Harith Alani
Harith Alani
Harith
Alani
ad0c7d68490b84d6c7f8b0cb8aa1e457559386ef
Sean Bechhofer
Sean Bechhofer
Sean
Bechhofer
cdc01cde5b33382c60bdc4c0a68ca5c1736ec313
Steve Speicher
Steve Speicher
Steve
Speicher
b8c0d25897216365cf21cc244576115458dd754d
Andreas Schultz
Andreas Schultz
Andreas
Schultz
a73f9b6c1d1b2df5d2c1deea7d369d12612ff686
Bernhard Schandl
Bernhard Schandl
Bernhard
Schandl
079c9cf4971f05cbfee8e2bbdb5f9d9cfa777b83
Chris Lowis
Chris Lowis
Chris
Lowis
4f8e414fd43dcb061b7059fca19339e2c08a7446
DEI, Politecnico di Milano
DEI, Politecnico di Milano
Oktie Hassanzadeh
Oktie Hassanzadeh
Oktie
Hassanzadeh
6051b86834b107d02b15bd1dd906a544729df7fb
Tim Berners-Lee
Tim Berners-Lee
Tim
Berners-Lee
Axel-Cyrille Ngonga Ngomo
Axel-Cyrille Ngonga Ngomo
Axel-Cyrille
Ngonga Ngomo
3e873fc82e7405de39cb8dc6f2d2c2e445f8c043
BBC
BBC
Valentina Presutti
Presutti
Valentina Presutti
Valentina
Valentina Presutti
Presutti
Valentina
7cdf93aba3cd434a666422057efd1e7df4bf3bac
Gong Cheng
Gong Cheng
Gong
Cheng
90949b51ff990bbe53fa3b030fb31b3c90634551
Prof.
Prof.
Haklae Kim
Haklae Kim
Haklae
Kim
0ea824b8eab6ec74f10daf53ecfa771cbd750b30
Christoph Böhm
Christoph Böhm
Christoph
Böhm
a4f7afa285e6ab408a0534db3ef2e9fc9d358609
Aidan Hogan
Aidan Hogan
Aidan
Hogan
d2163e057507f828085f322cc77dc43b4105a158
Arnaud Le Hors
Arnaud Le Hors
Arnaud
Le Hors
fdb386be13e5f174246074517649e94180fd777d
Dataset about ldow2012.
Tue May 03 19:03:58 CEST 2016
Fabien Gandon
Fabien Gandon
Fabien
Gandon
583b2ab35d1cef69e21b25a7f36ec5a36e11d31d
Thorsten Krüger
Thorsten Krüger
Thorsten
Krüger
0f9a07ed9da2425024e704ebb8e241f45a31f33f
Linked Data
Named Entity extractors
Information extraction
Evaluation
We have often heard that data is the new oil. In particular, extracting information from semi-structured textual documents on the Web is key to realize the Linked Data vision. Several attempts have been proposed to extract knowledge from textual documents, extracting named entities, classifying them according to pre-defined taxonomies and disambiguating them through URIs identifying real world entities. As a step towards interconnecting the Web of documents via those entities, different extractors have been proposed. Although they share the same main purpose (extracting named entity), they differ from numerous aspects such as their underlying dictionary or ability to disambiguate entities. We have developed NERD, an API and a front-end user interface powered by an ontology to unify various named entity extractors. The unified result output is serialized in RDF according to the NIF specification and published back on the Linked Data cloud. We evaluated NERD with a dataset composed of five TED talk transcripts, a dataset composed of 1000 New York Times articles and a dataset composed of the 217 abstracts of the papers published at WWW 2011.
