R2D2: A Dbpedia Chatbot Using Triple-Pattern Like Queries

Chatbots, also known as conversation agents, are programs that are able to simulate and reproduce an intelligent conversation with humans. Although this type of program is not new, the explosion of the available information and the rapid increase of the users seeking this information have renewed the interest in their development. In this paper, we present R2D2, an intelligent chatbot relying on semantic web technologies and offering an intelligent controlled natural language interface for accessing the information available in DBpedia. The chatbot accepts structured input, allowing users to enter triple-pattern like queries, which are answered by the underlying engine. While typing, an auto-complete service guides users on creating the triple patterns, suggesting resources available in the DBpedia. Based on user input (in the form of triple-pattern like queries), the corresponding SPARQL queries are automatically formulated. The queries are submitted to the corresponding DBpedia SPARQL endpoint, and then the result is received by R2D2 and augmented with maps and visuals and eventually presented to the user. The usability evaluation performed shows the advantages of our solution and its usefulness.

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A semantic-based approach for querying linked data using natural language

Journal of Information Science ◽

10.1177/0165551515616311 ◽

2016 ◽

Vol 42 (6) ◽

pp. 851-862 ◽

Cited By ~ 9

Author(s):

Mario Andrés Paredes-Valverde ◽

Rafael Valencia-García ◽

Miguel Ángel Rodríguez-García ◽

Ricardo Colomo-Palacios ◽

Giner Alor-Hernández

Keyword(s):

Semantic Web ◽

Natural Language ◽

Knowledge Bases ◽

Semantic Knowledge ◽

Semantic Web Technologies ◽

Web Technologies ◽

Natural Language Interface ◽

High Quality Information ◽

Domain Independent

The semantic Web aims to provide to Web information with a well-defined meaning and make it understandable not only by humans but also by computers, thus allowing the automation, integration and reuse of high-quality information across different applications. However, current information retrieval mechanisms for semantic knowledge bases are intended to be only used by expert users. In this work, we propose a natural language interface that allows non-expert users the access to this kind of information through formulating queries in natural language. The present approach uses a domain-independent ontology model to represent the question’s structure and context. Also, this model allows determination of the answer type expected by the user based on a proposed question classification. To prove the effectiveness of our approach, we have conducted an evaluation in the music domain using LinkedBrainz, an effort to provide the MusicBrainz information as structured data on the Web by means of Semantic Web technologies. Our proposal obtained encouraging results based on the F-measure metric, ranging from 0.74 to 0.82 for a corpus of questions generated by a group of real-world end users.

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SEMCL

International Journal of Knowledge and Systems Science ◽

10.4018/jkss.2010070101 ◽

2010 ◽

Vol 1 (3) ◽

pp. 1-19 ◽

Cited By ~ 2

Author(s):

Weisen Guo ◽

Steven B. Kraines

Keyword(s):

Semantic Web ◽

Natural Language ◽

Knowledge Sharing ◽

Language Processing ◽

Global Knowledge ◽

Semantic Web Technologies ◽

Natural Languages ◽

Web Technologies ◽

Language Knowledge ◽

Cross Language

To promote global knowledge sharing, one should solve the problem that knowledge representation in diverse natural languages restricts knowledge sharing effectively. Traditional knowledge sharing models are based on natural language processing (NLP) technologies. The ambiguity of natural language is a problem for NLP; however, semantic web technologies can circumvent the problem by enabling human authors to specify meaning in a computer-interpretable form. In this paper, the authors propose a cross-language semantic model (SEMCL) for knowledge sharing, which uses semantic web technologies to provide a potential solution to the problem of ambiguity. Also, this model can match knowledge descriptions in diverse languages. First, the methods used to support searches at the semantic predicate level are given, and the authors present a cross-language approach. Finally, an implementation of the model for the general engineering domain is discussed, and a scenario describing how the model implementation handles semantic cross-language knowledge sharing is given.

