Networks: Enhancing Transportation Security0 aTopological Analysis of Criminal Activity Networks Enhancing Tra  c2009  a83 - 910 v103 aThe security of border and transportation systems is a critical component of the national strategy for homeland security. The security concerns at the border are not independent of law enforcement in border-area jurisdictions because the information known by local law enforcement agencies may provide valuable leads that are useful for securing the border and transportation infrastructure. The combined analysis of law enforcement information and data generated by vehicle license plate readers at international borders can be used to identify suspicious vehicles and people at ports of entry. This not only generates better quality leads for border protection agents but may also serve to reduce wait times for commerce, vehicles, and people as they cross the border. This paper explores the use of criminal activity networks (CANs) to analyze information from law enforcement and other sources to provide value for transportation and border security. We analyze the topological characteristics of CAN of individuals and vehicles in a multiple jurisdiction scenario. The advantages of exploring the relationships of individuals and vehicles are shown. We find that large narcotic networks are small world with short average path lengths ranging from 4.5 to 8.5 and have scale-free degree distributions with power law exponents of 0.85–1.3. In addition, we find that utilizing information from multiple jurisdictions provides higher quality leads by reducing the average shortest-path lengths. The inclusion of vehicular relationships and border-crossing information generates more investigative leads that can aid in securing the border and transportation infrastructure.10aAccounting10aBIS1 aKaza, Siddharth1 aXu, Jennifer1 aMarshall, Byron1 aChen, Hsinchun  uhttp://dx.doi.org/10.1109/TITS.2008.201169500461nas a2200133   4500008004100000245005300041210005200094260002500146653000800171100001500179700002100194700001900215856009300234       2008                        eng d00aPRM-based identity matching using social context0 aPRMbased identity matching using social context  aTaipei, Taiwanc200810aBIS1 aLi, Jiexun1 aWang, Gang, Alan1 aChen, Hsinchun  u/biblio/prm-based-identity-matching-using-social-context02064nas a2200181   4500008004100000245008400041210006900125260000900194300001400203490000700217520147700224653001501701653000801716100002001724700001901744700002001763856009901783       2008                        eng d00aUsing Importance Flooding to Identify Interesting Networks of Criminal Activity0 aUsing Importance Flooding to Identify Interesting Networks of Cr  c2008  a2099-21140 v593 aCross-jurisdictional law enforcement data sharing and analysis is of vital importance because law breakers regularly operate in multiple jurisdictions. Agencies continue to invest massive resources in various sharing initiatives despite several high-profile failures. Key difficulties include: privacy concerns, administrative issues, differences in data representation, and a need for better analysis tools. This work presents a methodology for sharing and analyzing investigation-relevant data and is potentially useful across large cross-jurisdictional data sets. The approach promises to allow crime analysts to use their time more effectively when creating link charts and performing similar analysis tasks. Many potential privacy and security pitfalls are avoided by reducing shared data requirements to labeled relationships between entities. Our importance flooding algorithm helps extract interesting networks of relationships from existing law enforcement records using user-controlled investigation heuristics, spreading activation, and path-based interestingness rules. In our experiments, several variations of the importance flooding approach outperformed relationship-weight-only methods in matching expert-selected associations. We find that accuracy in not substantially affected by reasonable variations in algorithm parameters and demonstrate that user feedback and additional, case-specific information can be usefully added to the computational model.10aAccounting10aBIS1 aMarshall, Byron1 aChen, Hsinchun1 aKaza, Siddharth  uhttp://people.oregonstate.edu/~marshaby/Papers/Marshall_JASIST_ImportanceFlooding_PrePrint.pdf00603nas a2200145   4500008004100000245010700041210006900148260004600217653000800263100001200271700001900283700001500302700001500317856012500332       2007                        eng d00aAuto patent classification using citation network information: An experimental study in nanotechnology0 aAuto patent classification using citation network information An  aVancouver, British Columbia, Canadac200710aBIS1 aLi, Xin1 aChen, Hsinchun1 aZhang, Zhu1 aLi, Jiexun  u/biblio/auto-patent-classification-using-citation-network-information-experimental-study00565nas a2200145   4500008004100000245009100041210006900132260002700201653000800228100001200236700001500248700001900263700001500282856012200297       