NERD meets NIF: Lifting NLP Extraction Results to the Linked Data Cloud
NERD meets NIF: Lifting NLP Extraction Results to the Linked Data Cloud
University of Seville
University of Seville
Universidad de Sevilla
Universidad de Sevilla
Yunjia Li
Yunjia Li
Yunjia
Li
bd1e3cb53855eb1dd72620052d91e85a7e483e1a
Leigh Dodds
Leigh Dodds
Leigh
Dodds
1bca73e5c6916c738d6ec7cc0597ad0e395e7ace
IBM
IBM
Felix Naumann
Felix Naumann
Felix
Naumann
4df7fbc69778c001a25d244ebfbe1514e925f061
Stuart E. Madnick
Stuart E. Madnick
Stuart
Madnick
08445a31a78661b5c746feff39a9db6e4e2cc5cf
Ingenta
Ingenta
Wendy Hall
Wendy Hall
Wendy
Hall
27ee74540695fd57411b7614bc1826fd8e2bed4e
Andrea Giovanni Nuzzolese
Andrea Giovanni Nuzzolese
Andrea
Nuzzolese
71a761b4b91daced90923b5fc2d37f7afeb2c501
Fujitsu Laboratories of Europe
Fujitsu Laboratories of Europe
Talis Systems Ltd
Talis Systems Ltd
Universität Leipzig
University of Leipzig
Universität Leipzig
University of Leipzig
Sebastian Hellmann
Sebastian Hellmann
Sebastian
Hellmann
3b9b030bfa83b9c747d525b7943829d3abc2813b
EURECOM
EURECOM
Yves Raimond
Yves Raimond
Yves
Raimond
c99a220cc2b8dacc4d4de0342a3909c73eb4e2be
Nicolas Delaforge
Nicolas Delaforge
Nicolas
Delaforge
8c4dbc19d838e6693674295c3d5a22cc568e5e09
Paolo Ciancarini
Paolo Ciancarini
Paolo
Ciancarini
f2918e17b4b356eb9731b54b0fd710dbfede6681
Structured Dynamics LLC
Structured Dynamics LLC
INRIA
INRIA
Ivan Herman
Ivan Herman
Ivan
Herman
5ac8032d5f6012aa1775ea2f63e1676bafd5e80b
World Wide Web Consortium
World Wide Web Consortium
Sören Auer
Sören Auer
Sören
Auer
09ac456515dee0896e8eba4b06ae589bef2069cf
Aldo
Gangemi
Gangemi
Aldo Gangemi
8d7f004803b48a3b7c5e9f73dc16953069a6632d
Aldo
Aldo Gangemi
Aldo Gangemi
Raphael Troncy
Raphael Troncy
Raphael
Troncy
76b8645ac23d412d99c23dd95e0fbbe092d3f730
SIEMENS
SIEMENS
Gnowsis.com
Gnowsis.com
Knowledge Media Institute
Knowledge Media Institute
Michael K. Bergman
Michael K. Bergman
Michael
Bergman
b6876bb3a1ddb7cb934f8a991366fcb8dd64a7e0
Martin Bruemmer
Martin Bruemmer
Martin
Bruemmer
1e703d9aa0e8bc8eda4e46d183e8af25fe03ab44
University of Oxford
University of Oxford
Dave Reynolds
Dave Reynolds
Dave
Reynolds
c496c063ea605640c8bd5ae7ab3adce9bbef360e
Daniel Schwabe
Daniel Schwabe
Daniel
Schwabe
64426ee795421651f6fc88bf0fca221c52c4005a
Axel Polleres
Axel Polleres
Axel
Polleres
35a8d4858ba240996a6f71836d93fbfdcd2b4843
Prateek Jain
Prateek Jain
Prateek
Jain
de5ee5f6661d6e948785f17c7a2410ce6d162376
Michiel Hildebrand
Michiel Hildebrand
Michiel
Hildebrand
184569c6f14af77bb174676f6230cf30075ae928
Ian Dickinson
Ian Dickinson
Ian
Dickinson
045bd7b29a53dc34779605fcbf7a97669cd008fa
Oscar Corcho
Oscar Corcho
Oscar
Corcho
efbd90eca236ae3131e67f30e3abe0a1bceff305
Marta Corubolo
Marta Corubolo
Marta
Corubolo
a76a1efbdfb40d73448dc59460b4a6ddabda3c08
STLab (ISTC-CNR)
STLab (ISTC-CNR)
Irene Celino
Irene Celino
Irene
Celino
a62cfa4877ae7d8b225f36c1afeeeec08a5316ae
DFKI GmbH
DFKI GmbH
Martin Nally
Martin Nally
Martin
Nally
15238a47d2525294e91f8f05e52a66699cfc8c73
PeerIndex
PeerIndex
(unknown)
(unknown)
Ulm University, Institute of Artificial Intelligence
Ulm University, Institute of Artificial Intelligence
Michael Hausenblas
Michael Hausenblas
Michael
Hausenblas
327b61f3721afbd39dceadf5e5b4fc2d79d5dcc8
Yahoo! Research
Yahoo! Research
Fudan University
Fudan University
University of Manchester
University of Manchester
CNR-ISTC
CNR-ISTC
Gregory Todd Williams
Gregory Todd Williams
Gregory
Williams
f80a0f19d2a0897b89f48647b2fb5ca1f0bc1cb8
Freie Universität Berlin
Freie Universität Berlin
Knud Möller
Knud Möller
Knud
Möller
88b9dc808f3c45ed427e8f4d9a2726b55e7a4571
Hasso-Plattner-Institute Potsdam
Hasso-Plattner-Institute Potsdam
Hasso Plattner Institute, Potsdam
Hasso Plattner Institute, Potsdam
INDACO, Politecnico di Milano
INDACO, Politecnico di Milano
Toni Gruetze
Toni Gruetze
Toni
Gruetze
