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- 141 creator johan-aberg.
- 141 creator patrick-lambrix.
- 141 creator valentina-ivanova.
- 141 type InProceedings.
- 141 label "Requirements for and Evaluation of User Support for Large-Scale Ontology Alignment".
- 141 sameAs 141.
- 141 abstract "Currently one of the challenges for the ontology alignment community is the user involvement in the alignment process. At the same time, the focus of the community has shifted towards large-scale matching which introduces an additional dimension to this issue. This paper aims to provide a set of requirements that foster the user involvement for large-scale ontology alignment tasks. Further, we present and discuss the results of a literature study for 7 ontology alignments systems as well as a heuristic evaluation and an observational user study for 3 ontology alignment systems to reveal the coverage of the requirements in the systems and the support for the requirements in the user interfaces.".
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- 141 keyword "large-scale ontology alignment".
- 141 keyword "user interfaces".
- 141 keyword "user involvement".
- 141 title "Requirements for and Evaluation of User Support for Large-Scale Ontology Alignment".
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- item-1 hasContent valentina-ivanova.
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- 142 creator annette-ten-teije.
- 142 creator frank-van-harmelen.
- 142 creator hamid-r-bazoobandi.
- 142 creator henri-bal.
- 142 creator jacopo-urbani.
- 142 creator steven-de-rooij.
- 142 type InProceedings.
- 142 label "A Compact In-Memory Dictionary for RDF data".
- 142 sameAs 142.
- 142 abstract "We present a method for a compact in-memory RDF dictionary that supports high frequency updates. Our method leverages the long common prefixes in RDF terms to compress them using a trie data structure. To overcome the memory inefficiency of tries, we present a highly memory efficient implementation which is especially tuned for RDF data. Our approach compacts the dictionary further by unifying the two independent tables that are normally used for encoding and decoding into a single table by mapping each string to a memory location from where it can be reached again instead of counter ID. To motivate our approach, we present empirical data on the amount of repetition that is present in both IRIs and literals of large realistic RDF graphs. An empirical analysis shows that our technique saves 50-59% memory compared to uncompressed conventional dictionaries while still offering comparable encoding/decoding performance.".
- 142 hasAuthorList authorList.
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- 142 keyword "Compact in-memory dictionary".
- 142 keyword "list-trie".
- 142 keyword "trie".
- 142 title "A Compact In-Memory Dictionary for RDF data".
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- 144 creator axel-cyrille-ngonga-ngomo.
- 144 creator christina-unger.
- 144 creator lorenz-buehmann.
- 144 creator ricardo-usbeck.
- 144 type InProceedings.
- 144 label "HAWK - Hybrid Question Answering using Linked Data".
- 144 sameAs 144.
- 144 abstract "The decentral architecture behind the Web has led to pieces of information being distributed across data sources with varying structure. Hence, answering complex questions often required combining information from structured and unstructured data sources. We present HAWK, a novel entity search approach for Hybrid Question Answering based on combining Linked Data and textual data. The approach uses predicate-argument representations of questions to derive equivalent combinations of SPARQL query fragments and text queries. These are executed so as to integrate the results of the text queries into SPARQL and thus generate a formal interpretation of the query. We present a thorough evaluation of the framework, including an analysis of the influence of entity annotation tools on the generation process of the hybrid queries and a study of the overall accuracy of the system. Our results show that HAWK 0.68 respectively 0.61 F-measure within the training respectively test phases on the Question Answering over Linked Data (QALD-4) hybrid query benchmark.".
- 144 hasAuthorList authorList.
- 144 isPartOf proceedings.
- 144 keyword "Entity Search".
- 144 keyword "Hybrid Question Answering".
- 144 keyword "Ranking".
- 144 title "HAWK - Hybrid Question Answering using Linked Data".
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