Semantic Integration and Retrieval of Multimedia Metadata
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Semantic Integration and Knowledge Discovery for Environmental Research
Journal of Database Management, 18(1), 43-67, January-March 2007 43 Semantic Integration and Knowledge Discovery for Environmental Research Zhiyuan Chen, University of Maryland, Baltimore County (UMBC), USA Aryya Gangopadhyay, University of Maryland, Baltimore County (UMBC), USA George Karabatis, University of Maryland, Baltimore County (UMBC), USA Michael McGuire, University of Maryland, Baltimore County (UMBC), USA Claire Welty, University of Maryland, Baltimore County (UMBC), USA ABSTRACT Environmental research and knowledge discovery both require extensive use of data stored in various sources and created in different ways for diverse purposes. We describe a new metadata approach to elicit semantic information from environmental data and implement semantics- based techniques to assist users in integrating, navigating, and mining multiple environmental data sources. Our system contains specifications of various environmental data sources and the relationships that are formed among them. User requests are augmented with semantically related data sources and automatically presented as a visual semantic network. In addition, we present a methodology for data navigation and pattern discovery using multi-resolution brows- ing and data mining. The data semantics are captured and utilized in terms of their patterns and trends at multiple levels of resolution. We present the efficacy of our methodology through experimental results. Keywords: environmental research, knowledge discovery and navigation, semantic integra- tion, semantic networks, -
Semantic Integration Across Heterogeneous Databases Finding Data Correspondences Using Agglomerative Hierarchical Clustering and Artificial Neural Networks
DEGREE PROJECT IN COMPUTER SCIENCE AND ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2018 Semantic Integration across Heterogeneous Databases Finding Data Correspondences using Agglomerative Hierarchical Clustering and Artificial Neural Networks MARK HOBRO KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE Semantic Integration across Heterogeneous Databases Finding Data Correspondences using Agglomerative Hierarchical Clustering and Artificial Neural Networks MARK HOBRO Master in Computer Science Date: April 11, 2018 Supervisor: John Folkesson Examiner: Hedvig Kjellström Swedish title: Semantisk integrering mellan heterogena databaser: Hitta datakopplingar med hjälp av hierarkisk klustring och artificiella neuronnät School of Computer Science and Communication iii Abstract The process of data integration is an important part of the database field when it comes to database migrations and the merging of data. The research in the area has grown with the addition of machine learn- ing approaches in the last 20 years. Due to the complexity of the re- search field, no go-to solutions have appeared. Instead, a wide variety of ways of enhancing database migrations have emerged. This thesis examines how well a learning-based solution performs for the seman- tic integration problem in database migrations. Two algorithms are implemented. One that is based on informa- tion retrieval theory, with the goal of yielding a matching result that can be used as a benchmark for measuring the performance of the machine learning algorithm. The machine learning approach is based on grouping data with agglomerative hierarchical clustering and then training a neural network to recognize patterns in the data. This al- lows making predictions about potential data correspondences across two databases. -
Noel Paul Stookey Bio
Noel Paul Stookey Bio Singer/songwriter Noel Paul Stookey has been altering both the musical and ethical landscape of this country and the world for decades—both as the “Paul” of the legendary Peter, Paul and Mary and as an independent musician who passionately belie es in bringing the spiritual into the practice of daily life! "unny, irre erently re erent, thoughtful, compassionately passionate, Stookey’s oice is known all across this land: from the “%edding Song” to “&n 'hese 'imes.” Noel and Betty, his bride of over )* years +but who’s counting,, moved with their three daughters to the coast of maine over forty years ago. since that time he’s done the occasional home town benefit and the here-and-there in-state show but ne er had undertaken a concentrated tour in one season. “& recorded all . of my maine concerts last summer - from ogun/uit to eastport - check '01' out on a map” he says, “and, though not all of the ideo or audio made 2prime time2, i was able to collect *3 music ideos for the 4VD and +amazingly, fit all *3 songs on the 7D to create this 1' 08ME: the maine tour package.” 'he songs in this newest release represent a broad range: '0E 71(&N "959: %1;'< +homage to the rigors of enduring the lengthened winter in maine,, a new ersion of %01'S09:NAM9 +a bittersweet =az6 shaded reminiscence of a middle-aged man in denial, originally recorded on the PP&M ?@AA album, "1M&;&1 49; 78:1<8N +a new song that speaks to the immigration issue in compassionate rather than political terms,, %944&NB S8NG +with the 2original2 lyric and a spoken introduction - DVD only, and '0E ;1DY S1YS S09 48N2' ;&KE E1<< +a commentary on a common misperception of the creati e process,. -
L Dataspaces Make Data Ntegration Obsolete?
