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- item-2 hasContent jeff-z-pan.
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- paper-34 type InProceedings.
- paper-34 label "Hitting the Sweetspot: Economic Rewriting of Knowledge Bases".
- paper-34 sameAs paper-34.
- paper-34 abstract "Three conflicting requirements arise in the context of knowledge base (KB) extraction: the size of the extracted KB, the size of the corresponding signature and the syntactic similarity of the extracted KB with the original one. Minimal module extraction and uniform interpolation assign an absolute priority to one of these requirements, thereby limiting the possibilities to influence the other two. We propose a novel technique for EL that does not require such an extreme prioritization. We propose a tractable rewriting approach and empirically compare the technique with existing approaches with encouraging results.".
- paper-34 hasAuthorList authorList.
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- paper-34 title "Hitting the Sweetspot: Economic Rewriting of Knowledge Bases".
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- item-2 hasContent birte-glimm.
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- paper-35 type InProceedings.
- paper-35 label "Performance Heterogeneity and Approximate Reasoning in Description Logic Ontologies".
- paper-35 sameAs paper-35.
- paper-35 abstract "Due to the high worst case complexity of the core reasoning problem for the expressive profiles of OWL 2, ontology engineers are often surprised and confused by the performance behaviour of reasoners on their ontologies. Even very experienced modellers with a sophisticated grasp of reasoning algorithms do not have a good mental model of reasoner performance behaviour. Seemingly innocuous changes to an OWL ontology can degrade classification time from instantaneous to too long to wait for. Similarly, switching reasoners (e.g., to take advantage of specific features) can result in wildly different classification times. In this paper we investigate performance variability phenomena in OWL ontologies, and present methods to identify subsets of an ontology which are performance-degrading for a given reasoner. When such (ideally small) subsets are removed from an ontology, and the remainder is much easier for the given reasoner to reason over, we designate them âhot spotsâ?. The identification of these hot spots allows users to isolate difficult portions of the ontology in a principled and systematic way. Moreover, we devise and compare various methods for approximate reasoning and knowledge compilation based on hot spots. We verify our techniques with a select set of varyingly difficult ontologies from the NCBO BioPortal, and were able to, firstly, successfully identify performance hot spots against the major freely available DL reasoners, and, secondly, significantly improve classification time using approximate reasoning based on hot spots.".
- paper-35 hasAuthorList authorList.
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- paper-35 title "Performance Heterogeneity and Approximate Reasoning in Description Logic Ontologies".
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- item-1 hasContent rafael-s-goncalves.
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- item-2 hasContent bijan-parsia.
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- item-3 hasContent ulrike-sattler.
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- paper-36 type InProceedings.
- paper-36 label "Predicting Reasoning Performance Using Ontology Metrics".
- paper-36 sameAs paper-36.
- paper-36 abstract "A key issue in semantic reasoning is the computational complexity of inference tasks on expressive ontology languages such as OWL DL and OWL 2 DL. Theoretical works have established worst-case complexity results for reasoning tasks for these languages. However, hardness of reasoning about individual ontologies has not been adequately characterised. In this paper, we conduct a systematic study to tackle this problem using machine learning techniques, covering over 350 real-world ontologies and four state-of-the-art, widely-used OWL 2 reasoners. Our main contributions are two-fold. Firstly, we learn various classifiers that accurately predict classification time for an ontology based on its metric values. Secondly, we identify a number of metrics that can be used to effectively predict reasoning performance. Our prediction models have been shown to be highly effective, achieving an accuracy of over 80%.".
- paper-36 hasAuthorList authorList.
- paper-36 isPartOf proceedings-2.
- paper-36 title "Predicting Reasoning Performance Using Ontology Metrics".
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- item-1 hasContent yong-bin-kang.
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- item-2 hasContent yuan-fang-li.
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- item-3 hasContent shonali-krishnaswamy.
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- paper-37 type InProceedings.
- paper-37 label "An Evidence-based Verification Approach to Extract Entities and Relations for Knowledge Base Population".
- paper-37 sameAs paper-37.
- paper-37 abstract "This paper presents an approach to automatically extract entities and relationships from textual documents. The main goal is to populate a knowledge base that hosts this structured information about domain entities. The extracted entities and their expected relationships are verified using two evidence based techniques: classification and linking. This last process also enables the linking of our knowledge base to other sources which are part of the Linked Open Data cloud. We demonstrate the benefit of our approach through series of experiments with real-world datasets.".
- paper-37 hasAuthorList authorList.
- paper-37 isPartOf proceedings-2.
- paper-37 title "An Evidence-based Verification Approach to Extract Entities and Relations for Knowledge Base Population".
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- item-1 hasContent naimdjon-takhirov.
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- item-2 hasContent fabien-duchateau.
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- item-3 hasContent trond-aalberg.
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- paper-38 type InProceedings.
- paper-38 label "Large-Scale Learning of Relation-Extraction Rules with Distant Supervision from the Web".
- paper-38 sameAs paper-38.
- paper-38 abstract "We present a large-scale relation extraction (RE) system which learns grammar-based RE rules from the Web by utilizing large numbers of relation instances as seed. Our goal is to obtain rule sets large enough to cover the actual range of linguistic variation, thus tackling the long-tail problem of real-world applications. A variant of distant supervision learns several relations in parallel, enabling a new method of rule filtering. The system detects both binary and n-ary relations. We target 39 relations from Freebase, for which 3M sentences extracted from 20M web pages serve as the basis for learning an average of 40K distinctive rules per relation. Employing an efficient dependency parser, the average run time for each relation is only 19 hours. We compare these rules with ones learned from local corpora of different sizes and demonstrate that the Web is indeed needed for a good coverage of linguistic variation.".
- paper-38 hasAuthorList authorList.
- paper-38 isPartOf proceedings-2.
- paper-38 title "Large-Scale Learning of Relation-Extraction Rules with Distant Supervision from the Web".
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