scholarly journals Introducing External Knowledge to Answer Questions with Implicit Temporal Constraints over Knowledge Base

2020 ◽  
Vol 12 (3) ◽  
pp. 45
Author(s):  
Wenqing Wu ◽  
Zhenfang Zhu ◽  
Qiang Lu ◽  
Dianyuan Zhang ◽  
Qiangqiang Guo
Keyword(s):  
Natural Language ◽  
Knowledge Base ◽  
Question Answering ◽  
Knowledge Bases ◽  
Temporal Information ◽  
Temporal Constraints ◽  
External Knowledge ◽  
Question Answering Systems ◽  
Natural Language Question ◽  
Applied Knowledge

Knowledge base question answering (KBQA) aims to analyze the semantics of natural language questions and return accurate answers from the knowledge base (KB). More and more studies have applied knowledge bases to question answering systems, and when using a KB to answer a natural language question, there are some words that imply the tense (e.g., original and previous) and play a limiting role in questions. However, most existing methods for KBQA cannot model a question with implicit temporal constraints. In this work, we propose a model based on a bidirectional attentive memory network, which obtains the temporal information in the question through attention mechanisms and external knowledge. Specifically, we encode the external knowledge as vectors, and use additive attention between the question and external knowledge to obtain the temporal information, then further enhance the question vector to increase the accuracy. On the WebQuestions benchmark, our method not only performs better with the overall data, but also has excellent performance regarding questions with implicit temporal constraints, which are separate from the overall data. As we use attention mechanisms, our method also offers better interpretability.

Using a Dialogue Manager to Improve Semantic Web Search

2016 ◽  
Vol 12 (1) ◽  
pp. 62-78 ◽  
Author(s):  
Dora Melo ◽  
Irene Pimenta Rodrigues ◽  
Vitor Beires Nogueira
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Knowledge Base ◽  
System Performance ◽  
Web Search ◽  
Question Answering ◽  
Similar Question ◽  
User Intentions ◽  
Question Answering Systems ◽  
Dialogue Manager

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.

Natural language question answering: the view from here

2001 ◽  
Vol 7 (4) ◽  
pp. 275-300 ◽  
Author(s):  
L. HIRSCHMAN ◽  
R. GAIZAUSKAS
Keyword(s):  
Natural Language ◽  
Search Engines ◽  
Question Answering ◽  
Text Retrieval ◽  
Everyday Language ◽  
Question Answering Systems ◽  
Natural Language Question ◽  
On Line ◽  
Language Question

As users struggle to navigate the wealth of on-line information now available, the need for automated question answering systems becomes more urgent. We need systems that allow a user to ask a question in everyday language and receive an answer quickly and succinctly, with sufficient context to validate the answer. Current search engines can return ranked lists of documents, but they do not deliver answers to the user.Question answering systems address this problem. Recent successes have been reported in a series of question-answering evaluations that started in 1999 as part of the Text Retrieval Conference (TREC). The best systems are now able to answer more than two thirds of factual questions in this evaluation.

scholarly journals Similarity and set size impact the efficiency of hypothesis testing among faces

2019 ◽  
Author(s):  
Andrew T Hendrickson ◽  
Carolyn Semmler
Keyword(s):  
Recognition Memory ◽  
Natural Language ◽  
Hypothesis Testing ◽  
Face Processing ◽  
Question Answering ◽  
Strong Impact ◽  
Set Size ◽  
Question Answering Systems ◽  
Natural Language Question ◽  
Question Formation

Face processing is an important cognitive process that has been extensively studied in the domains of recognition memory and visual search. However, with the advent of biometric question- answering systems to assist experts searching for specific individuals, it is increasingly important to understand if the known biases and strengths of face processing extend to more deliberate natural language question formation and search. In this work we present a novel experimental task where people ask natural language questions to test hypotheses about faces in which the efficiency of the questions can be directly observed. The results indicate some aspects of existing paradigms do transfer to hypothesis testing, including less efficient questions when the number of items increases or as the similarity be- tween items increases. However, unlike recognition memory specific demographic features (gender and ethnicity) do not seem to have a strong impact on efficiency.