2007                        eng d00aGraph kernel-based learning for gene function prediction from gene interaction network0 aGraph kernelbased learning for gene function prediction from gen  aFremont, CA, USAc200710aBIS1 aLi, Xin1 aZhang, Zhu1 aChen, Hsinchun1 aLi, Jiexun  u/biblio/graph-kernel-based-learning-gene-function-prediction-gene-interaction-network00679nas a2200181   4500008004100000245012900041210006900170260000900239300001400248490000700262653000800269100001500277700001500292700001200307700002200319700001900341856013700360       2007                        eng d00aLarge-scale regulatory network analysis from microarray data: Modified Bayesian network learning and association rule mining0 aLargescale regulatory network analysis from microarray data Modi  c2007  a1207-12250 v4310aBIS1 aHuang, Zan1 aLi, Jiexun1 aSu, Hua1 aWatts, George, S.1 aChen, Hsinchun  u/biblio/large-scale-regulatory-network-analysis-microarray-data-modified-bayesian-network-learning-000594nas a2200169   4500008004100000245008400041210006900125260000900194300001200203490000700215653000800222100001500230700001200245700001900257700002600276856012200302       2007                        eng d00aOptimal search-based gene subset selection for gene array cancer classification0 aOptimal searchbased gene subset selection for gene array cancer   c2007  a398-4050 v1110aBIS1 aLi, Jiexun1 aSu, Hua1 aChen, Hsinchun1 aFutscher, Bernard, W.  u/biblio/optimal-search-based-gene-subset-selection-gene-array-cancer-classification-002033nas a2200205   4500008004100000245011700041210006900158260000900227300001200236490000700248520135500255653001501610653000801625100002501633700001201658700002001670700001901690700001901709856009901728       2007                        eng d00aUser-Centered Evaluation of Arizona BioPathway: An Information Extraction, Integration, and Visualization System0 aUserCentered Evaluation of Arizona BioPathway An Information Ext  c2007  a527-5360 v113 aExplosive growth in biomedical research has made automated information extraction, knowledge integration, and visualization increasingly important and critically needed. The Arizona BioPathway (ABP) system extracts and displays biological regulatory pathway information from the abstracts of journal articles. This study uses relations extracted from more than 200 PubMed abstracts presented in a tabular and graphical user interface with built-in search and aggregation functionality. This article presents a task-centered assessment of the usefulness and usability of the ABP system focusing on its relation aggregation and visualization functionalities. Results suggest that our graph-based visualization is more efficient in supporting pathway analysis tasks and is perceived as more useful and easier to use as compared to a text-based literature viewing method. Relation aggregation significantly contributes to knowledge acquisition efficiency. Together, the graphic and tabular views in the ABP Visualizer provide a flexible and effective interface for pathway relation browsing and analysis. Our study contributes to pathway-related research and biological information extraction by assessing the value of a multi-view, relation-based interface which supports user-controlled exploration of pathway information across multiple granularities.10aAccounting10aBIS1 aQuiñones, Karin, D.1 aSu, Hua1 aMarshall, Byron1 aEggers, Shauna1 aChen, Hsinchun  uhttp://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=4300830&arnumber=4300844&count=17&index=501468nas a2200205   4500008004100000245006900041210006900110260000900179300001300188490000700201520086100208653001501069653000801084100002001092700001201112700002101124700001901145700001901164856007901183       2006                        eng d00aAggregating Automatically Extracted Regulatory Pathway Relations0 aAggregating Automatically Extracted Regulatory Pathway Relations  c2006  a100- 1080 v103 aAutomatic tools to extract information from biomedical texts are needed to help researchers leverage the vast and increasing body of biomedical literature. While several biomedical relation extraction systems have been created and tested, little work has been done to meaningfully organize the extracted relations. Organizational processes should consolidate multiple references to the same objects over various levels of granularity, connect those references to other resources, and capture contextual information. We propose a feature decomposition approach to relation aggregation to support a five-level aggregation framework. Our BioAggregate tagger uses this approach to identify key features in extracted relation name strings. We show encouraging feature assignment accuracy and report substantial consolidation in a network of extracted relations.10aAccounting10aBIS1 aMarshall, Byron1 aSu, Hua1 aMcDonald, Daniel1 aEggers, Shauna1 aChen, Hsinchun  uhttp://people.oregonstate.edu/~marshaby/Papers/Marshall_IEEE_TITB_2005.pdf00575nas a2200145   4500008004100000245009800041210006900139260002700208653000800235100001500243700001200258700001200270700001900282856012800301       