e58ee4d04f4fe6364beeacdb29b5b5cc7a744d6a
Luca Costabello
Luca Costabello
Luca
Costabello
2a8ce9a87fb5a19cf4a86a84b7fab221e4e4b2b2
Markus Krötzsch
Markus Krötzsch
Markus
Krötzsch
00a53bb4ebedc71fc02952bad5bc837c7115a781
University of Toronto
University of Toronto
Juan F. Sequeda
Juan F. Sequeda
Juan
Sequeda
e36a6c5f10bf558670ec81424012f651b25e23a4
Igor Popov
Igor Popov
Igor
Popov
27ca662b5506dd3da7f837d818ae20532e839a5e
Alexandre Passant
Alexandre Passant
Alexandre
Passant
0cfa0c4363ecc5d6a0250fdac77c4267cab8a4dd
Epimorphics Ltd.
Epimorphics Ltd.
Epimorphics Ltd
Epimorphics Ltd
Stefano Salsano
Stefano Salsano
Stefano
Salsano
be3288df83d018b1de574c8a208cd3373e978ae3
We describe work-in-progress on the design and methodology of the Dynamic Linked Data Observatory: a framework to monitor Linked Data over an extended period of time. The core goal of our work is to collect frequent, continuous snapshots of a subset of the Web of Data that is interesting for further study and experimentation, with an aim to capture raw data about the dynamics of Linked Data. The resulting corpora will be made openly and continuously available to the Linked Data research community. Herein, we (1) motivate the importance of such a corpus; (2) out- line some of the use-cases and requirements for the resulting snapshots; (3) discuss different “views” of the Web of Data which affect how we define a sample to monitor; (4) detail how we select the scope of the monitoring experiment through sampling, (5) discuss the final design of the monitoring framework which will capture regular snapshots of (subsets of) the Web of Data over the coming months and years.
Sampling Methods
Towards a Dynamic Linked Data Observatory
Towards a Dynamic Linked Data Observatory
Linked Data
Web Dynamics
Humboldt-Universität zu Berlin
Humboldt-Universität zu Berlin
Herbert Van De Sompel
Herbert Van De Sompel
Herbert
Van De Sompel
592bc99d0b9ea4b006c0989c746c59bac50b2530
Nicholas Humfrey
Nicholas Humfrey
Nicholas
Humfrey
3f361af12a473b48852819e3ff9153783149a26a
Mathieu D'Aquin
Mathieu D'Aquin
Mathieu
D'Aquin
e61cc68181adeb9fbbddc539a6fa01dd24b299c8
Giuseppe Rizzo
Giuseppe Rizzo
Giuseppe
Rizzo
befc26a32bd6815d3c6e5290dc4b3d58e947acd5
Digital Enterprise Research Institute, NUI Galway
Digital Enterprise Research Institute (DERI), NUI Galway
Digital Enterprise Research Institute (DERI), National University of Ireland, Galway
Digital Enterprise Research Institute, National University of Ireland, Galway
Digital Enterprise Research Institute (DERI), NUI Galway
Digital Enterprise Research Institute, NUI Galway
Digital Enterprise Research Institute (DERI), National University of Ireland, Galway
Digital Enterprise Research Institute, National University of Ireland, Galway
Massachusetts Institute of Technology
Massachusetts Institute of Technology
Giovanni Bartolomeo
Giovanni Bartolomeo
Giovanni
Bartolomeo
f71dda84f6e1e83e15543e714996e5d0841aefa0
Rensselaer Polytechnic Institute
Rensselaer Polytechnic Institute
Mariano Consens
Mariano Consens
Mariano
Consens
d2bb00f4d62c71807808b1d556e40a09e5bcbe4c
Robert Isele
Robert Isele
Robert
Isele
5413079e510b7893a00b0b9365ee948efd17d5c2
Ian Millard
Ian Millard
Ian
Millard
d2fe0a32cc9a0000993b21b81b6c080f89d0aa4c
Carlos R. Rivero
Carlos R. Rivero
Carlos
Rivero
9729975c6e107fc98401b40a92d518cdc59e99c1
Andreas Harth
Andreas Harth
Andreas
Harth
0604d144b935121a11d7495b2751e5d3176fc6fa
CEFRIEL - Politecnico di Milano
CEFRIEL - Politecnico di Milano
VOID
Query Analysis
Querying the Web of Interlinked Datasets using VOID Descriptions
Query Federation
Querying the Web of Interlinked Datasets using VOID Descriptions
Dataset Selection
