web usage mining
linked data
linked data
web usage mining
ontology maintenance
ontology maintenance
The Linked Data initiative gained momentum inside as well as outside of theresearch community. Thus, it is already an accepted research issue to investigate usage mining in the context ofthe Web of Data from various perspectives. We are currently working onan approach that applies such usage mining methods and analysis to support ontology and datasetmaintenance tasks. This paper presents one part of this work, namely a methodto detect errors or weaknesses within ontologies used for Linked Data populationbased on statistics and network visualizations. We contribute a detailed description of a log file preprocessing algorithm for Web of Data endpoints, a set of statistical measures that help to visualize different usage aspects, and an examplary analysis of one of the most prominent Linked Data set -- DBpedia -- aimed to show the feasibility and the potential of our approach.
Statistical Analysis of Web of Data Usage
Statistical Analysis of Web of Data Usage
Statistical Analysis of Web of Data Usage
The Linked Data initiative gained momentum inside as well as outside of theresearch community. Thus, it is already an accepted research issue to investigate usage mining in the context ofthe Web of Data from various perspectives. We are currently working onan approach that applies such usage mining methods and analysis to support ontology and datasetmaintenance tasks. This paper presents one part of this work, namely a methodto detect errors or weaknesses within ontologies used for Linked Data populationbased on statistics and network visualizations. We contribute a detailed description of a log file preprocessing algorithm for Web of Data endpoints, a set of statistical measures that help to visualize different usage aspects, and an examplary analysis of one of the most prominent Linked Data set -- DBpedia -- aimed to show the feasibility and the potential of our approach.
Stanford University
Stanford University
Stanford
Stanford University
Stanford
Stanford
Dublin City University
Dublin City University
Dublin City University
Alessandro Artale
Alessandro Artale
Alessandro Artale
University of Magdeburg
University of Magdeburg
University of Magdeburg
Cartic Ramakrishnan
Cartic Ramakrishnan
Cartic Ramakrishnan
Institute of Computer Science, FORTH
FORTH-ICS
Institute of Computer Science, FORTH
Institute of Computer Science, FORTH
FORTH-ICS
FORTH-ICS
word sense discrimination
Knowing about language evolution can significantly help to reveal lost information and help access documents containing language that has long since been forgotten. In this position paper we will report on our methods for finding word senses and show how these can be used to reveal important information about their evolution over time. We discuss the weaknesses of current approaches and outline future work to overcome these weaknesses.
Knowing about language evolution can significantly help to reveal lost information and help access documents containing language that has long since been forgotten. In this position paper we will report on our methods for finding word senses and show how these can be used to reveal important information about their evolution over time. We discuss the weaknesses of current approaches and outline future work to overcome these weaknesses.
clustering
language evolution
clustering
language evolution
word sense discrimination
Towards automatic language evolution tracking, A study on word sense tracking
Towards automatic language evolution tracking, A study on word sense tracking
Towards automatic language evolution tracking, A study on word sense tracking
Valentina Presutti
Valentina
Presutti
Joint Workshop on Knowledge Evolution and Ontology Dynamics
Tim Clark
Tim Clark
Tim Clark
L3S Research Center
L3S Research Center
L3S Research Center
Rafael S. Gonçalves
Rafael S. Gonçalves
Rafael S. Gonçalves
Enrico Motta
Enrico Motta
Enrico Motta
Gully Burns
Gully Burns
Gully Burns
Yalemisew Abgaz
Yalemisew Abgaz
Yalemisew Abgaz
Free University of Bolzano-Bozen, Faculty of Computer Science
Free University of Bolzano-Bozen, Faculty of Computer Science
Free University of Bolzano-Bozen, Faculty of Computer Science
Thomas Risse
Thomas Risse
Thomas Risse
Zhisheng Huang
Zhisheng Huang
Zhisheng Huang
Tania Tudorache
Tania Tudorache
Tania Tudorache
Claus Pahl
Claus Pahl
Claus Pahl
Institute of Genomics and Systems Biology, University of Chicago
Institute of Genomics and Systems Biology, University of Chicago
Institute of Genomics and Systems Biology, University of Chicago
ISI / University of Southern California
ISI / University of Southern California
ISI / University of Southern California
Ed Hovy
Ed Hovy
Ed Hovy
Markus Bischoff
Markus Bischoff
Markus Bischoff
Livia Predoiu
Livia Predoiu
Livia Predoiu
Dataset about evodyn2011-alignments.
