PHLIQA 1: Multilevel Semantics in Question Answering (original) (raw)
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Issues and Challenges in Semantic Question Answering through Natural Language Interface
2003
"Semantic question answering demand different processing compared to the common information retrieval based question answering method because the semantic knowledge base is stored in the triples form. However, manipulating the knowledge requires understanding its structure and proficiency in semantic query language such as SPARQL. Natural language interface alleviates this by allowing user to input question in their human language and produce an answer by translating the input into an equivalent SPARQL before it is executed to retrieve the answer. However, several challenges exist such as concept identification, compatible ontology triple construction and semantic mapping. Existing studies have focused mainly on the semantic disambiguation such as through consolidation where user interaction is initiated so that relevant concept can be chosen by the user to be inserted into the SPARQL. In this paper we focus on the linguistic challenge in NLI, specifically on the question complexity depth, processes that need to be performed to answer the question and gaps in existing study. It is concluded that more works are in demand to process complex questions such that involve multi-variables and multi-triples. Besides the issues posed by the answer generation for the combined sub-questions in the composite question, the challenges faced by the simple question is also inherited which requires an application that is robust in ontology concept annotation. This will ensure that the translated query would capture entirely the expression in the original question and accurate answer will be returned. "
Formal methods in the design of question-answering systems
Artificial Intelligence, 1971
This paper contributes to the discussion whether and how predicate calculus should be used as a deep structure in question-answering programs. The first part of the paper stresses that there are several possible ways of using predicate calculus, and argues that predicate calculus has significant advantages above competing deep structures if the way of usiai~ it is carefully selected. The second half gives hints on how various natural-language corL~tructions can be encoded in a consistent way, and how axiom sets that define these encodings can be written and debugged.
State of the art on Semantic Question Answering
2007
The goal of Question Answering (QA) systems as described by [38] is to allow users to ask questions in natural language, using their own terminology, and receive a concise answer, possible with enough validating context.
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.
MuG-QA: Multilingual Grammatical Question Answering for RDF Data
2018 IEEE International Conference on Progress in Informatics and Computing (PIC), 2018
We introduce Multilingual Grammatical Question Answering (MuG-QA), a system for answering questions in the English, German, Italian and French languages over DBpedia. The natural language modelling and parsing is implemented using Grammatical Framework (GF), a grammar formalism having natural support for multilinguality. The question analysis is based on forming an abstract conceptual grammar from the questions, and then using linearisation of the abstract grammar into different languages to parse the questions. Once a natural language question is parsed, the resulting abstract grammar tree is matched with the knowledge base schema and contents to formulate a SPARQL query. A particular strength of our approach is that once the abstract grammar has been designed, implementation for a new concrete language is relatively quick, supposing that the language has basic support in the GF Resource Grammar Library. MuG-QA has been tested with data from the QALD-7 benchmark and showed competitive results.
The QALL-ME Framework: A specifiable-domain multilingual Question Answering architecture
Web Semantics: Science, Services and Agents on the World Wide Web, 2011
This paper presents the QALL-ME Framework, a reusable architecture for building multi-and crosslingual Question Answering (QA) systems working on structured data modelled by an ontology. It is released as free open source software with a set of demo components and extensive documentation, which makes it easy to use and adapt. The main characteristics of the QALL-ME Framework are: (i) its domain portability, achieved by an ontology modelling the target domain; (ii) the context awareness regarding space and time of the question; (iii) the use of textual entailment engines as the core of the question interpretation; and (iv) an architecture based on Service Oriented Architecture (SOA), which is realized using interchangeable web services for the framework components. Furthermore, we present a running example to clarify how the framework processes questions as well as a case study that shows a QA application built as an instantiation of the QALL-ME Framework for cinema/movie events in the tourism domain.
An Efficient Easily Adaptable System for Interpreting Natural Language Queries
1982
This paper gives an overall account of a prototype natural language question answering system, called Chat-80. Chat-80 has been designed to be both efficient and easily adaptable to a variety of applications. The system is implemented entirely in Prolog, a programming language based on logic. With the aid of a logic-based grammar formalism called extraposition grammars, Chat-80 translates English questions into the Prolog subset of logic. The resulting logical expression is then transformed by a planning algorithm into efficient Prolog, c.f. "query optimisation" in a relational database. Finally the Prolog form is executed to yield the answer. On a domain of world geography, most questions within the English subset are answered in well under one second, including relatively complex queries.
2020
Most approaches to question answering over linked data (QALD) frame the task as a machine learning problem, consisting in learning a mapping from natural language questions into SPARQL queries by parametrizing a model from training data given in the form of pairs of natural language (NL) question and SPARQL query. In this preliminary work we present an alternative approach to developing a QA system using machine learning that relies on the automatic generation of a QA grammar from a lemon lexicon. This model-based approach comes with a number of advantages compared to a machine learning approach. First, our approach gives maximum control over the QA interface to the developer of the system as every entry added to the lexicon increases the coverage of the grammar and thus of the QA system in a predictable way. This is in contrast to machine learning approaches where the impact of the addition of a single training example is difficult to predict. A further advantage of our approach is...
Architecture of an Ontology-Based Domain-Specific Natural Language Question Answering System
International journal of Web & Semantic Technology, 2013
Question answering (QA) system aims at retrieving precise information from a large collection of documents against a query. This paper describes the architecture of a Natural Language Question Answering (NLQA) system for a specific domain based on the ontological information, a step towards semantic web question answering. The proposed architecture defines four basic modules suitable for enhancing current QA capabilities with the ability of processing complex questions. The first module was the question processing, which analyses and classifies the question and also reformulates the user query. The second module allows the process of retrieving the relevant documents. The next module processes the retrieved documents, and the last module performs the extraction and generation of a response. Natural language processing techniques are used for processing the question and documents and also for answer extraction. Ontology and domain knowledge are used for reformulating queries and identifying the relations. The aim of the system is to generate short and specific answer to the question that is asked in the natural language in a specific domain. We have achieved 94 % accuracy of natural language question answering in our implementation.