DBKDA 2011, January 23-28, 2011 – St. Maarten, The Netherlands Antilles DBKDA 2011 Panel Discussion: Will Dataspaces Make Data Integration Obsolete? Moderator: Fritz Laux, Reutlingen Univ., Germany Panelists: Kazuko Takahashi, Kwansei Gakuin Univ., Japan Lena Strömbäck, Linköping Univ., Sweden Nipun Agarwal, Oracle Corp., USA Christopher Ireland, The Open Univ., UK Fritz Laux, Reutlingen Univ., Germany DBKDA 2011, January 23-28, 2011 – St. Maarten, The Netherlands Antilles The Dataspace Idea Space of Data Management Scalable Functionality and Costs far Web Search Functionality virtual Organization pay-as-you-go, Enterprise Dataspaces Admin. Portal Schema Proximity Federated first, DBMS DBMS scient. Desktop Repository Search DBMS schemaless, near unstructured high Semantic Integration low Time and Cost adopted from [Franklin, Halvey, Maier, 2005] DBKDA 2011, January 23-28, 2011 – St. Maarten, The Netherlands Antilles Dataspaces (DS) [Franklin, Halevy, Maier, 2005] is a new abstraction for Information Management ● DS are [paraphrasing and commenting Franklin, 2009] – Inclusive ● Deal with all the data of interest, in whatever form => but semantics matters ● We need access to the metadata! ● derive schema from instances? ● Discovering new data sources => The Münchhausen bootstrap problem? Theodor Hosemann (1807-1875) DBKDA 2011, January 23-28, 2011 – St. Maarten, The Netherlands Antilles Dataspaces (DS) [Franklin, Halevy, Maier, 2005] is a new abstraction for Information Management ● DS are [paraphrasing and commenting Franklin, 2009] – Co-existence -
Learning to Match Ontologies on the Semantic Web
The VLDB Journal manuscript No. (will be inserted by the editor) Learning to Match Ontologies on the Semantic Web AnHai Doan1, Jayant Madhavan2, Robin Dhamankar1, Pedro Domingos2, Alon Halevy2 1 Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA fanhai,[email protected] 2 Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA fjayant,pedrod,[email protected] Received: date / Revised version: date Abstract On the Semantic Web, data will inevitably come and much of the potential of the Web has so far remained from many different ontologies, and information processing untapped. across ontologies is not possible without knowing the seman- In response, researchers have created the vision of the Se- tic mappings between them. Manually finding such mappings mantic Web [BLHL01], where data has structure and ontolo- is tedious, error-prone, and clearly not possible at the Web gies describe the semantics of the data. When data is marked scale. Hence, the development of tools to assist in the ontol- up using ontologies, softbots can better understand the se- ogy mapping process is crucial to the success of the Seman- mantics and therefore more intelligently locate and integrate tic Web. We describe GLUE, a system that employs machine data for a wide variety of tasks. The following example illus- learning techniques to find such mappings. Given two on- trates the vision of the Semantic Web. tologies, for each concept in one ontology GLUE finds the most similar concept in the other ontology. We give well- founded probabilistic definitions to several practical similar- Example 1 Suppose you want to find out more about some- ity measures, and show that GLUE can work with all of them. -
Semantic Integration in the IFF Gies , Etc