Querying the web of data is a functioning of the Semantic Web. Today, the Semantic Web is characterized as a Web of interlinked datasets, and thus querying web can be seen as dataset integration on the web. Also, this dataset integration must be transparent from the user as if she is querying the whole web. In this paper, we introduce a federated query engine called WoDQA (Web of Data Query Analyzer) which uses VOID standard and automates dataset discovery for a query on the web. Query federation includes two stages which the first one is dataset selection and the latter one is query optimization. WoDQA focuses on powerful dataset elimination and analyzes query structure in detail besides using predicate indexes. Thus, dataset and linkset descriptions in VIOD stores are analyzed for a SPARQL query and a federated query on web of interlinked datasets is constructed. By virtue of linkset concept, WoDQA integrates links between datasets into selection of federated data sources.
Querying Linked Data
SPARQL
Andy Seaborne
Andy Seaborne
Andy
Seaborne
2efe5b34e4a919ff4712d4519432c7157c8e6f2a
Universidad Politécnica de Madrid
Universidad Politécnica de Madrid
INRIA Sophia
INRIA Sophia
INRIA Sophia Antipolis
INRIA Sophia Antipolis
David Ruiz
David Ruiz
David
Ruiz
2be8eec47d8699357d9140671b00080adea17cb9
Wenwen Li
Wenwen Li
Wenwen
Li
c4240ed91bd836cd1c635c27bbcfd6228437dba2
UC Berkeley
UC Berkeley
Department of Computer Sciences, The University of Texas at Austin
Department of Computer Sciences, The University of Texas at Austin
Mike Wald
Mike Wald
Mike
Wald
dbac203aa2b30d8966a8988ead8eaaae73ea924f
University of Rome Tor Vergata
University of Rome Tor Vergata
University of Rome "Tor Vergata"
University of Rome "Tor Vergata"
Serena Villata
Serena Villata
Serena
Villata
200fa053a13df35295f805b07e244b0922bb0010
Christopher Brewster
Christopher Brewster
Christopher
Brewster
f8a34c46e54009b5108a66db7294aed348013126
Emanuele Della Valle
Emanuele Della Valle
Emanuele
Della Valle
1ee386235c89959195075bc4944d5c68f8265f96
Andreas Langegger
Andreas Langegger
Andreas
Langegger
29e44240f2956bbf951710aafe088875679a8787
Linked Data Basic Profile for Application Integration
alm
enterprise_application
usage_patterns
standards
Linked Data Basic Profile for Application Integration
application_integration
Linked Data, as defined by Tim Berners-Lee’s 4 rules, is usually associated with the Semantic Web but it also represents an opportunity to address some of the integration challenges the IT industry has been wrestling with for many years in a new way which is more powerful and which is based on open standards.
In the context of the Semantic Web which aims at creating a web of data similar to the web of documents, Linked Data is used to expose information on the Internet in a machine readable format which conveys the semantic of the data along with the data itself. This approach provides a powerful infrastructure for publishing information such as public records and the processing of that information by various types of applications. These applications include search engines, which can return more accurate results and reasoning engines, which can infer new information from the one being exposed. Such examples include pharmaceutical applications as well as the now famous IBM Watson system that defeated the best Jeopardy champions.
But IBM has been using Linked Data as an architectural model and implementation technology for application and service integration in various domains including Application Lifecycle Management (ALM) and Integration System Management (ISM).
This paper explains why Linked Data, which builds on the existing World Wide Web infrastructure, presents some unique characteristics, such as being distributed and scalable, that may allow the industry to succeed where other application integration approaches have failed. It discusses lessons we have learned along the way and some of the challenges we have been facing in using Linked Data to integrate enterprise applications.