2006                        eng d00aA Bayesian framework of integrating gene functional relations from heterogeneous data sources0 aBayesian framework of integrating gene functional relations from  aPhoenix, AZ, USAc200610aBIS1 aLi, Jiexun1 aLi, Xin1 aSu, Hua1 aChen, Hsinchun  u/biblio/bayesian-framework-integrating-gene-functional-relations-heterogeneous-data-sources00649nas a2200181   4500008004100000245011900041210006900160260000900229300001200238490000700250653000800257100001600265700001500281700001900296700001500315700001200330856012500342       2006                        eng d00aA framework of authorship identification for online messages: Writing style features and classification techniques0 aframework of authorship identification for online messages Writi  c2006  a378-3930 v5710aBIS1 aZheng, Rong1 aLi, Jiexun1 aChen, Hsinchun1 aHuang, Zan1 aQin, Yi  u/biblio/framework-authorship-identification-online-messages-writing-style-features-and-000673nas a2200181   4500008004100000245012800041210006900169260000900238300001400247490000700261653000800268100001500276700001200291700001200303700001900315700002500334856013200359       2006                        eng d00aA framework of integrating gene functional relations from heterogeneous data sources: An experiment on Arabidopsis thaliana0 aframework of integrating gene functional relations from heteroge  c2006  a2037-20430 v2210aBIS1 aLi, Jiexun1 aLi, Xin1 aSu, Hua1 aChen, Hsinchun1 aGalbraith, David, W.  u/biblio/framework-integrating-gene-functional-relations-heterogeneous-data-sources-experiment-000422nas a2200157   4500008004100000245003500041210003500076260000900111300001000120490000700130653000800137100001500145700001600160700001900176856006900195       2006                        eng d00aFrom fingerprint to writeprint0 aFrom fingerprint to writeprint  c2006  a76-820 v4910aBIS1 aLi, Jiexun1 aZheng, Rong1 aChen, Hsinchun  u/biblio/fingerprint-writeprint-001362nas a2200181   4500008004100000245008600041210006900127260000900196300001400205490000700219520077400226653001501000653000801015100002001023700001901043700002401062856009401086       2006                        eng d00aMatching Knowledge Elements in Concept Maps Using a Similarity Flooding Algorithm0 aMatching Knowledge Elements in Concept Maps Using a Similarity F  c2006  a1290-13060 v423 aConcept mapping systems used in education and knowledge management emphasize flexibility of representation to enhance learning and facilitate knowledge capture. Collections of concept maps exhibit terminology variance, informality, and organizational variation. These factors make it difficult to match elements between maps in comparison, retrieval, and merging processes. In this work, we add an element anchoring mechanism to a similarity flooding (SF) algorithm to match nodes and substructures between pairs of simulated maps and student-drawn concept maps. Experimental results show significant improvement over simple string matching with combined recall accuracy of 91% for conceptual nodes and concept ¨ link ¨ concept propositions in student-drawn maps.10aAccounting10aBIS1 aMarshall, Byron1 aChen, Hsinchun1 aMadhusudan, Therani  uhttp://people.oregonstate.edu/~marshaby/Papers/MatchKnowledgeElements_PrePrintVersion.pdf01741nas a2200181   4500008004100000245008600041210006900127260000900196490000600205520119200211653001501403653000801418100002001426700001901446700001401465700002001479856006001499       2006                        eng d00aMoving Digital Libraries into the Student Learning Space: the GetSmart Experience0 aMoving Digital Libraries into the Student Learning Space the Get  c20060 v63 aThe GetSmart system was built to support theoretically sound learning processes in a digital library environment by integrating course management, digital library, and concept mapping components to support a constructivist, six-step, information search process. In the fall of 2002 more than 100 students created 1400 concept maps as part of selected computing classes offered at the University of Arizona and Virginia Tech. Those students conducted searches, obtained course information, created concept maps, collaborated in acquiring knowledge, and presented their knowledge representations. This article connects the design elements of the GetSmart system to targeted concept-map-based learning processes, describes our system and research testbed, and analyzes our system usage logs. Results suggest that students did in fact use the tools in an integrated fashion, combining knowledge representation and search activities. After concept mapping was included in the curriculum, we observed improvement in students' online quiz scores. Further, we observed that students in groups collaboratively constructed concept maps with multiple group members viewing and updating map details.10aAccounting10aBIS1 aMarshall, Byron1 aChen, Hsinchun1 aShen, Rao1 aFox, Edward, A.  uhttp://portal.acm.org/citation.cfm?doid=1217862.121786400566nas a2200145   4500008004100000245008400041210006900125260002700194653000800221100001500229700001200244700001900256700002500275856012000300       