Tue May 03 19:03:52 CEST 2016
Aldo Gangemi
Aldo
Gangemi
Computation Institute, University of Chicago
Computation Institute, University of Chicago
Computation Institute, University of Chicago
Xerox Research Centre Europe
Xerox Research Centre Europe
Xerox Research Centre Europe
Michael Lawley
Michael Lawley
Michael Lawley
Dept. of Medicine and Human Genetics, University of Chicago
Dept. of Medicine and Human Genetics, University of Chicago
Dept. of Medicine and Human Genetics, University of Chicago
Alpen Adria Universität
Alpen Adria Universität
Alpen Adria Universität
Philipp Fleiss
Philipp Fleiss
Philipp Fleiss
Giorgos Flouris
Giorgos Flouris
Giorgos Flouris
Thomas Meyer
Thomas Meyer
Thomas Meyer
IBM Research
IBM Research
IBM Research
RDBMS
RETRO: A Framework for Semantics Preserving SQL-to-SPARQL Translation
RETRO: A Framework for Semantics Preserving SQL-to-SPARQL Translation
RDF
RETRO: A Framework for Semantics Preserving SQL-to-SPARQL Translation
RDF
RDBMS
Semantic Web
There have been several attempts to make RDBMS and RDFstores inter-operate. The most popular one, D2RQ, has explored onedirection i.e. to look at RDBMS through RDF lenses. In this paper wepresent RETRO, which investigates the reverse direction i.e. to look atRDF through Relational lenses. RETRO generates a relational schemafrom an RDF store, enabling a user to query RDF data using SQL.A significant advantage of this direction in-addition to interoperabilityis that it makes numerous relational tools developed over past severaldecades, available to the RDF stores. In order to provide interoperabilitybetween these two DB systems one needs to resolve the heterogeneitybetween their respective data models and include schema mapping, datamapping and query mapping in the transformation process [1]. However,like D2RQ, RETRO chooses not to physically transform the data anddeals only with schema mapping and query mapping. RETRO’s schemamapping derives a domain specific relational schema from RDF dataand its query mapping transforms an SQL query over the schema into aprovably equivalent SPARQL query, which in-turn is executed upon theRDF store. Since RETRO is a read-only framework, its query mappinguses only a relevant and relationally complete subset of SQL. A proofof correctness of this transformation is given based on compositionalsemantics of the two query languages.
Database Interoperability
Semantic Web
SQL
Query Translation
SQL
SPARQL
SPARQL
Query Translation
Database Interoperability
There have been several attempts to make RDBMS and RDFstores inter-operate. The most popular one, D2RQ, has explored onedirection i.e. to look at RDBMS through RDF lenses. In this paper wepresent RETRO, which investigates the reverse direction i.e. to look atRDF through Relational lenses. RETRO generates a relational schemafrom an RDF store, enabling a user to query RDF data using SQL.A significant advantage of this direction in-addition to interoperabilityis that it makes numerous relational tools developed over past severaldecades, available to the RDF stores. In order to provide interoperabilitybetween these two DB systems one needs to resolve the heterogeneitybetween their respective data models and include schema mapping, datamapping and query mapping in the transformation process [1]. However,like D2RQ, RETRO chooses not to physically transform the data anddeals only with schema mapping and query mapping. RETRO’s schemamapping derives a domain specific relational schema from RDF dataand its query mapping transforms an SQL query over the schema into aprovably equivalent SPARQL query, which in-turn is executed upon theRDF store. Since RETRO is a read-only framework, its query mappinguses only a relevant and relationally complete subset of SQL. A proofof correctness of this transformation is given based on compositionalsemantics of the two query languages.
