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- 174 creator ankit-sharma.
- 174 creator jens-lehmann.
- 174 creator konrad-hoffner.
- 174 creator mohnish-dubey.
- 174 creator sourish-dasgupta.
- 174 type InProceedings.
- 174 label "AskNow: A Framework for Natural Language Query Formalization in SPARQL".
- 174 sameAs 174.
- 174 abstract "Natural Language Query Formalization involves semantically parsing queries in natural language and translating them into their corresponding formal representations. It is a key component for developing question-answering (QA) systems on RDF data. The chosen formal representation language in this case is often SPARQL. In this paper, we propose a novel framework, called AskNow, where users can pose queries in English to a target RDF knowledge base (e.g. DBpedia), which are first normalized into an intermediary canonical syntactic form, called Normalized Query Structure (NQS), and then translated into SPARQL queries. NQS facilitates the identification of the desire (or expected output information) and the user-provided input information, and establishing their mutual semantic relationship. At the same time, it is sufficiently adaptive to query paraphrasing. We have empirically evaluated the framework with respect to the syntactic robustness of NQS and semantic accuracy of the SPARQL translator on standard benchmark datasets.".
- 174 hasAuthorList authorList.
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- 174 title "AskNow: A Framework for Natural Language Query Formalization in SPARQL".
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- item-1 hasContent mohnish-dubey.
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- 177 creator alessio-palmero-aprosio.
- 177 creator francesco-corcoglioniti.
- 177 creator marco-rospocher.
- 177 creator mauro-dragoni.
- 177 type InProceedings.
- 177 label "Multi-layered Linked Open Data Enrichment for Information Retrieval".
- 177 sameAs 177.
- 177 abstract "Document retrieval is the task of returning relevant textual resources for a given user query. In this paper, we investigate whether the semantic analysis of the query and the documents, obtained exploiting state-of-the-art Natural Language Processing techniques (e.g., Entity Linking, Frame Detection) and Semantic Web resources (e.g., Yago, DBpedia), can improve the performances of the traditional term-based similarity approach. Our experiments, conducted on a recently released document collection, show that Mean Average Precision (MAP) increases of around 4 percentage points when combining textual and semantic analysis, thus suggesting that semantic content can effectively improve the performances of Information Retrieval systems.".
- 177 hasAuthorList authorList.
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- 177 title "Multi-layered Linked Open Data Enrichment for Information Retrieval".
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- item-1 hasContent francesco-corcoglioniti.
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- 179 creator diego-peroni.
- 179 creator emanuela-carlino.
- 179 creator gloria-re-calegari.
- 179 creator irene-celino.
- 179 type InProceedings.
- 179 label "Supporting Geo-Ontology Engineering through Spatial Data Analytics".
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- 179 abstract "Geo-ontologies are becoming first-class artifacts in spatial data management because of their ability to represent places and points of interest. Several general-purpose geo-ontologies are available and widely employed to describe spatial entities across the world. The cultural, contextual and geographic differences between locations, however, call for more specialized and spatially-customized geo-ontologies. In order to help ontology engineers in (re)engineering geo-ontologies, spatial data analytics can provide interesting insights on territorial characteristics, thus revealing peculiarities and diversities between places. In this paper we propose a set of spatial analytics methods and tools to evaluate existing instances of a general-purpose geo-ontology within two distinct urban environments, in order to support ontology engineers in two tasks: (1) the identification of possible location-specific ontology restructuring activities, like specializations or extensions, and (2) the specification of new potential concepts to formalize neighborhood semantic models. We apply the proposed approach to datasets related to the cities of Milano and London extracted from LinkedGeoData, we present the experimental results and we discuss their value to assist geo-ontology engineering.".
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- 179 title "Supporting Geo-Ontology Engineering through Spatial Data Analytics".
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