ogies, composing ontologies via fusions, noting dependen- cies between ontologies, declaring the use of other ontolo- 3 Semantic Integration in the IFF gies , etc. The IFF takes a building blocks approach to- wards the development of object-level ontological struc- ture. This is a rather elaborate categorical approach, which Robert E. Kent uses insights and ideas from the theory of distributed logic Ontologos known as information flow (Barwise and Seligman, 1997) [email protected] and the theory of formal concept analysis (Ganter and Wille, 1999). The IFF represents metalogic, and as such operates at the structural level of ontologies. In the IFF, Abstract there is a precise boundary between the metalevel and the object level. The IEEE P1600.1 Standard Upper Ontology (SUO) The modular architecture of the IFF consists of metale- project aims to specify an upper ontology that will provide vels, namespaces and meta-ontologies. There are three me- a structure and a set of general concepts upon which do- talevels: top, upper and lower. This partition, which cor- main ontologies could be constructed. The Information responds to the set-theoretic distinction between small Flow Framework (IFF), which is being developed under (sets), large (classes) and generic collections, is permanent. the auspices of the SUO Working Group, represents the Each metalevel services the level below by providing a structural aspect of the SUO. The IFF is based on category language that is used to declare and axiomatize that level. theory. Semantic integration of object-level ontologies in * The top metalevel services the upper metalevel, the upper the IFF is represented with its fusion construction . -
INTERVIEWS from the 90'S
INTERVIEWS FROM THE 90’s 1 INTERVIEWS FROM THE 90’S p4. THE SONG TALK INTERVIEW, 1991 p27. THE AGE, APRIL 3, 1992 (MELBOURNE, AUSTRALIA) p30. TIMES THEY ARE A CHANGIN'.... DYLAN SPEAKS, APRIL 7, 1994 p33. NEWSWEEK, MARCH 20, 1995 p36. EDNA GUNDERSEN INTERVIEW, USA TODAY, MAY 5, 1995 p40. SUN-SENTINEL TODAY, FORT LAUDERDALE, SEPT. 29, 1995 p45. NEW YORK TIMES, SEPT. 27, 1997 p52. USA TODAY, SEPT. 28, 1997 p57. DER SPIEGEL, OCT. 16, 1997 p66. GUITAR WORLD MAGAZINE, MARCH 1999 Source: http://www.interferenza.com/bcs/interv.htm 2 3 THE SONG TALK INTERVIEW, 1991 Bob Dylan: The Song Talk Interview. By: Paul Zollo Date: 1991 Transcribed from GBS #3 booklet "I've made shoes for everyone, even you, while I still go barefoot" from "I and I" By Bob Dylan Songwriting? What do I know about songwriting? Bob Dylan asked, and then broke into laughter. He was wearing blue jeans and a white tank-top T-shirt, and drinking coffee out of a glass. "It tastes better out of a glass," he said grinning. His blonde acoustic guitar was leaning on a couch near where we sat. Bob Dylan's guitar . His influence is so vast that everything that surrounds takes on enlarged significance: Bob Dylan's moccasins. Bob Dylan's coat . "And the ghost of 'lectricity howls in the bones of her face Where these visions of Johanna have now taken my place. The harmonicas play the skeleton keys and the rain And these visions of Johanna are now all that remain" from "Visions of Johanna" Pete Seeger said, "All songwriters are links in a chain," yet there are few artists in this evolutionary arc whose influence is as profound as that of Bob Dylan. -
Rule-Based Intelligence on the Semantic Web Implications for Military Capabilities
UNCLASSIFIED Rule-Based Intelligence on the Semantic Web Implications for Military Capabilities Dr Paul Smart Senior Research Fellow School of Electronics and Computer Science University of Southampton Southampton SO17 1BJ United Kingdom 26th November 2007 UNCLASSIFIED Report Documentation Page Report Title: Rule-Based Intelligence on the Semantic Web Report Subtitle Implications for Military Capabilities Project Title: N/A Number of Pages: 57 Version: 1.2 Date of Issue: 26/11/2007 Due Date: 22/11/2007 Performance EZ~01~01~17 Number of 95 Indicator: References: Reference Number: DT/Report/RuleIntel Report Availability: APPROVED FOR PUBLIC RELEASE; LIMITED DISTRIBUTION Abstract Availability: APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED Authors: Paul Smart Keywords: semantic web, ontologies, reasoning, decision support, rule languages, military Primary Author Details: Client Details: Dr Paul Smart Senior Research Fellow School of Electronics and Computer Science University of Southampton Southampton, UK SO17 1BJ tel: +44 (0)23 8059 6669 fax: +44 (0)23 8059 2783 