Finally, we discuss several areas that could benefit from additional standard work and discuss several commonly applicable usage patterns along with proposals on how to address them using the existing W3C standards in the form of a Linked Data Basic Profile. This includes techniques applicable to clients and servers that read and write linked data, a type of container that allows new resources to be created using HTTP POST and existing resources to be found using HTTP GET (analogous to things like Atom Publishing Protocol).
Linkeddata
University of Bologna/STLab, ISTC-CNR
University of Bologna/STLab, ISTC-CNR
Mischa Tuffield
Mischa Tuffield
Mischa
Tuffield
5cd3e6661b9c5ea896008cf9dbfbea301d602d30
Olaf Hartig
Olaf Hartig
Olaf
Hartig
9c09772d208636b590bf7b41d9d1976b80f6b335
Stefano Fumeo
Stefano Fumeo
Stefano
Fumeo
e30aa9fce28bdf31d1e65a01f50e02bb5f087ade
A Spectrometry of Linked Data
Entity
Entity mining is still a troublesome open problem. In past years many approaches served to automate equivalence links generation using schema matching or various heuristics based on the recognition of similar property values. Using the equivalences already deployed in the Linked Data cloud, we introduce a methodology to detect and analyze the composition of clusters of coreferences mostly representative of an entity for the Linked Data community.
A Spectrometry of Linked Data
Identity
Linked Data
Provenance
Semantic Web
Linked Data
Towards Interoperable Provenance Publication on the Linked Data Web
Towards Interoperable Provenance Publication on the Linked Data Web
RDF
Provenance provides vital information for evaluating quality and trustworthiness of information on the Web. To achieve this we must have access to semantically interchangeable provenance information and an agreement on where and how this information to be located. The ongoing W3C Provenance Interchange Working Group provides a promise towards leveraging these problems. In this position paper, we analyze how the upcoming standards could fit together with existing provenance vocabularies and publication approaches so that we achieve the optimal interoperability now and in the near future. Because the standardization is still an ongoing effort, any analysis results presented in this paper are positional and are aimed at communicating the latest development of the working group to the community.
Google
Google
Krzysztof Janowicz
Krzysztof Janowicz
Krzysztof
Janowicz
a09448c2b7c9d3d4d56b2a11c7dee3a2aee7c289
Kno.e.sis, Wright State University
Kno.e.sis, Wright State University
Peter Mika
Peter Mika
Peter
Mika
c0d6551197a0295bfc604841a994d544e0091665
Anna Lisa Gentile
Anna Lisa
Gentile
Siemens AG Österreich / DERI, National University of Ireland, Galway
Siemens AG Österreich / DERI, National University of Ireland, Galway
Type inference through the analysis of Wikipedia links
Type inference through the analysis of Wikipedia links
Linked Data
Classification
DBpedia contains millions of untyped entities, either if we consider the native DBpedia ontology, or Yago plus WordNet. Is it possible to automatically classify those entities? Based on previous work on wikilink invariances, we wondered if wikilinks convey a knowledge rich enough for their classification.In this paper we give three contributions. Concerning the DBpedia link structure, we describe some measurements and notice both problems (e.g. the bias that could be induced by the incomplete ontological coverage of the DBpedia ontology), and potentials existing in current type coverage. Concerning classification, we present two techniques that exploit wikilinks, one based on induction from machine learning techniques, and the other on abduction. Finally, we discuss the limited results of classification, which confirmed our fears expressed in the description of general figures from the measurement. We also suggest some new possible approaches to entity classification that could be taken, based on more solid grounds.
DBpedia
owl ld
Seven years on from OWL becoming a W3C recommendation, and two years on from the more recent OWL 2 W3C recommendation, OWL has still experienced only patchy uptake on the Web. Although certain OWL features (like owl:sameAs) are very popular, other features of OWL are largely neglected by publishers in the Linked Data world. This may suggest that despite the promise of easy implementations and the proposal of tractable profiles suggested in OWL’s second version, there is still no “right” standard fragment for the Linked Data community. In this paper, we (1) analyse uptake of OWL on the Web of Data, (2) gain insights into the OWL fragment that is actually used/usable on the Web, where we arrive at the conclusion that this fragment is likely to be a simplified profile based on OWL RL, (3) propose and discuss such a new fragment, which we call OWL LD (for Linked Data).
linked data
OWL: Yet to arrive on the Web of Data?
owl 2 rl
owl
OWL: Yet to arrive on the Web of Data?