2006                        eng d00aOptimal search-based gene subset selection for microarray cancer classification0 aOptimal searchbased gene subset selection for microarray cancer   aPhoenix, AZ, USAc200610aBIS1 aLi, Jiexun1 aSu, Hua1 aChen, Hsinchun1 aFutscher, Bernard, W  u/biblio/optimal-search-based-gene-subset-selection-microarray-cancer-classification00487nas a2200133   4500008004100000245008400041210006900125260002400194653001500218653000800233100002000241700001900261856007300280       2006                        eng d00aUsing Importance Flooding to Identify Interesting Networks of Criminal Activity0 aUsing Importance Flooding to Identify Interesting Networks of Cr  aSan Diego, CAc200610aAccounting10aBIS1 aMarshall, Byron1 aChen, Hsinchun  uhttp://people.oregonstate.edu/~marshaby/Papers/Marshall_ISI_2006.pdf01720nas a2200169   4500008004100000245010700041210006900148260000900217520116000226653001501386653000801401100002001409700001201429700001801441700001901459856007201478       2005                        eng d00aLinking Ontological Resources Using Aggregatable Substance Identifiers to Organize Extracted Relations0 aLinking Ontological Resources Using Aggregatable Substance Ident  c20053 aSystems that extract biological regulatory pathway relations from free-text sources are<br>intended to help researchers leverage vast and growing collections of research literature.<br>Several systems to extract such relations have been developed but little work has focused on<br>how those relations can be usefully organized (aggregated) to support visualization systems or<br>analysis algorithms. Ontological resources that enumerate name strings for different types of<br>biomedical objects should play a key role in the organization process. In this paper we<br>delineate five potentially useful levels of relational granularity and propose the use of<br>aggregatable substance identifiers to help reduce lexical ambiguity. An aggregatable<br>substance identifier applies to a gene and its products. We merged 4 extensive lexicons and<br>compared the extracted strings to the text of five million MEDLINE abstracts. We report on<br>the ambiguity within and between name strings and common English words. Our results show<br>an 89% reduction in ambiguity for the extracted human substance name strings when using an<br>aggregatable substance approach.10aAccounting10aBIS1 aMarshall, Byron1 aSu, Hua1 aMcDonald, Dan1 aChen, Hsinchun  uhttp://people.oregonstate.edu/~marshaby/Papers/marshall_PSB2005.pdf00468nas a2200133   4500008004100000245006100041210006100102260002000163653000800183100001500191700001200206700001900218856009700237       2005                        eng d00aOptimal search based gene selection for cancer prognosis0 aOptimal search based gene selection for cancer prognosis  aOmaha, NEc200510aBIS1 aLi, Jiexun1 aSu, Hua1 aChen, Hsinchun  u/biblio/optimal-search-based-gene-selection-cancer-prognosis01188nas a2200181   4500008004100000245005600041210005600097260000900153520064400162653001500806653000800821100002000829700002100849700001200870700001900882700001900901856008600920       2005                        eng d00aVisualizing Aggregated Biological Pathway Relations0 aVisualizing Aggregated Biological Pathway Relations  c20053 aThe Genescene development team has constructed an aggregation interface for automatically-extracted biomedical pathway<br>relations that is intended to help researchers identify and process relevant information from the vast digital library of abstracts found in the National Library of Medicine’s PubMed collection.<br>Users view extracted relations at various levels of relational granularity in an interactive and visual node-link interface. Anecdotal feedback reported here suggests that this multigranular visual paradigm aligns well with various research tasks,<br>helping users find relevant articles and discover new information.10aAccounting10aBIS1 aMarshall, Byron1 aQuiñones, Karin1 aSu, Hua1 aEggers, Shauna1 aChen, Hsinchun  uhttp://people.oregonstate.edu/~marshaby/Papers/Marshall_JCDL_2005_Aggregation.pdf02392nas a2200193   4500008004100000245007400041210006900115260000900184300001200193490000700205520180700212653001502019653000802034100002002042700001802062700001902080700001902099856008002118       2004                        eng d00aEBizPort:  Collecting and Analyzing Business Intelligence Information0 aEBizPort Collecting and Analyzing Business Intelligence Informat  c2004  a873-8910 v553 aIn this article, Marshall, McDonald, Chen, and Chung take a different approach to supporting search services to large and heterogeneous document collections. They propose development of a domain-specific collection by crawling the content of a small set of highly reputable sites, maintaining a local index of the content, and providing browsing and searching services on the specialized content. This resource, known as a vertical portal, has the potential of overcoming several problems associated with bias, update delay, reputation, and integration of scattered