Software architecture of the question-answering subsystem with elements of self-learning

2021 ◽  
Vol 26 (jai2021.26(2)) ◽  
pp. 88-95
Author(s):  
Hlybovets A ◽  
◽  
Tsaruk A ◽  
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Speech Synthesis ◽  
Question Answering ◽  
Building Blocks ◽  
Knowledge Bases ◽  
Learning Technologies ◽  
Software Systems ◽  
Question Answering Systems ◽  
Self Learning

Within the framework of this paper, the analysis of software systems of question-answering type and their basic architectures has been carried out. With the development of machine learning technologies, creation of natural language processing (NLP) engines, as well as the rising popularity of virtual personal assistant programs that use the capabilities of speech synthesis (text-to-speech), there is a growing need in developing question-answering systems which can provide personalized answers to users' questions. All modern cloud providers proposed frameworks for organization of question answering systems but still we have a problem with personalized dialogs. Personalization is very important, it can put forward additional demands to a question-answering system’s capabilities to take this information into account while processing users’ questions. Traditionally, a question-answering system (QAS) is developed in the form of an application that contains a knowledge base and a user interface, which provides a user with answers to questions, and a means of interaction with an expert. In this article we analyze modern approaches to architecture development and try to build system from the building blocks that already exist on the market. Main criteria for the NLP modules were: support of the Ukrainian language, natural language understanding, functions of automatic definition of entities (attributes), ability to construct a dialogue flow, quality and completeness of documentation, API capabilities and integration with external systems, possibilities of external knowledge bases integration After provided analyses article propose the detailed architecture of the question-answering subsystem with elements of self-learning in the Ukrainian language. In the work you can find detailed description of main semantic components of the system (architecture components)

SWFQA Semantic Web Based Framework for Question Answering

2019 ◽  
Vol 9 (1) ◽  
pp. 88-106
Author(s):  
Irphan Ali ◽  
Divakar Yadav ◽  
Ashok Kumar Sharma
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Knowledge Base ◽  
Language Processing ◽  
Question Answering ◽  
Knowledge Bases ◽  
Digital Information ◽  
Web Based ◽  
Question Answering System ◽  
User Query

A question answering system aims to provide the correct and quick answer to users' query from a knowledge base. Due to the growth of digital information on the web, information retrieval system is the need of the day. Most recent question answering systems consult knowledge bases to answer a question, after parsing and transforming natural language queries to knowledge base-executable forms. In this article, the authors propose a semantic web-based approach for question answering system that uses natural language processing for analysis and understanding the user query. It employs a “Total Answer Relevance Score” to find the relevance of each answer returned by the system. The results obtained thereof are quite promising. The real-time performance of the system has been evaluated on the answers, extracted from the knowledge base.

scholarly journals SPARQL QUERY GENERATION FOR COMPLEX QUESTION ANSWERING WITH BERT AND BILSTM-BASED MODEL

2020 ◽  
Author(s):  
D. A. Evseev ◽  
◽  
M. Yu. Arkhipov ◽  
Keyword(s):  
Natural Language ◽  
Knowledge Base ◽  
Question Answering ◽  
Sparql Query ◽  
Question Answering System ◽  
Model Ranking ◽  
Natural Language Question ◽  
Query Generation ◽  
Language Question ◽  
Complex Question

In this paper we describe question answering system for answering of complex questions over Wikidata knowledge base. Unlike simple questions, which require extraction of single fact from the knowledge base, complex questions are based on more than one triplet and need logical or comparative reasoning. The proposed question answering system translates a natural language question into a query in SPARQL language, execution of which gives an answer. The system includes the models which define the SPARQL query template corresponding to the question and then fill the slots in the template with entities, relations and numerical values. For entity detection we use BERTbased sequence labelling model. Ranking of candidate relations is performed in two steps with BiLSTM and BERT-based models. The proposed models are the first solution for LC-QUAD2.0 dataset. The system is capable of answering complex questions which involve comparative or boolean reasoning.

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