semantic web
Tom Heath
Tom Heath
Tom
Heath
1dd0dab717be578c153b8ed70bee284845439706
zeb/information.technology Vienna
zeb/information.technology Vienna
Andrea Giovanni Nuzzolese
Andrea Giovanni
Nuzzolese
Xiaoqing Zheng
Xiaoqing Zheng
Xiaoqing
Zheng
cccf0e42b43809430e8793baeaeb41f689f4ef20
Semantic heterogeneity
SPARQL Query Mediation over RDF Data Sources with Disparate Contexts
SPARQL Query Mediation over RDF Data Sources with Disparate Contexts
Query mediator
Data integration
Many Semantic Web applications require the integration of data from distributed and autonomous RDF data sources. However, the values in the RDF triples would be frequently recorded simply as the literal, and additional contextual information such as unit and format is often omitted, relying on consistent understanding of the context. In the wider context of the Web, it is generally not safe to make this assumption. The Context Interchange strategy provides a systematic approach for mediated data access in which semantic conflicts among heterogeneous data sources are automatically detected and reconciled by a context mediator. In this paper, we show that SPARQL queries that involve multiple RDF graphs originating from different contexts can be mediated in the way using the Context Interchange (COIN) Framework.
Semantic interoperability
Self
Self
Ziya Akar
Ziya Akar
Ziya
Akar
c34dab8daa21fc50d6b3da6cf208f6d5171b7cb1
Li Ding
Li Ding
Li
Ding
a7d0967c21ba8952ab0443bf822750d4c44afe2c
Jürgen Umbrich
Jürgen Umbrich
Jürgen
Umbrich
b9514eba2ed2ae41a072765fe3ed3544de10974f
Jun Zhao
Jun Zhao
Jun
Zhao
1bd08cb5d8a73a4cd9da350b32dbf0bb56b44c6b
Erdem Eser Ekinci
Erdem Eser Ekinci
Erdem
Ekinci
9053f10149136868805fff5991ff63c133b1e9db
M.C. Schraefel
M.C. Schraefel
M.C.
Schraefel
041a973a784cd5c81056f0c3d002791e8f5df8e6
Karlsruhe Institute of Technology
Karlsruhe Institute of Technology
Nigel Shadbolt
Nigel Shadbolt
Nigel
Shadbolt
e966302104bd52f060f0e4545e8299f2d54ee3ae
Nanjing University, China
Nanjing University, China
Aston University
Aston University
Matthew Rowe
Matthew Rowe
Matthew
Rowe
e317a9678ced3513e8b9e50ee22a7a54495df3a5
Gianluca Correndo
Gianluca Correndo
Gianluca
Correndo
7a9a1fd95595d708e6be4d5de3468027593146d2
Thomas Steiner
Thomas Steiner
Thomas
Steiner
08e16fa0ce4602315e959303c0f2f0b26a7cd4c2
Richard Cyganiak
Richard Cyganiak
Richard
Cyganiak
39f3c9b7479a83c76596a7c92b61f76dee3f5343
Xitong Li
Xitong Li
Xitong
Li
08445a31a78661b5c746feff39a9db6e4e2cc5cf
Gary Wills
Gary Wills
Gary
Wills
d68fa2ffdef3d0cf9ffa1e6a56eeb6e83e6f247d
Roger Menday
Roger Menday
Roger
Menday
36715182d3779a8a59e7ea6b5197a995bfa693a1
Gunnar Aastrand Grimnes
Gunnar Aastrand Grimnes
Gunnar
Grimnes
308577beee07afcb8932615cf535410d57af5024
Computer Engineering Department of Ege University
Computer Engineering Department of Ege University
Tope Omitola
Tope Omitola
Tope
Omitola
c3b947cbba730e8b07d09a0e6254ae7be46f56e6
Simone Contessa
Simone Contessa
Simone
Contessa
a64b4301ba3ed64f5a00f928c8c8606a40cda56d
University of Oxford, Department of Computer Science
University of Oxford, Department of Computer Science
Interacting with the Web of Data through a Web of Inter-connected Lenses
User Interface
Interaction
As a medium of structured information available on the Web, Linked Data is still hard to access for most end users. Current solutions facilitating end user access to Linked Data are either thought the use of data-mapping approaches, which allow configureable interfaces to be quickly deployed over pre-selected aggregations of Linked Data, or enable users themselves to browse the Web of Data through the use of generic data browsers. While the first approach is useful and promotes surfacing and easy repurposing of structured data it does little to promote the use of linkages to other, remote datasets. The second approach is much less useable for end users, however enables them to experience browsing a inter-connected Web of Data. In this paper we present mashpoint, a framework that aims to provide a middle ground between both approaches. The approach treats data-centric applications as high-level lenses over the data, and allows selections of data to be pivoted between applications thus facilitating navigation. The paper presents an initial prototype and discusses both implications and challenges in terms of interaction and technology.