Kewen Wang
Kewen Wang
Kewen Wang
Markus Luczak-Rösch
Markus Luczak-Rösch
Markus Luczak-Rösch
University of Sao Paulo
University of Sao Paulo
University of Sao Paulo
Andrey Rzhetsky
Andrey Rzhetsky
Andrey Rzhetsky
University of Manchester
University of Manchester
University of Manchester
Graph-based Discovery of Ontology Change Patterns
Ontology Evolution
Ontology Change Representation
Graph-based Discovery of Ontology Change Patterns
Change Log Graph.
Change Log Graph.
Graph-based Discovery of Ontology Change Patterns
Ontologies can support a variety of purposes, ranging fromcapturing conceptual knowledge to the organisation of digital contentand information. However, information systems are always subject tochange and ontology change management can pose challenges. We investigateontology change representation and discovery of change patterns.Ontology changes are formalised as graph-based change logs. We useattributed graphs, which are typed over a generic graph with node andedge attribution.We analyse ontology change logs, represented as graphs,and identify frequent change sequences. Such sequences are applied as a reference in order to discover reusable, often domain-specific and usage driven change patterns. We describe the pattern discovery algorithms and measure their performance using experimental results.
Pattern Discovery Algorithm
Ontologies can support a variety of purposes, ranging fromcapturing conceptual knowledge to the organisation of digital contentand information. However, information systems are always subject tochange and ontology change management can pose challenges. We investigateontology change representation and discovery of change patterns.Ontology changes are formalised as graph-based change logs. We useattributed graphs, which are typed over a generic graph with node andedge attribution.We analyse ontology change logs, represented as graphs,and identify frequent change sequences. Such sequences are applied as a reference in order to discover reusable, often domain-specific and usage driven change patterns. We describe the pattern discovery algorithms and measure their performance using experimental results.
Pattern Discovery Algorithm
Ontology Change Representation
Ontology Evolution
Agnes Sandor
Agnes Sandor
Agnes Sandor
The University Of Texas Dallas
University of Texas at Dallas
The University of Texas at Dallas
The University of Texas at Dallas
The University of Texas at Dallas
The University Of Texas Dallas
University of Texas at Dallas
University of Texas at Dallas
The University Of Texas Dallas
Protege 4 Difference Engine
Ontology
Collaborative
Protege 4 Difference Engine
Version Control
Evolving
Version Control
Ontology
Protege 4 Difference Engine
Collaborative
As ontologies evolve, it becomes important to be able to dis-cover how they have changed over time. In the recent past there havebeen a few very useful tools based on the Manchester OWL API thataddress this issue. However, these new tools do not take into accounthow to align entities in two ontologies when the names of the entitieshave changed. In this case, we need to be able to discover the alignmentbetween the entities in the two ontologies before can match up the struc-tures in the two ontologies. In this paper, we describe a highly optimizedpluggable difference engine that searches for alignments between entitiesin different ontology versions and applies those alignments to display thedifferences in the ontologies. We discuss our experiences applying thetools to a selection of ontologies from the BioPortal ontology reposi-tory, including the performance and accuracy of the tool.
Evolving
As ontologies evolve, it becomes important to be able to dis-cover how they have changed over time. In the recent past there havebeen a few very useful tools based on the Manchester OWL API thataddress this issue. However, these new tools do not take into accounthow to align entities in two ontologies when the names of the entitieshave changed. In this case, we need to be able to discover the alignmentbetween the entities in the two ontologies before can match up the struc-tures in the two ontologies. In this paper, we describe a highly optimizedpluggable difference engine that searches for alignments between entitiesin different ontology versions and applies those alignments to display thedifferences in the ontologies. We discuss our experiences applying thetools to a selection of ontologies from the BioPortal ontology reposi-tory, including the performance and accuracy of the tool.
Facilitating the Analysis of Ontology Differences
OWL
SNOMED CT
SNOMED CT
Ontology Evolution
The analysis of changes between OWL ontologies (in the form of a diff) is an important service for ontology engineering. A purely syntactic analysis of changes is insufficient to distinguish between changes that have logical impact and those that do not. The current state of the art in semantic diffing ignores logically ineffectual changes and lacks any further characterisation of even significant changes. We present and demonstrate a diff method based on an exhaustive categorisation of effectual and ineffectual changes between ontologies. In order to verify the applicability of our approach we apply it to 88 versions of the National Cancer Institute (NCI) Thesaurus (NCIt), and 5 versions of SNOMED CT, demonstrating that all categories are realized throughout the corpus. Based on the outcome of these studies we argue that the devised categorisation of changes is helpful for ontology engineers and their understanding of changes carried out between ontologies.
