PUMA publications for /user/jaeschke/schemahttps://puma.uni-kassel.de/user/jaeschke/schemaPUMA RSS feed for /user/jaeschke/schema2024-03-19T14:55:54+01:00Pay-as-you-go user feedback for dataspace systemshttps://puma.uni-kassel.de/bibtex/23bff24fb9eb1e39fa97a524aabb8dee9/jaeschkejaeschke2012-10-02T09:10:21+02:00collective computing entity feedback human intelligence linking matching schema semantic social web <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Shawn R. Jeffery" itemprop="url" href="/author/Shawn%20R.%20Jeffery"><span itemprop="name">S. Jeffery</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Michael J. Franklin" itemprop="url" href="/author/Michael%20J.%20Franklin"><span itemprop="name">M. Franklin</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Alon Y. Halevy" itemprop="url" href="/author/Alon%20Y.%20Halevy"><span itemprop="name">A. Halevy</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 2008 ACM SIGMOD international conference on Management of data</span>, </em></span><em>Seite <span itemprop="pagination">847--860</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2008<meta content="2008" itemprop="datePublished"/></span></em>)Tue Oct 02 09:10:21 CEST 2012New York, NY, USAProceedings of the 2008 ACM SIGMOD international conference on Management of data847--860Pay-as-you-go user feedback for dataspace systems2008collective computing entity feedback human intelligence linking matching schema semantic social web A primary challenge to large-scale data integration is creating semantic equivalences between elements from different data sources that correspond to the same real-world entity or concept. Dataspaces propose a pay-as-you-go approach: automated mechanisms such as schema matching and reference reconciliation provide initial correspondences, termed <i>candidate matches</i>, and then user feedback is used to incrementally confirm these matches. The key to this approach is to determine in what order to solicit user feedback for confirming candidate matches.</p> <p>In this paper, we develop a decision-theoretic framework for ordering candidate matches for user confirmation using the concept of the <i>value of perfect information (VPI)</i>. At the core of this concept is a <i>utility function</i> that quantifies the desirability of a given state; thus, we devise a utility function for dataspaces based on query result quality. We show in practice how to efficiently apply VPI in concert with this utility function to order user confirmations. A detailed experimental evaluation on both real and synthetic datasets shows that the ordering of user feedback produced by this VPI-based approach yields a dataspace with a significantly higher utility than a wide range of other ordering strategies. Finally, we outline the design of Roomba, a system that utilizes this decision-theoretic framework to guide a dataspace in soliciting user feedback in a pay-as-you-go manner.Ontology Evolution: Not the Same as Schema Evolutionhttps://puma.uni-kassel.de/bibtex/208ee0381e240c3ee414e0eefc7fe1a83/jaeschkejaeschke2012-09-05T11:09:29+02:00database evolution ontology schema semantic web <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Natalya F. Noy" itemprop="url" href="/author/Natalya%20F.%20Noy"><span itemprop="name">N. Noy</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Michel Klein" itemprop="url" href="/author/Michel%20Klein"><span itemprop="name">M. Klein</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Knowledge and Information Systems</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">6 </span></span>(<span itemprop="issueNumber">4</span>):
<span itemprop="pagination">428--440</span></em> </span>(<em><span>2004<meta content="2004" itemprop="datePublished"/></span></em>)Wed Sep 05 11:09:29 CEST 2012LondonKnowledge and Information Systems4428--440Ontology Evolution: Not the Same as Schema Evolution62004database evolution ontology schema semantic web As ontology development becomes a more ubiquitous and collaborative process, ontology versioning and evolution becomes an important area of ontology research. The many similarities between database-schema evolution and ontology evolution will allow us to build on the extensive research in schema evolution. However, there are also important differences between database schemas and ontologies. The differences stem from different usage paradigms, the presence of explicit semantics and different knowledge models. A lot of problems that existed only in theory in database research come to the forefront as practical problems in ontology evolution. These differences have important implications for the development of ontology-evolution frameworks: The traditional distinction between versioning and evolution is not applicable to ontologies. There are several dimensions along which compatibility between versions must be considered. The set of change operations for ontologies is different. We must develop automatic techniques for finding similarities and differences between versions.Pushing the boundaries of crowd-enabled databases with query-driven schema expansionhttps://puma.uni-kassel.de/bibtex/241224a60badfeefb0fe2cea85f2a4ff0/jaeschkejaeschke2012-06-12T15:23:55+02:00cirg collective computing crowdsourcing database expansion human intelligence schema social <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Joachim Selke" itemprop="url" href="/author/Joachim%20Selke"><span itemprop="name">J. Selke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christoph Lofi" itemprop="url" href="/author/Christoph%20Lofi"><span itemprop="name">C. Lofi</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Wolf-Tilo Balke" itemprop="url" href="/author/Wolf-Tilo%20Balke"><span itemprop="name">W. Balke</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Proceedings of the VLDB Endowment</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">5 </span></span>(<span itemprop="issueNumber">6</span>):
<span itemprop="pagination">538--549</span></em> </span>(<em><span>Februar 2012<meta content="Februar 2012" itemprop="datePublished"/></span></em>)Tue Jun 12 15:23:55 CEST 2012Proceedings of the VLDB Endowmentfeb6538--549Pushing the boundaries of crowd-enabled databases with query-driven schema expansion52012cirg collective computing crowdsourcing database expansion human intelligence schema social By incorporating human workers into the query execution process crowd-enabled databases facilitate intelligent, social capabilities like completing missing data at query time or performing cognitive operators. But despite all their flexibility, crowd-enabled databases still maintain rigid schemas. In this paper, we extend crowd-enabled databases by flexible query-driven schema expansion, allowing the addition of new attributes to the database at query time. However, the number of crowd-sourced mini-tasks to fill in missing values may often be prohibitively large and the resulting data quality is doubtful. Instead of simple crowd-sourcing to obtain all values individually, we leverage the usergenerated data found in the Social Web: By exploiting user ratings we build <i>perceptual spaces</i>, i.e., highly-compressed representations of opinions, impressions, and perceptions of large numbers of users. Using few training samples obtained by expert crowd sourcing, we then can extract all missing data automatically from the perceptual space with high quality and at low costs. Extensive experiments show that our approach can boost both performance and quality of crowd-enabled databases, while also providing the flexibility to expand schemas in a query-driven fashion.