Matches in ScholarlyData for { ?s ?p ?o. }
- 171 type InProceedings.
- 171 label "Exploiting Information Extraction, Reasoning and Machine Learning for Relation Prediction".
- 171 sameAs 171.
- 171 abstract "The three most common approaches for deriving or predicting instantiated relations, i.e. triple statements (s, p, o), are information extraction, reasoning and relational machine learning. Information extraction uses sensory information, typically in form of text, and extracts statements using various methods ranging from simple classifiers to the most sophisticated NLP approaches. Logical reasoning is based on a set of true statements and derives new statements via inference using higher-order logical axioms. Finally, machine learning exploits regularities in the data to predict the likelihood of new statements. In this paper we combine all three methods to exploit all sources of available information in a modular way, by which we mean that each approach, i.e., information extraction, reasoning, machine learning, can be optimized independently to be combined in an overall system. For relational machine learning, we present a novel approach based on hierarchical Bayesian multi-label learning which also sheds new light on common factorization approaches. We rank the probabilities for statements to be true in the sense that: given that we are forced to make a decision, what is the best option. We consider the fact that an entity can belong to more than one ontological class and discuss aggregation. We extend the approach to modeling nonlinear dependencies between relationships and for personalization. We validate our model using data from the Yago and the DBpedia ontology.".
- 171 hasAuthorList authorList.
- 171 isPartOf proceedings.
- 171 keyword "Multivariate modeling".
- 171 keyword "Probabilistic PCA".
- 171 keyword "Relation prediction".
- 171 title "Exploiting Information Extraction, Reasoning and Machine Learning for Relation Prediction".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasItem item-4.
- authorList hasLastItem item-4.
- item-1 type ListItem.
- item-1 hasContent xueyan-jiang.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent yi-huang.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent maximilian-nickel.
- item-3 next item-4.
- item-3 previous item-2.
- item-4 type ListItem.
- item-4 hasContent volker-tresp.
- item-4 previous item-3.
- 173 creator christophe-gueret.
- 173 creator claus-stadler.
- 173 creator jens-lehmann.
- 173 creator paul-groth.
- 173 type InProceedings.
- 173 label "Assessing Linked Data Mappings using Network Measures".
- 173 sameAs 173.
- 173 abstract "Assessing Linked Data Mappings using Network Measures Christophe Guéret, Paul Groth, Claus Stadler, and Jens Lehmann Linked Data is at its core about the setting of links between resources. Links provide enriched semantics, pointers to extra information and enable the merging of data sets. However, as the amount of Linked Data has grown, there has been the need to automate the creation of links and such automated approaches can create low-quality links or unsuitable network structures. In particular, it is difficult to know whether the links introduced improve or diminish the quality of Linked Data. In this paper, we present an extensible framework and a series of justified network measures that allow for the assessment of Linked Data mappings. We test the framework on a set of known good and bad links generated by common mapping systems and discuss which network measures show promise for use in assessment.".
- 173 hasAuthorList authorList.
- 173 isPartOf proceedings.
- 173 keyword "linked data".
- 173 keyword "network analysis".
- 173 keyword "quality assurance".
- 173 title "Assessing Linked Data Mappings using Network Measures".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasItem item-4.
- authorList hasLastItem item-4.
- item-1 type ListItem.
- item-1 hasContent christophe-gueret.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent paul-groth.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent claus-stadler.
- item-3 next item-4.
- item-3 previous item-2.
- item-4 type ListItem.
- item-4 hasContent jens-lehmann.
- item-4 previous item-3.
- 174 creator melike-sah.
- 174 creator vincent-wade.
- 174 type InProceedings.
- 174 label "A Novel Concept-based Search for the Web of Data using UMBEL and a Fuzzy Retrieval Model".
- 174 sameAs 174.
- 174 abstract "As the size of Linked Open Data (LOD) increases, the search and access to the relevant LOD resources becomes more challenging. To overcome search difficulties, we propose a novel concept-based search mechanism for the Web of Data (WoD) based on UMBEL concept hierarchy and fuzzy-based retrieval model. The proposed search mechanism groups LOD resources with the same concepts to form categories, which is called concept lenses, for more efficient access to the WoD. To achieve concept-based search, we use UMBEL concept hierarchy for representing context of LOD resources. A semantic indexing model is applied for efficient representation of UMBEL concept descriptions and a novel fuzzy-based retrieval model is introduced for categorization of LOD resources to UMBEL concepts. The proposed fuzzy-based model was evaluated on a particular benchmark (~10,000 mappings). These evaluation results show that we can achieve highly acceptable categorization accuracy and perform better than the vector space model.".
- 174 hasAuthorList authorList.
- 174 isPartOf proceedings.
- 174 keyword "UMBEL concept hierarchy".
- 174 keyword "categorization".
- 174 keyword "concept-based search".
- 174 keyword "data mining".
- 174 keyword "fuzzy retrieval model".
- 174 keyword "linked open data".
- 174 keyword "semantic indexing".
- 174 title "A Novel Concept-based Search for the Web of Data using UMBEL and a Fuzzy Retrieval Model".
- 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 melike-sah.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent vincent-wade.
- item-2 previous item-1.
- 176 creator antoine-isaac.
- 176 creator guus-schreiber.
- 176 creator jacco-van-ossenbruggen.
- 176 creator jan-wielemaker.
- 176 creator judith-van-gent.
- 176 creator michiel-hildebrand.
- 176 creator viktor-de-boer.