4,185 research outputs found
Taking SPARQL 1.1 extensions into account in the SWIP system
International audienceThe SWIP system aims at hiding the complexity of expressing a query in a graph query language such as SPARQL. We propose a mechanism by which a query expressed in natural language is translated into a SPARQL query. Our system analyses the sentence in order to exhibit concepts, instances and relations. Then it generates a query in an internal format called the pivot language. Finally, it selects pre-written query patterns and instantiates them with regard to the keywords of the initial query. These queries are presented by means of explicative natural language sentences among which the user can select the query he/she is actually interested in. We are currently focusing on new kinds of queries which are handled by the new version of our system, which is now based on the 1.1 version of SPARQL
Demo : Swip, a semantic web interface using patterns
International audienceOur purpose is to provide end-users with a means to query ontology based knowledge bases using natural language queries and thus hide the complexity of formulating a query expressed in a graph query language such as SPARQL. The main originality of our approach lies in the use of query patterns. Our contribution is materialized in a system named SWIP, standing for Semantic Web Interface Using Patterns. The demo will present use cases of this system
Natural language query interpretation into SPARQL using patterns
International audienceOur purpose is to provide end-users with a means to query ontology based knowledge bases using natural language queries and thus hide the complexity of formulating a query expressed in a graph query language such as SPARQL. The main originality of our approach lies in the use of query patterns. In this article we justify the postulate supporting our work which claims that queries issued by real life end-users are variations of a few typical query families. We also explain how our approach is designed to be adaptable to different user languages. Evaluations on the QALD-3 data set have shown the relevancy of the approach
SWIP at QALD-3 : results, criticisms and lesson learned
International audienceThis paper presents the results obtained by the SWIP system while participating in the QALD-3 (Question Answering over Linked Data) challenge, co-located with CLEF 2013 (Conference and Labs of the Evaluation Forum). We tackled task 1, multilingual question answering, whose purpose is to interpret natural language questions in order to return the answers contained in a graph knowledge base. We answered queries of both proposed datasets (one concerning DBpedia, the other Musicbrainz) and took into consideration only questions in English. The system SWIP (Semantic Web Interface using Patterns) aims at automatically generating formal queries from user queries expressed in natural language. For this, it relies on the use of query patterns which enable the complex task of interpreting natural language queries. The results obtained on the Musicbrainz dataset (precision = 0,51, recall = 0,51, F-measure = 0,51) are very satisfactory and encouraging. The results on DBpedia (precision = 0,16, recall = 0,15, F-measure = 0,16) are more disappointing. In this paper, we present both the SWIP approach and its implementation. We then present the results of the challenge in more detail and their analysis. Finally we draw some conclusions on the strengths and weaknesses of our approach, and suggest ways to improve its performance
The business process modelling ontology
In this paper we describe the Business Process Modelling Ontology (BPMO), which is part of an approach to modelling business processes at the semantic level, integrating knowledge about the organisational context, workflow activities and Semantic Web Services. We harness knowledge representation and reasoning techniques so that business process workflows can: be exposed and shared through semantic descriptions; refer to semantically annotated data and services; incorporate heterogeneous data though semantic mappings; and be queried using a reasoner or inference engine. In this paper we describe our approach and evaluate BPMO through a use case
AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba
Conceptual graphs, which is a particular type of Knowledge Graphs, play an
essential role in semantic search. Prior conceptual graph construction
approaches typically extract high-frequent, coarse-grained, and time-invariant
concepts from formal texts. In real applications, however, it is necessary to
extract less-frequent, fine-grained, and time-varying conceptual knowledge and
build taxonomy in an evolving manner. In this paper, we introduce an approach
to implementing and deploying the conceptual graph at Alibaba. Specifically, We
propose a framework called AliCG which is capable of a) extracting fine-grained
concepts by a novel bootstrapping with alignment consensus approach, b) mining
long-tail concepts with a novel low-resource phrase mining approach, c)
updating the graph dynamically via a concept distribution estimation method
based on implicit and explicit user behaviors. We have deployed the framework
at Alibaba UC Browser. Extensive offline evaluation as well as online A/B
testing demonstrate the efficacy of our approach.Comment: Accepted by KDD 2021 (Applied Data Science Track
Development of Use Cases, Part I
For determining requirements and constructs appropriate for a Web query language, or in fact
any language, use cases are of essence. The W3C has published two sets of use cases for XML
and RDF query languages. In this article, solutions for these use cases are presented using
Xcerpt. a novel Web and Semantic Web query language that combines access to standard Web
data such as XML documents with access to Semantic Web metadata
such as RDF resource
descriptions with reasoning abilities and rules familiar from logicprogramming.
To the
best knowledge of the authors, this is the first in depth study of how to solve use cases for
accessing XML and RDF in a single language: Integrated access to data and metadata
has been
recognized by industry and academia as one of the key challenges in data processing for the
next decade. This article is a contribution towards addressing this challenge by demonstrating
along practical and recognized use cases the usefulness of reasoning abilities, rules, and
semistructured
query languages for accessing both data (XML) and metadata
(RDF)
Survey over Existing Query and Transformation Languages
A widely acknowledged obstacle for realizing the vision of the Semantic Web is the inability
of many current Semantic Web approaches to cope with data available in such diverging
representation formalisms as XML, RDF, or Topic Maps. A common query language is the first
step to allow transparent access to data in any of these formats. To further the understanding
of the requirements and approaches proposed for query languages in the conventional as well
as the Semantic Web, this report surveys a large number of query languages for accessing
XML, RDF, or Topic Maps. This is the first systematic survey to consider query languages from
all these areas. From the detailed survey of these query languages, a common classification
scheme is derived that is useful for understanding and differentiating languages within and
among all three areas
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