Matches in ScholarlyData for { ?s ?p ?o. }
- item-7 hasContent marco-luca-sbodio.
- item-7 next item-8.
- item-7 previous item-6.
- item-8 type ListItem.
- item-8 hasContent veli-bicer.
- item-8 next item-9.
- item-8 previous item-7.
- item-9 type ListItem.
- item-9 hasContent anastasios-kementsietsidis.
- item-9 previous item-8.
- paper-72 type InProceedings.
- paper-72 label "The Benefits of Incremental Reasoning in OWL EL".
- paper-72 sameAs paper-72.
- paper-72 abstract "This demo will present the advantages of the new, bookkeeping-free method for incremental reasoning in OWL EL on incremental classification of large ontologies. In particular, we will show how a typical experience of a user editing a large ontology can be improved if the reasoner (or ontology IDE) provides the capability of instantaneously re-classifying the ontology in the background mode when a change is made. In addition, we intend to demonstrate how incremental reasoning helps in other tasks such as answering DL queries and computing explanations of entailments. We will use our OWL EL reasoner ELK and its Protege plug-in as the main tools to highlight these benefits.".
- paper-72 hasAuthorList authorList.
- paper-72 isPartOf poster-demo-proceedings.
- paper-72 title "The Benefits of Incremental Reasoning in OWL EL".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasLastItem item-2.
- item-1 type ListItem.
- item-1 hasContent yevgeny-kazakov.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent pavel-klinov.
- item-2 previous item-1.
- paper-01 type InProceedings.
- paper-01 label "TRM – Learning Dependencies between Text and Structure with Topical Relational Models".
- paper-01 sameAs paper-01.
- paper-01 abstract "Text-rich structured data become more and more ubiquitous on the Web and on the enterprise databases by encoding heterogeneous structural information between entities such as people, locations, or organizations and the associated textual information. For analyzing this type of data, existing topic modeling approaches, which are highly tailored toward document collections, require manually-defined regularization terms to exploit and to bias the topic learning towards structure information. We propose an approach, called Topical Relational Model, as a principled approach for automatically learning topics from both textual and structure information. Using a topic model, we can show that our approach is effective in exploiting heterogeneous structure information, outperforming a state-of-the-art approach that requires manually-tuned regularization.".
- paper-01 hasAuthorList authorList.
- paper-01 isPartOf proceedings-1.
- paper-01 title "TRM – Learning Dependencies between Text and Structure with Topical Relational Models".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasLastItem item-3.
- item-1 type ListItem.
- item-1 hasContent veli-bicer.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent thanh-tran.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent yongtao-ma.
- item-3 previous item-2.
- paper-02 type InProceedings.
- paper-02 label "A Confidentiality Model for Ontologies".
- paper-02 sameAs paper-02.
- paper-02 abstract "We illustrate several novel attacks to the confidentiality of knowledge bases (KB). Then we introduce a new confidentiality model, sensitive enough to detect those attacks, and a method for constructing secure KB views. We identify safe approximations of the background knowledge exploited in the attacks; they can be used to reduce the complexity of constructing secure KB views.".
- paper-02 hasAuthorList authorList.
- paper-02 isPartOf proceedings-1.
- paper-02 title "A Confidentiality Model for Ontologies".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasLastItem item-2.
- item-1 type ListItem.
- item-1 hasContent piero-bonatti.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent luigi-sauro.
- item-2 previous item-1.
- paper-03 type InProceedings.
- paper-03 label "Pattern Based Knowledge Base Enrichment".
- paper-03 sameAs paper-03.
- paper-03 abstract "Although an increasing number of RDF knowledge bases are published, many of those consist primarily of instance data and lack sophisticated schemata. Having such schemata allows more powerful querying, consistency checking and debugging as well as improved inference. One of the reasons why schemata are still rare is the effort required to create them. In this article, we propose a semi-automatic schemata construction approach addressing this problem: First, the frequency of axiom patterns in existing knowledge bases is discovered. Afterwards, those patterns are converted to SPARQL based pattern detection algorithms, which allow to enrich knowledge base schemata. We argue that we present the first scalable knowledge base enrichment approach based on real schema usage patterns. The approach is evaluated on a large set of knowledge bases with a quantitative and qualitative result analysis.".
- paper-03 hasAuthorList authorList.
- paper-03 isPartOf proceedings-1.
- paper-03 title "Pattern Based Knowledge Base Enrichment".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasLastItem item-2.
- item-1 type ListItem.
- item-1 hasContent lorenz-buehmann.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent jens-lehmann.
- item-2 previous item-1.
- paper-04 type InProceedings.
- paper-04 label "Controlled Query Evaluation over OWL 2 RL Ontologies".
- paper-04 sameAs paper-04.
- paper-04 abstract "We study confidentiality enforcement in ontology-based information systems where ontologies are expressed in OWL 2 RL, a profile of OWL 2 that is becoming increasingly popular in Semantic Web applications. We formalise a natural adaptation of the Controlled Query Evaluation (CQE) framework to ontologies. Our goal is to provide CQE algorithms that (i) ensure confidentiality of sensitive information; (ii) are efficiently implementable by means of RDF triple store technologies; and (iii) ensure maximality of the answers returned by the system to user queries (thus restricting access to information as little as possible). We formally show that these requirements are in conflict and cannot be satisfied without imposing restrictions on ontologies. We propose a fragment of OWL 2 RL for which all three requirements can be satisfied. For the identified fragment, we design a CQE algorithm that has the same computational complexity as standard query answering and can be implemented by relying on state-of-the-art triple stores.".
- paper-04 hasAuthorList authorList.
- paper-04 isPartOf proceedings-1.
- paper-04 title "Controlled Query Evaluation over OWL 2 RL Ontologies".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasItem item-4.