email: [email protected] Abstract: Rules are a key element of the Semantic Web vision, promising to provide a foundation for reasoning capabilities that underpin the intelligent manipulation and exploitation of information content. Although ontologies provide the basis for some forms of reasoning, it is unlikely that ontologies, by themselves, will support the range of knowledge-based services that are likely to be required on the Semantic Web. As such, it is important to consider the contribution that rule-based systems can make to the realization of advanced machine intelligence on the Semantic Web. This report aims to review the current state-of-the-art with respect to semantic rule-based technologies. -
Peter Paul and Mary.Pptx
Peter, Paul and Mary Group 3: Chen Chen & Hailey Funk Outline • Biography -- Peter; Paul; Mary; Albert Gross the group • Album timeline • Musical style • Musical Analysis (vocal, instrumentation) • Comparison between Peter, Paul and Mary's cover version and original version • Musical influence on&of Peter, Paul and Mary Biography-Peter • Born May 31st 1938, in New York City • learned guitar and violin early on o Went to high school for "Music and Art" • Got a Bachelor's degree in psychology from Cornell • Met Mary and Paul in Greenwich Village (1960) o Due to manager, Albert Grossman Biography-Paul • Born Dec. 30 1937 in Baltimore, Maryland as Noel "Paul" Stookey • Raised in Michigan • Learned to play guitar at 11 • Graduated from Michigan State University o he was a master of ceremonies o involved in band, Corsairs • Moved to New York in 1959 • Worked in sales and at a club in Greenwich Village o Albert Grossman introduced him to Peter and Mary Biography-Mary • Born November 9 1936 in Louisville, Kentucky • Raised in Greenwich Village • Her and schoolmates sang backup for Pete Seeger's album • Dropped out of high school in 11th grade • In Broadway musical The Next President Albert Grossman • Manager of folk/folk rock: o Bob Dylan o Janis Joplin o +others • Put together Peter, Paul and Mary • looking for tall and blonde women (Mary), a good-looking guy (Peter) and a comedic man (Paul). Biography-Peter, Paul and Mary After auditioning several singers in the New York folk scene, Albert Grossman, the Manager, created Peter, Paul and Mary in 1961, After rehearsing them out of town in Boston and Miami, Grossman booked them into The Bitter End, a coffee house, nightclub and popular folk music venue in New York City's Greenwich Village. -
Semantic Web Technologies and Data Management
Semantic Web Technologies and Data Management Li Ma, Jing Mei, Yue Pan Krishna Kulkarni Achille Fokoue, Anand Ranganathan IBM China Research Laboratory IBM Software Group IBM Watson Research Center Bei Jing 100094, China San Jose, CA 95141-1003, USA New York 10598, USA Introduction The Semantic Web aims to build a common framework that allows data to be shared and reused across applications, enterprises, and community boundaries. It proposes to use RDF as a flexible data model and use ontology to represent data semantics. Currently, relational models and XML tree models are widely used to represent structured and semi-structured data. But they offer limited means to capture the semantics of data. An XML Schema defines a syntax-valid XML document and has no formal semantics, and an ER model can capture data semantics well but it is hard for end-users to use them when the ER model is transformed into a physical database model on which user queries are evaluated. RDFS and OWL ontologies can effectively capture data semantics and enable semantic query and matching, as well as efficient data integration. The following example illustrates the unique value of semantic web technologies for data management. Figure 1. An example of ontology based data management In Figure 1, we have two tables in a relational database. One stores some basic information of several companies, and another one describes shareholding relationship among these companies. Sometimes, users want to issue such a query “find Company EDOX’s all direct and indirect shareholders which are from Europe and are IT company”. Based on the data stored in the database, existing RDBMSes cannot represent and answer the above query. -
