R2D2: A Dbpedia Chatbot Using Triple-Pattern Like Queries

2008

In this paper we describe a way to access information using chatbot, without the need for sophisticated natural language processing or logical inference. FAQs are Frequently-Asked Questions documents, designed to capture the logical ontology of a given domain. Any Natural Language interface to an FAQ is constrained to reply with the given Answers, so there is no need for NL generation to recreate well-formed answers, or for deep analysis or logical inference to map user input questions onto this logical ontology; simple (but large) set of pattern-template matching rules will suffice. In this paper and as an evidence for this argument, the FAQ in the School of Computing (SoC) at the University of Leeds as well as Perl, Linux, and Python were used to retrain the ALICE chat-bot system, producing FAQchat. The replies from FAQchat looks like results generated by WWW search engines such as AskJeeves or Google. User trials with AskJeeves, Google and FAQchat demonstrate that FAQchat is a viable alternative, and in fact many users prefer it to Google as tool to access FAQ databases. The restricted domain of an FAQ is special case of Question-Answering which does not require the sophisticated analysis techniques.

SPARQL queries are a powerful method for querying the large and increasing number of linked open data repositories available through the Semantic Web. However , generating SPARQL queries can be difficult, even for experts. Interfaces that accept questions in natural language and convert them to SPARQL queries are one solution to this problem. We describe the Linked Open Data Question Answering (LODQA) system. LODQA is developed to generate SPARQL queries from natural language, with the goal of providing an easy-to-use interface to search linked open RDF data. The paper presents a prototype version of LODQA which works on SNOMED CT, discussing the design and implementation, together with the limitations of the current implementation and future directions for improvement.

cstp.umkc.edu, 2004

Abstract: Semantic Web technologies allow for a simplified way to represent machine perishable knowledge over Web and have significant implications in extending Web capabilities beyond our current imagination. However, in order to bring its unprecedented power to Web ...

Advances in Web Intelligence, 2003

We present a dialogue system that enables the access in natural language to a web information retrieval system. We use a Web Semantic Language to model the knowledge conveyed by the texts. In this way we are able to obtain the associated knowledge necessary to perform the different analysis stages of natural language sentences. In the context of information retrieval, we aim to develop a system that, by increasing the interaction management capabilities, is able to achieve a better degree of cooperativeness and to reduce the average number of interactions needed to retrieve the intended set of documents. The documents in the IR system considered here are composed by the set of documents produced by the Portuguese Attorney General since 1940. These documents were analyzed and an ontology describing their structure and content was defined. Then, they were automatically parsed and a (partial) semantic structure was created. The ontology and the semantic content was represented in the OWL language. An example of a user interaction session is presented and explained in detail.

2003

We present a dialogue system that enables the access in natural language to a web law information retrieval system. We use a semantic web language to model the document knowledge and to define an ontology representing the main classes of domain objects, their properties and their relations. Our system includes an ontology in DAML+OIL language describing the documents structure, a document database build with a subset of the Portuguese Attorney General’s Office documents were each document includes a field with its semantic content described in DAML+OIL. The dialogue system interprets the user natural language sentences using the ontology, the documents semantic content and the inferred user attitudes.

IGI Global eBooks, 2018

Question Answering systems that resort to the Semantic Web as a knowledge base can go well beyond the usual matching words in documents and, preferably, find a precise answer, without requiring user help to interpret the documents returned. In this paper, the authors introduce a Dialogue Manager that, through the analysis of the question and the type of expected answer, provides accurate answers to the questions posed in Natural Language. The Dialogue Manager not only represents the semantics of the questions, but also represents the structure of the discourse, including the user intentions and the questions context, adding the ability to deal with multiple answers and providing justified answers. The authors' system performance is evaluated by comparing with similar question answering systems. Although the test suite is slight dimension, the results obtained are very promising.

2013

Cultural heritage appears to be a very useful use case for Semantic Web technologies. The domain provides with plenty of circumstances where linkages between different knowledge sources are required to ensure access to rich information and respond to the needs of professionals dealing with cultural heritage content. Semantic Web technologies offer the technological backbone to meet the requirement of integrating heterogeneous data easily, but they are still more adapted to be consumed by computers than by humans, especially non-engineers or developers. This chapter is about a technique which allows interaction in natural language with semantic knowledge bases. The proposed technique offers a method that allows querying a semantic repository in natural language and obtaining results from it as a coherent text. This unique solution includes several steps of transition from natural language to SPARQL and from RDF to coherent multilingual descriptions, using the Grammatical Framework, G...

2019

Chatbot is a conversational agent that communicates with users based on natural language. It is founded on a question answering system which tries to understand the intent of the user. Several chatbot methods deal with a model based template of question answering. However, these approaches are not able to cope with various questions and can affect the quality of the results. To address this issue, we propose a new semantic question answering approach combining Natural Language Processing (NLP) methods and Semantic Web techniques to analyze user's question and transform it into SPARQL query. An ontology has been developed to represent the domain knowledge of the chatbot. Experimentations show that our approach outperforms state of the art methods.

The paper illustrates the implementation and semantic enhancement of a domain-oriented Question-Answering system based on a pattern-matching chatbot technology, developed within an industrial project, named FRASI. The main difficulty in building a KB for a chatbot is to handwrite all possible question-answer pairs that constitute the KB. The proposed approach simplifies the chatbot realization thanks to two solutions. The first one uses an ontology, which is exploited in a twofold manner: to construct dynamic answers as a result of an inference process about the domain, and to automatically populate, off-line, the chatbot KB with sentences that can be derived from the ontology, describing properties and relations between concepts involved in the dialogue. The second one is to preprocess user sentences, and to reduce them to a simpler structure that can be referred to existing elements of the chatbot KB. The enhanced symbolic reduction of user questions and the automatic population of question templates in the chatbot KB from domain ontology have been implemented as two computational services (external modules).