information. The article discusses the design of a vertical portal system's architecture called EbizPort, rationale behind its major components, and algorithms and techniques for building collections and search functions. Collection (or more broadly content) has an obvious relationship to the nature of the search interface, as it can impact the type of search functions that can be offered. Powerful search interface functions were built for EbizPort by exploiting the underlying content representation and a relatively narrow and well-defined domain focus. Particularly noteworthy are the innovative browsing functions, which include a summarizer, a categorizer, a visualizer, and a navigation side-bar. The article ends with a discussion of an evaluation study, which compared the EbizPort system with a baseline system called Brint. Results are presented on effectiveness and efficiency, usability and information quality, and quality of local collection and content retrieved from other sources (an extended search operation called meta-search service was also provided in the system). Overall, the authors find that EbizPort outperforms the baseline system, and it provides a viable way to support access to business information.10aAccounting10aBIS1 aMarshall, Byron1 aMcDonald, Dan1 aChen, Hsinchun1 aChung, Wingyan  uhttp://people.oregonstate.edu/~marshaby/Papers/Marshall_JASIST_EBizPort.pdf01663nas a2200193   4500008004100000245009100041210006900132260000900201300001100210490000700221520107000228653001501298653000801313100001801321700001901339700001201358700002001370856007901390       2004                        eng d00aExtracting Gene Pathway Relations Using a Hybrid Grammar:  The Arizona Relation Parser0 aExtracting Gene Pathway Relations Using a Hybrid Grammar The Ari  c2004  a3370-80 v203 aMotivation: Text-mining research in the biomedical domain has been motivated by the rapid growth of new research findings. Improving the accessibility of findings has potential to speed hypothesis generation.Results: We present the Arizona Relation Parser that differs from other parsers in its use of a broad coverage syntax-semantic hybrid grammar. While syntax grammars have generally been tested over more documents, semantic grammars have outperformed them in precision and recall. We combined access to syntax and semantic information from a single grammar. The parser was trained using 40 PubMed abstracts and then tested using 100 unseen abstracts, half for precision and half for recall. Expert evaluation showed that the parser extracted biologically relevant relations with 89% precision. Recall of expert identified relations with semantic filtering was 35 and 61% before semantic filtering. Such results approach the higher-performing semantic parsers. However, the AZ parser was tested over a greater variety of writing styles and semantic content. 
10aAccounting10aBIS1 aMcDonald, Dan1 aChen, Hsinchun1 aSu, Hua1 aMarshall, Byron  uhttp://people.oregonstate.edu/~marshaby/Papers/MCDONALD_BIOINFORMATICS.pdf00666nas a2200229   4500008004100000245004700041210004600088260002700134653000800161100001700169700001900186700002100205700001400226700001400240700001400254700001500268700001500283700001700298700002100315700001400336856008600350       2003                        eng d00aGenescene: Biomedical text and data mining0 aGenescene Biomedical text and data mining  aHouston, TX, USAc200310aBIS1 aLeroy, Gondy1 aChen, Hsinchun1 aMartinez, Jessie1 aEggers, S1 aFalsey, R1 aKislin, K1 aHuang, Zan1 aLi, Jiexun1 aXu, Jennifer1 aMcDonald, Daniel1 aNg, Gavin  u/biblio/genescene-biomedical-text-and-data-mining01516nas a2200205   4500008004100000245006500041210006300106260000900169520090600178653001501084653000801099100002001107700001701127700001901144700001501163700001401178700001601192700002001208856008201228       2003                        eng d00aKnowledge Management and E-Learning: the GetSmart Experience0 aKnowledge Management and ELearning the GetSmart Experience  c20033 aThe National Science Digital Library (NSDL), launched in December 2002, is emerging as a center of innovation in digital libraries as applied to education. As a part of this extensive project, the GetSmart system was created to apply knowledge management techniques in a learning environment. The design of the system is based on an analysis of learning theory and theinformation search process. Its key notion is the integration of search tools and curriculum support with
concept mapping. More than 100 students at the University of Arizona and Virginia Tech used the system
in the fall of 2002. A database of more than one
thousand student-prepared concept maps has been
collected with more than forty thousand relationships
expressed in semantic, graphical, node-link
representations. Preliminary analysis of the collected
data is revealing interesting knowledge representation
patterns.
10aAccounting10aBIS1 aMarshall, Byron1 aZhang, Yiwen1 aChen, Hsinchun1 aLally, Ann1 aShen, Rao1 aFox, Edward1 aCassel, Lillian  uhttp://people.oregonstate.edu/~marshaby/Papers/Marshall_JCDL2003_GetSmart.pdf