Semantic Integration and Analysis of Clinical Data-1024
Semantic integration and analysis of clinical data Hong Sun, Kristof Depraetere, Jos De Roo, Boris De Vloed, Giovanni Mels, Dirk Colaert Advanced Clinical Applications Research Group, Agfa HealthCare, Gent, Belgium {hong.sun, kristof.depraetere, jos.deroo, boris.devloed, giovanni.mels, dirk.colaert }@agfa.com Abstract. There is a growing need to semantically process and integrate clinical data from different sources for Clinical Data Management and Clinical Deci- sion Support in the healthcare IT industry. In the clinical practice domain, the semantic gap between clinical information systems and domain ontologies is quite often difficult to bridge in one step. In this paper, we report our experi- ence in using a two-step formalization approach to formalize clinical data, i.e. from database schemas to local formalisms and from local formalisms to do- main (unifying) formalisms. We use N3 rules to explicitly and formally state the mapping from local ontologies to domain ontologies. The resulting data ex- pressed in domain formalisms can be integrated and analyzed, though originat- ing from very distinct sources. Practices of applying the two-step approach in the infectious disorders and cancer domains are introduced. Keywords: Semantic interoperability, N3 rules, SPARQL, formal clinical data, data integration, data analysis, clinical information system. 1 Introduction A decade ago formal semantics, the study of logic within a linguistic framework, found a new means of expression, i.e. the World Wide Web and with it the Semantic Web [1]. The Semantic Web provides additional capabilities that enable information sharing between different resources which are semantically represented. It consists of a set of standards and technologies that include a simple data model for representing information (RDF) [2], a query language for RDF (SPARQL) [3], a schema language describing RDF vocabularies (RDFS) [4], a few syntaxes to represent RDF (RDF/XML [20], N3 [19]) and a language for describing and sharing ontologies (OWL) [5]. -
View Song List
Piano Bar Song List 1 100 Years - Five For Fighting 2 1999 - Prince 3 3am - Matchbox 20 4 500 Miles - The Proclaimers 5 59th Street Bridge Song (Feelin' Groovy) - Simon & Garfunkel 6 867-5309 (Jenny) - Tommy Tutone 7 A Groovy Kind Of Love - Phil Collins 8 A Whiter Shade Of Pale - Procol Harum 9 Absolutely (Story Of A Girl) - Nine Days 10 Africa - Toto 11 Afterglow - Genesis 12 Against All Odds - Phil Collins 13 Ain't That A Shame - Fats Domino 14 Ain't Too Proud To Beg - The Temptations 15 All About That Bass - Meghan Trainor 16 All Apologies - Nirvana 17 All For You - Sister Hazel 18 All I Need Is A Miracle - Mike & The Mechanics 19 All I Want - Toad The Wet Sprocket 20 All Of Me - John Legend 21 All Shook Up - Elvis Presley 22 All Star - Smash Mouth 23 All Summer Long - Kid Rock 24 All The Small Things - Blink 182 25 Allentown - Billy Joel 26 Already Gone - Eagles 27 Always Something There To Remind Me - Naked Eyes 28 America - Neil Diamond 29 American Pie - Don McLean 30 Amie - Pure Prairie League 31 Angel Eyes - Jeff Healey Band 32 Another Brick In The Wall - Pink Floyd 33 Another Day In Paradise - Phil Collins 34 Another Saturday Night - Sam Cooke 35 Ants Marching - Dave Matthews Band 36 Any Way You Want It - Journey 37 Are You Gonna Be My Girl? - Jet 38 At Last - Etta James 39 At The Hop - Danny & The Juniors 40 At This Moment - Billy Vera & The Beaters 41 Authority Song - John Mellencamp 42 Baba O'Riley - The Who 43 Back In The High Life - Steve Winwood 44 Back In The U.S.S.R.