NCI Thesaurus
Diff
Diff
Ontologies
Facilitating the Analysis of Ontology Differences
Ontologies
NCI Thesaurus
Facilitating the Analysis of Ontology Differences
The analysis of changes between OWL ontologies (in the form of a diff) is an important service for ontology engineering. A purely syntactic analysis of changes is insufficient to distinguish between changes that have logical impact and those that do not. The current state of the art in semantic diffing ignores logically ineffectual changes and lacks any further characterisation of even significant changes. We present and demonstrate a diff method based on an exhaustive categorisation of effectual and ineffectual changes between ontologies. In order to verify the applicability of our approach we apply it to 88 versions of the National Cancer Institute (NCI) Thesaurus (NCIt), and 5 versions of SNOMED CT, demonstrating that all categories are realized throughout the corpus. Based on the outcome of these studies we argue that the devised categorisation of changes is helpful for ontology engineers and their understanding of changes carried out between ontologies.
OWL
Ontology Evolution
query selection
Balancing brave and cautious query strategies in ontology debugging
ontology debugging
description logic
diagnosis
Sequential ontology debugging aims to the efficient discrimination between diagnoses. By querying additional information the debugger can gradually reduce the number of diagnoses to be considered by the user. The selection of the best queries is of central importance for minimizing diagnosis costs. If prior fault probabilities are available, the best results are achieved by entropy based selection methods.However, given some weakly justified priors these methods bravely suggest suboptimal queries. In such a case, it is more efficient to use a no-risk method which prefers queries that eliminate 50% of diagnoses independently of any fault probabilities. However, choosing the appropriate method in advance is impossible because the quality of given priors cannot be assessed before additional information is queried.In this paper we propose a method which combines advantages of both approaches. On the one hand the method takes into account available fault probabilities and the user
Balancing brave and cautious query strategies in ontology debugging
Sequential ontology debugging aims to the efficient discrimination between diagnoses. By querying additional information the debugger can gradually reduce the number of diagnoses to be considered by the user. The selection of the best queries is of central importance for minimizing diagnosis costs. If prior fault probabilities are available, the best results are achieved by entropy based selection methods.However, given some weakly justified priors these methods bravely suggest suboptimal queries. In such a case, it is more efficient to use a no-risk method which prefers queries that eliminate 50% of diagnoses independently of any fault probabilities. However, choosing the appropriate method in advance is impossible because the quality of given priors cannot be assessed before additional information is queried.In this paper we propose a method which combines advantages of both approaches. On the one hand the method takes into account available fault probabilities and the user
query selection
ontology debugging
Balancing brave and cautious query strategies in ontology debugging
description logic
diagnosis
Stefan Dietze
Stefan Dietze
Stefan Dietze
Dietrich Rebholz-Schuhmann
Dietrich Rebholz-Schuhmann
Dietrich Rebholz-Schuhmann
Bhavani Thuraisingham
Bhavani Thuraisingham
Bhavani Thuraisingham
Kavitha Srinivas
Kavitha Srinivas
Kavitha Srinivas
Learning Vague Concepts for the Semantic Web
Vagueness
OWL
Ontology Alignment
Ontology Evolution
OWL
Learning Vague Concepts for the Semantic Web
Ontology Change
Ontology Evolution
Ontologies can be a powerful tool for structuring knowledge, and they are currently the subject of extensive research. Updating the contents of an ontology or improving its interoperability with other ontologies is an important but difficult process. In this paper, we focus on the presence of vague concepts, which are pervasive in natural language, within the framework of formal ontologies. We will adopt a framework in which vagueness is captured via numerical restrictions that can be automatically adjusted. Since updating vague concepts, either through ontology alignment or ontology evolution, can lead to inconsistent sets of axioms, we define and implement a method to detecting and repairing such inconsistencies in a local fashion.