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Creating RESTful APIs over SPARQL endpoints using RAMOSE

Semantic Web ◽

10.3233/sw-210439 ◽

2021 ◽

pp. 1-19

Author(s):

Marilena Daquino ◽

Ivan Heibi ◽

Silvio Peroni ◽

David Shotton

Keyword(s):

Semantic Web ◽

Service Providers ◽

Query Language ◽

Sparql Query ◽

Direct Access ◽

Technical Solution ◽

Semantic Web Technologies ◽

Web Technologies ◽

Sparql Endpoint ◽

Rdf Data

Semantic Web technologies are widely used for storing RDF data and making them available on the Web through SPARQL endpoints, queryable using the SPARQL query language. While the use of SPARQL endpoints is strongly supported by Semantic Web experts, it hinders broader use of RDF data by common Web users, engineers and developers unfamiliar with Semantic Web technologies, who normally rely on Web RESTful APIs for querying Web-available data and creating applications over them. To solve this problem, we have developed RAMOSE, a generic tool developed in Python to create REST APIs over SPARQL endpoints. Through the creation of source-specific textual configuration files, RAMOSE enables the querying of SPARQL endpoints via simple Web RESTful API calls that return either JSON or CSV-formatted data, thus hiding all the intrinsic complexities of SPARQL and RDF from common Web users. We provide evidence that the use of RAMOSE to provide REST API access to RDF data within OpenCitations triplestores is beneficial in terms of the number of queries made by external users of such RDF data using the RAMOSE API, compared with the direct access via the SPARQL endpoint. Our findings show the importance for suppliers of RDF data of having an alternative API access service, which enables its use by those with no (or little) experience in Semantic Web technologies and the SPARQL query language. RAMOSE can be used both to query any SPARQL endpoint and to query any other Web API, and thus it represents an easy generic technical solution for service providers who wish to create an API service to access Linked Data stored as RDF in a triplestore.

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SEMCL

Systems Approaches to Knowledge Management, Transfer, and Resource Development ◽

10.4018/978-1-4666-1782-7.ch011 ◽

2013 ◽

pp. 168-187

Author(s):

Weisen Guo ◽

Steven B. Kraines

Keyword(s):

Semantic Web ◽

Natural Language ◽

Knowledge Sharing ◽

Language Processing ◽

Global Knowledge ◽

Semantic Web Technologies ◽

Natural Languages ◽

Web Technologies ◽

Language Knowledge ◽

Cross Language

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Towards Verbalizing SPARQL Queries in Arabic

Engineering, Technology & Applied Science Research ◽

10.48084/etasr.630 ◽

2016 ◽

Vol 6 (2) ◽

pp. 937-944

Author(s):

I. Al Agha ◽

O. El-Radie

Keyword(s):

Semantic Web ◽

Natural Language ◽

Query Language ◽

Arabic Language ◽

Syntactic Analysis ◽

Information Need ◽

Translation Process ◽

Semantic Web Technologies ◽

Web Technologies ◽

Different Characteristics

With the wide spread of Open Linked Data and Semantic Web technologies, a larger amount of data has been published on the Web in the RDF and OWL formats. This data can be queried using SPARQL, the Semantic Web Query Language. SPARQL cannot be understood by ordinary users and is not directly accessible to humans, and thus they will not be able to check whether the retrieved answers truly correspond to the intended information need. Driven by this challenge, natural language generation from SPARQL data has recently attracted a considerable attention. However, most existing solutions to verbalize SPARQL in natural language focused on English and Latin-based languages. Little effort has been made on the Arabic language which has different characteristics and morphology. This work aims to particularly help Arab users to perceive SPARQL queries on the Semantic Web by translating SPARQL to Arabic. It proposes an approach that gets a SPARQL query as an input and generates a query expressed in Arabic as an output. The translation process combines both morpho-syntactic analysis and language dependencies to generate a legible and understandable Arabic query. The approach was preliminary assessed with a sample query set, and results indicated that 75% of the queries were correctly translated into Arabic.

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