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.

Using a Dialogue Manager to Improve Semantic Web Search

2018 ◽  
pp. 1166-1183
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.

scholarly journals Semantic Web Search Through Natural Language Dialogues

2018 ◽  
pp. 329-349 ◽  
Author(s):  
Dora Melo ◽  
Irene Pimenta Rodrigues ◽  
Vitor Beires Nogueira
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
System Performance ◽  
Web Search ◽  
Question Answering ◽  
Small Dimension ◽  
User Intentions ◽  
Question Answering Systems ◽  
Dialogue Manager ◽  
User Intervention

The Semantic Web as a knowledge base gives to the Question Answering systems the capabilities needed to go well beyond the usual word matching in the documents and find a more accurate answer, without needing the user intervention to interpret the documents returned. In this chapter, the authors introduce a Dialogue Manager that, throughout 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 system performance is evaluated by comparing with similar question answering systems. Although the test suite is of small dimension, the results obtained are very promising.

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.

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 Work Out the Semantic Web Search: The Cooperative Way

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Dora Melo ◽  
Irene Pimenta Rodrigues ◽  
Vitor Beires Nogueira
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Web Search ◽  
Question Answering ◽  
Semantic Representation ◽  
Sources Of Information ◽  
Semantic Meaning ◽  
Natural Language Question ◽  
Semantic Resources ◽  
Language Question

We propose a Cooperative Question Answering System that takes as input natural language queries and is able to return a cooperative answer based on semantic web resources, more specifically DBpedia represented in OWL/RDF as knowledge base and WordNet 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. The system is able to clarify the problems of ambiguity and helps finding the path to the correct answer. 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 clarified answer to the user.

scholarly journals Composing Questions through Conceptual Authoring

2007 ◽  
Vol 33 (1) ◽  
pp. 105-133 ◽  
Author(s):  
Catalina Hallett ◽  
Donia Scott ◽  
Richard Power
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Free Text ◽  
Risk Averse ◽  
Proof Of Concept ◽  
Concept System ◽  
Complex Queries ◽  
Extensive Training ◽  
Question Answering Systems ◽  
Medical Histories

This article describes a method for composing fluent and complex natural language questions, while avoiding the standard pitfalls of free text queries. The method, based on Conceptual Authoring, is targeted at question-answering systems where reliability and transparency are critical, and where users cannot be expected to undergo extensive training in question composition. This scenario is found in most corporate domains, especially in applications that are risk-averse. We present a proof-of-concept system we have developed: a question-answering interface to a large repository of medical histories in the area of cancer. We show that the method allows users to successfully and reliably compose complex queries with minimal training.

BUILD KNOWLEDGE GRAPH FROM HETEROGENEOUS DOCUMENTS

2021 ◽  
Vol 47 (05) ◽  
Author(s):  
NGUYỄN CHÍ HIẾU
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Semantic Analysis ◽  
Knowledge Graph ◽  
Question Answering Systems ◽  
Knowledge Graphs

Knowledge Graphs are applied in many fields such as search engines, semantic analysis, and question answering in recent years. However, there are many obstacles for building knowledge graphs as methodologies, data and tools. This paper introduces a novel methodology to build knowledge graph from heterogeneous documents.  We use the methodologies of Natural Language Processing and deep learning to build this graph. The knowledge graph can use in Question answering systems and Information retrieval especially in Computing domain

A Review on Question Generation from Natural Language Text

2022 ◽  
Vol 40 (1) ◽  
pp. 1-43
Author(s):  
Ruqing Zhang ◽  
Jiafeng Guo ◽  
Lu Chen ◽  
Yixing Fan ◽  
Xueqi Cheng
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Data Augmentation ◽  
Text Structure ◽  
Current Status ◽  
Question Generation ◽  
Natural Language Text ◽  
Question Answering Systems ◽  
The Right ◽  
Language Text

Question generation is an important yet challenging problem in Artificial Intelligence (AI), which aims to generate natural and relevant questions from various input formats, e.g., natural language text, structure database, knowledge base, and image. In this article, we focus on question generation from natural language text, which has received tremendous interest in recent years due to the widespread applications such as data augmentation for question answering systems. During the past decades, many different question generation models have been proposed, from traditional rule-based methods to advanced neural network-based methods. Since there have been a large variety of research works proposed, we believe it is the right time to summarize the current status, learn from existing methodologies, and gain some insights for future development. In contrast to existing reviews, in this survey, we try to provide a more comprehensive taxonomy of question generation tasks from three different perspectives, i.e., the types of the input context text, the target answer, and the generated question. We take a deep look into existing models from different dimensions to analyze their underlying ideas, major design principles, and training strategies We compare these models through benchmark tasks to obtain an empirical understanding of the existing techniques. Moreover, we discuss what is missing in the current literature and what are the promising and desired future directions.

Mining information from sentences through Semantic Web data and Information Extraction tasks

2020 ◽  
pp. 016555152093438
Author(s):  
Jose L. Martinez-Rodriguez ◽  
Ivan Lopez-Arevalo ◽  
Ana B. Rios-Alvarado
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Information Extraction ◽  
Knowledge Base ◽  
Semantic Similarity ◽  
Similarity Measure ◽  
Real World ◽  
Semantic Similarity Measure ◽  
Web Standards ◽  
Extract Information

The Semantic Web provides guidelines for the representation of information about real-world objects (entities) and their relations (properties). This is helpful for the dissemination and consumption of information by people and applications. However, the information is mainly contained within natural language sentences, which do not have a structure or linguistic descriptions ready to be directly processed by computers. Thus, the challenge is to identify and extract the elements of information that can be represented. Hence, this article presents a strategy to extract information from sentences and its representation with Semantic Web standards. Our strategy involves Information Extraction tasks and a hybrid semantic similarity measure to get entities and relations that are later associated with individuals and properties from a Knowledge Base to create RDF triples (Subject–Predicate–Object structures). The experiments demonstrate the feasibility of our method and that it outperforms the accuracy provided by a pattern-based method from the literature.

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