A survey on semantic question answering systems (original) (raw)
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Semantic Question Answering System over Linked Data using Relational Patterns
Question answering is the task of answering questions in natural language. Linked Data project and Semantic Web community made it possible for us to query structured knowledge bases like DBpedia and YAGO. Only expert users, however, with the knowledge of RDF and ontology definitions can build correct SPARQL queries for querying knowledge bases formally. In this paper, we present a method for mapping natural language questions to ontology-based structured queries to retrieve direct answers from open knowledge bases (linked data). Our tool is based on translating natural language questions into RDF triple patterns using the dependency tree of the question text. In addition, our method uses relational patterns extracted from the Web. We tested our tool using questions from QALD-2, Question Answering over Linked Data challenge track and found promising preliminary results.
Question Answering System over Linked Data: A Detailed Survey
ABC Research Alert
As the amount of information in the world is growing very quickly, in the case of the semantic web this increasing amount of information is becoming more difficult to find and manage the exact answers to our various questions. To overcome these difficulties some systems have been developed that make it work for us. But there exists many challenges in developing these systems that require a lot of improvement. In this tutorial, we give a basic understanding of Semantic web, RDF triple, SPARQL query language. Here we will discuss the main obstacles for the QA system in processing the questions and a detailed survey of the existing systems. We also provide some advantages and disadvantages of existing QA systems. We also discuss the evaluation campaigns of the existing models based on their precision, recall and F-1 scores on the QALD dataset.
ACM SIGWEB Newsletter, 2014
The architectural choices underlying Linked Data have led to a compendium of data sources which contain both duplicated and fragmented information on a large number of domains. One way to enable non-experts users to access this data compendium is to provide keyword search frameworks that can capitalize on the inherent characteristics of Linked Data. Developing such systems is challenging for three main reasons. First, resources across different datasets or even within the same dataset can be homonyms. Second, different datasets employ heterogeneous schemas and each one may only contain a part of the answer for a certain user query. Finally, constructing a federated formal query from keywords across different datasets requires exploiting links between the different datasets on both the schema and instance levels. We present Sina, a scalable keyword search system that can answer user queries by transforming user-supplied keywords or natural-languages queries into conjunctive SPARQL queries over a set of interlinked data sources. Sina uses a hidden Markov model to determine the most suitable resources for a user-supplied query from different datasets. Moreover, our framework is able to construct federated queries by using the disambiguated resources and leveraging the link structure underlying the datasets to query. We evaluate Sina over three different datasets. We can answer 25 queries from the QALD-1 correctly. Moreover, we perform as well as the best question answering system from the QALD-3 competition by answering 32 questions correctly while also being able to answer queries on distributed sources. We study the runtime of SINA in its mono-core and parallel implementations and draw preliminary conclusions on the scalability of keyword search on Linked Data.
Question answering on the semantic web
2004
Abstract Question answering on the Web is moving beyond the stage where users simply type a query and retrieve a ranked ordering of appropriate Web pages. Users and analysts want targeted answers to their questions without extraneous information. These answers might contain information from current and authoritative sources, terms with the same meaning as those used in the query, relevant links such as justifications, follow-up questions fitting the context, and provenance information.
ScoQAS: A Semantic-based Closed and Open Domain Question Answering System
Sociedad Española para el Procesamiento del Lenguaje Natural (SEPLN), 2017
Question Answering (QA) has reappeared in research activities and in companies over the past years. We present an architecture of Semantic-based closed and open domain Question Answering System (ScoQAS) over ontology resources (not free text) with two different prototyping: Ontology-based closed domain and an open domain under Linked Open Data (LOD) resource. Both scenarios are presented , discussed and evaluated. Keywords: Semantic question answering, natural language processing (NLP), on-tology, linked open data (LOD), linked data (LD) Resumen: La búsqueda de la respuesta ha reaparecido con fuerza en losúltimoslos´losúltimos años, tanto a nivel industrial como académico. Presentamos una arquitectura de búsqueda de respuesta, ScoQAS, basada en la semántica aplicable tanto a dominio cerrado (definido por una ontología) como a dominio abierto, dirigido a repositarios de Linked Open Data (LOD). Los dos se presentan, discuten y son evaluados. Palabras clave: Respuesta de pregunta semántica, procesamiento del lenguaje natural (PNL), ontología, linked open data (LOD), linked data (LD)
A Semantic Question Answering Framework for Large Data Sets
Open J. Semantic Web, 2016
Traditionally, the task of answering natural language questions has involved a keyword-based document retrieval step, followed by in-depth processing of candidate answer documents and paragraphs. This post-processing uses semantics to various degrees. In this article, we describe a purely semantic question answering (QA) framework for large document collections. Our high-precision approach transforms the semantic knowledge extracted from natural language texts into a language-agnostic RDF representation and indexes it into a scalable triplestore. In order to facilitate easy access to the information stored in the RDF semantic index, a user's natural language questions are translated into SPARQL queries that return precise answers back to the user. The robustness of this framework is ensured by the natural language reasoning performed on the RDF store, by the query relaxation procedures, and the answer ranking techniques. The improvements in performance over a regular free text s...
A question answering system on domain specific knowledge with semantic web support
2011
In today's world the majority of information is accessible via the World Wide Web. A common way to access this information is through information retrieval applications like web search engines. We already know that web search engines flood their users with enormous amount of data from which they cannot figure out the essential and most important information. These disadvantages can be reduced with question answering systems. The basic idea of question answering systems is to be able to provide answers to a specific question written in natural language. The main goal of question answering systems is to find a specific answer. This paper presents an architecture of our ontology-driven system that uses semantic description of the processes, databases and web services for question answering system in the Slovenian language.
Question Answering on the Real Semantic Web
2007
Abstract. Restriction to a predefined set of ontologies, and consequently limitation to specific domain environments is a pervading drawback in Semantic Search technologies. In this work we present PowerAqua [2], a multi-ontology-based Question Answering (QA) platform that exploits multiple distributed ontologies and knowledge bases to answer queries in multi-domain environments.
Ontology‐lexicon–based question answering over linked data
ETRI Journal
Recently, Linked Open Data has become a large set of knowledge bases. Therefore, the need to query Linked Data using question answering (QA) techniques has attracted the attention of many researchers. A QA system translates natural language questions into structured queries, such as SPARQL queries, to be executed over Linked Data. The two main challenges in such systems are lexical and semantic gaps. A lexical gap refers to the difference between the vocabularies used in an input question and those used in the knowledge base. A semantic gap refers to the difference between expressed information needs and the representation of the knowledge base. In this paper, we present a novel method using an ontology lexicon and dependency parse trees to overcome lexical and semantic gaps. The proposed technique is evaluated on the QALD-5 benchmark and exhibits promising results.
Cooperative Question Answering for the Semantic Web
In this paper we propose a Cooperative Question Answering System that takes as input queries expressed in natural language and is able to return a cooperative answer obtained from resources in the Semantic Web, more specifically DBpedia databases represented in OWL/RDF. Moreover, when the DBpedia provides no answer, we use the WordNet in order to build similar questions. Our system resorts to ontologies not only for reasoning but also to find answers and is independent of prior knowledge of the semantic resources by the user. The natural language question is translated into its semantic representation and then answered by consulting the semantics sources of information. If there are multiple answers to the question posed (or to the similar questions for which DBpedia contains answers), they will be grouped according to their semantic meaning, providing a more cooperative and clean answer to the user.