Linked Data
Interacting with the Web of Data through a Web of Inter-connected Lenses
Anja Jentzsch
Anja Jentzsch
Anja
Jentzsch
cc5a9befda7b2fde603a51ff419a5512a922d3f7
Hugh Glaser
Hugh Glaser
Hugh
Glaser
623018b35a1850179ed2903e332d96978eeb1d4f
Birte Glimm
Birte Glimm
Birte
Glimm
d9e3004543dab6b7586ec0c3846985b999320232
ontology alignment
scalability
Holistic and Scalable Ontology Alignment for Linked Open Data
LOD
Holistic and Scalable Ontology Alignment for Linked Open Data
The Linked Open Data community continuously releases massive amounts of RDF data that shall be used to easily create applications that incorporate data from different sources. Inter-operability across different sources requires links at instance- and at schema-level, thus connecting entities on the one hand and relating concepts on the other hand. State-of-the-art entity- and ontology-alignment methods produce high quality alignments for two source ontologies, whereas an identification of relevant and meaningful pairs of ontologies is a precondition. Therefore, these methods lack in dealing with heterogeneous web data from many sources simultaneously, e.g., data from a web crawl. To this end we propose Holistic Concept Matching (HCM). HCM aligns thousands of concepts from hundreds of ontologies (from many sources) simultaneously, while maintaining scalability and leveraging the global view on the entire data cloud. We evaluated our approach against the OAEI ontology alignment benchmark as well as the 2011 Billion Triple Challenge data and present high precision results.
Kasabi
Kasabi
Pavel Shvaiko
Pavel Shvaiko
Pavel
Shvaiko
6fc18ceccfe31ac7c9d76fea4e28a96b6fdd804e
Tobias Käfer
Tobias Käfer
Tobias
Käfer
be155e4ec9834c71479615f4fde4d3ef6609690e
Kai-Uwe Sattler
Kai-Uwe Sattler
Kai-Uwe
Sattler
5a074b484cb48f9056d70c9d136a3c6a296346e0
Daniele Dell'Aglio
Daniele Dell'Aglio
Daniele
Dell'Aglio
d961eff3c9dc7d0a7ac7c829fa3a1562f75810bf
Knowledge Media Institute, the Open University
Knowledge Media Institute, the Open University
KMi, The Open University
KMi, The Open University
Jens Lehmann
Jens Lehmann
Jens
Lehmann
01fee219e665ecea3905f361517b2bd4a344975d
Linked Data Access Goes Mobile: Context-Aware Authorization for Graph Stores
Linked Data
Ubiquitous Web
Access Control
Linked Data Access Goes Mobile: Context-Aware Authorization for Graph Stores
To encourage data providers to publish a maximum of data on the Web, we propose a mechanism to define lightweight access control policies for graph stores. Influenced by the steep growth of the mobile web, our Linked Data access control framework features context-aware control policies. The proposed framework is exclusively grounded on standard Semantic Web languages. The framework architecture is designed as a pluggable filter for generic SPARQL endpoints and it has been evaluated on a test dataset.
Department of Computer Engineering, Ege University 35100 Bornova, Izmir, Turkey
Department of Computer Engineering, Ege University 35100 Bornova, Izmir, Turkey
University of Edinburgh
University of Edinburgh
50c02ff93e7d477ace450e3fbddd63d228fb23f3
Chris
dde0fd05db4aa12fbadc26ac8a6c82eaedc10ab1
Bizer
Chris Bizer
Christian Bizer
Chris Bizer
Christian Bizer
Christian
Erik Wilde
Erik Wilde
Erik
Wilde
cdee7ba2e53e5c97d302fe5e107715e9d271f6d5
University of Southampton
University of Southampton
Linked Data
Speech Processing
Automated interlinking of speech radio archives
Named Entity Extraction
Text Processing
Automated interlinking of speech radio archives
The BBC is currently tagging programmes manually, using DBpedia as a source of tag identifiers, and a list of suggested tags extracted from their synopsis. These tags are then used to help navigation and topic-based search of BBC programmes. However, given the very large number of programmes available in the archive, most of them having very little metadata attached to them, we need a way of automatically assigning tags to programmes. We describe a framework to do so, using speech recognition, text processing and concept tagging techniques. We evaluate this framework against manually applied tags, and compare it with related work. We find that this framework is good enough to bootstrap the interlinking process of archived content.