Ontology Change
Vagueness
Ontology Repair
Ontology Alignment
Learning Vague Concepts for the Semantic Web
Ontology Repair
Ontologies can be a powerful tool for structuring knowledge, and they are currently the subject of extensive research. Updating the contents of an ontology or improving its interoperability with other ontologies is an important but difficult process. In this paper, we focus on the presence of vague concepts, which are pervasive in natural language, within the framework of formal ontologies. We will adopt a framework in which vagueness is captured via numerical restrictions that can be automatically adjusted. Since updating vague concepts, either through ontology alignment or ontology evolution, can lead to inconsistent sets of axioms, we define and implement a method to detecting and repairing such inconsistencies in a local fashion.
CSIRO Australian E-Health Research Centre
CSIRO Australian E-Health Research Centre
CSIRO Australian E-Health Research Centre
Patrick Rodler
Patrick Rodler
Patrick Rodler
Mathieu D'Aquin
Mathieu D'Aquin
Mathieu D'Aquin
School of ITEE, The University of Queensland
School of ITEE, The University of Queensland
School of ITEE, The University of Queensland
Vaibhav Khadilkar
Vaibhav Khadilkar
Vaibhav Khadilkar
DERI, National University of Ireland, Galway
DERI - National University of Ireland, Galway
DERI - National University of Ireland, Galway
DERI, National University of Ireland, Galway
DERI - National University of Ireland, Galway
DERI, National University of Ireland, Galway
Grigoris Antoniou
Grigoris Antoniou
Grigoris Antoniou
Jeff Z. Pan
Jeff Z. Pan
Jeff Z. Pan
Griffith University
Griffith University
Griffith University
Bijan Parsia
Bijan Parsia
Bijan Parsia
Harvard Medical School
Harvard Medical School
Harvard Medical School
Paul Buitelaar
Paul Buitelaar
Paul Buitelaar
Natasha Noy
Natasha Noy
Natasha Noy
Anna Lisa Gentile
Anna Lisa
Gentile
University of Aberdeen
University of Aberdeen
University of Aberdeen
Dimitris Plexousakis
Dimitris Plexousakis
Dimitris Plexousakis
Andrea Giovanni Nuzzolese
Andrea Giovanni
Nuzzolese
Jyothsna Rachapalli
Jyothsna Rachapalli
Jyothsna Rachapalli
Vinay Chaudhri
Vinay Chaudhri
Vinay Chaudhri
Tudor Groza
Tudor Groza
Tudor Groza
Kostyantyn Shchekotykhin
Kostyantyn Shchekotykhin
Kostyantyn Shchekotykhin
Freie Universität Berlin, Networked Information Systems
Freie Universität Berlin, Networked Information Systems
Freie Universität Berlin, Networked Information Systems
Gerhard Friedrich
Gerhard Friedrich
Gerhard Friedrich
Ulrike Sattler
Ulrike Sattler
Ulrike Sattler
Massachusetts General Hospital, Department of Neurology, Boston, MA, USA
Massachusetts General Hospital, Department of Neurology, Boston, MA, USA
Massachusetts General Hospital, Department of Neurology, Boston, MA, USA
Paolo Pareti
Paolo Pareti
Paolo Pareti
Muhammad Javed
Muhammad Javed
Muhammad Javed
Timothy Redmond
Timothy Redmond
Timothy Redmond
Ewan Klein
Ewan Klein
Ewan Klein
Vit Novacek
Vit Novacek
Vit Novacek
SRI International
SRI International
SRI International
Knowledge Media Institute, The Open University
Knowledge Media Institute, The Open University
Knowledge Media Institute, The Open University
Meraka
Meraka
Meraka
UTD
UTD
UTD
University of Edinburgh
University of Edinburgh
University of Edinburgh
Murat Kantarcioglu
Murat Kantarcioglu
Murat Kantarcioglu
European Bioinformatics Institute
European Bioinformatics Institute
European Bioinformatics Institute
Armin Haller
Armin Haller
Armin Haller
Vrije University of Amsterdam
Vrije University of Amsterdam
Vrije University of Amsterdam
Nina Tahmasebi
Nina Tahmasebi
Nina Tahmasebi
CSIRO ICT Canberra
CSIRO ICT Canberra
CSIRO ICT Canberra
Renata Wassermann
Renata Wassermann
Renata Wassermann