Concept Tagging
Linked Data sources on theWeb use a wide range of different
vocabularies to represent the same type of entity. For some
types of entities, like people or bibliographic record, common
vocabularies have emerged that are used by multiple data
sources. But even for representing data of these common
types, different user communities use different competing
common vocabularies. Linked Data applications that want
to understand as much data from the Web as possible, thus
need to overcome the vocabulary heterogeneity and translate
the original data into a single target vocabulary. To
support application developers with this integration task,
several Linked Data translation systems have been developed.
These systems provide languages to represent correspondences
in the form of declarative mappings and use
these mappings to translate heterogeneous Web data into a
single target vocabulary.In this paper, we present a benchmark
for comparing the expressivity as well as the runtime
performance of data translation systems. Based on a set
of examples from the LOD Cloud, we developed a catalog
of fifteen data translation patterns and survey how often
these patterns occur in our example set. Based on these
statistics, we designed the LODIB (Linked Open Data Integration
Benchmark) which aims to reflect the real-world
heterogeneities that exist on the Web of Data. We apply the
benchmark to test the performance of two data translation
systems, Mosto and LDIF, and compare the performance of
the systems with the SPARQL CONSTRUCT performance
of the Jena TDB RDF store.
Data translation
Benchmarking the Performance of Linked Data Translation Systems
Linked Data
Benchmarking the Performance of Linked Data Translation Systems
Benchmarking
Dept. of Informatics, PUC-Rio
Dept. of Informatics, PUC-Rio
Tayfun Gökmen Halaç
Tayfun Gökmen Halaç
Tayfun
Halaç
cb27b168427a9d2f3d63ab1032d24db738206aba
Oğuz Dikenelli
Oğuz Dikenelli
Oğuz
Dikenelli
1d48e2893a47715c560c43af24bee95cfd8d8b8b
University of California, Santa Barbara
University of California, Santa Barbara
Los Alamos National Laboratory, Research Library
Los Alamos National Laboratory, Research Library
games with a purpose
urban computing
UrbanMatch – linking and improving Smart Cities Data
Urban-related data and geographic information are becoming mainstream in the Linked Data community due also to the popularity of Location-based Services. In this paper, we introduce the UrbanMatch game, a mobile gaming application that joins data linkage and data quality/trustworthiness assessment in an urban environment. By putting together Linked Data and Human Computation, we create a new interaction paradigm to consume and produce location-specific linked data by involving and engaging the final user. The UrbanMatch game is also offered as an example of value proposition and business model of a new family of linked data applications based on gaming in Smart Cities.
smart cities
UrbanMatch – linking and improving Smart Cities Data
linked data
human computation
Synote: Weaving Media Fragments and Linked Data
annotation
media fragment
search
schema.org
linked data
Synote: Weaving Media Fragments and Linked Data
While end users could easily share and tag the multimedia resources online, the searching and reusing of the inside content of multimedia, such as a certain area within an image or a ten minutes segment within a one-hour video, is still difficult. Linked data is a promising way to interlink media fragments with other resources. Many applications in Web 2.0 have generated large amount of external annotations linked to media fragments. In this paper, we use Synote as the target application to discuss how media fragments could be published together with external annotations following linked data principles. Our design solves the dereferencing, describing and interlinking methods problems in interlinking multimedia. We also implement a model to let Google index media fragments which improves media fragments' online presence. The evaluation shows that our design can successfully publish media fragments for semantic Web agent and traditional search engine. Publishing media fragments, using the design we describe in this paper, will lead to better indexing of multimedia resources and their consequent findability and reuse.
Patrick Sinclair
Patrick Sinclair
Patrick
Sinclair
7fda53d68ed5cd2d511409397900404a0c0001d4
TU Ilmenau
TU Ilmenau
TasLab, Informatica Trentina
TasLab, Informatica Trentina
VU University Amsterdam
VU University Amsterdam
AIFB, Karlsruhe Institute of Technology
AIFB, Karlsruhe Institute of Technology
Yuzhong Qu
Yuzhong Qu
Yuzhong
Qu
57682429d3d2a18d6a9c2c2b0559a2105ca034a1
Harry Halpin
Harry Halpin
Harry
Halpin
c5e75a0dd882184416c8680f5c402a261314bb79