Complex Query Augmentation for Question Answering over Knowledge Graphs

2019 ◽  
pp. 571-587 ◽  
Author(s):  
Abdelrahman Abdelkawi ◽  
Hamid Zafar ◽  
Maria Maleshkova ◽  
Jens Lehmann
Keyword(s):  
Question Answering ◽  
Complex Query ◽  
Knowledge Graphs ◽  
Query Augmentation

scholarly journals A Survey of Techniques for Constructing Chinese Knowledge Graphs and Their Applications

2018 ◽  
Vol 10 (9) ◽  
pp. 3245 ◽  
Author(s):  
Tianxing Wu ◽  
Guilin Qi ◽  
Cheng Li ◽  
Meng Wang
Keyword(s):  
Artificial Intelligence ◽  
Question Answering ◽  
Knowledge Representation And Reasoning ◽  
Knowledge Graph ◽  
Development History ◽  
One Belt One Road ◽  
History Of ◽  
Knowledge Graphs ◽  
The One ◽  
The Impact

With the continuous development of intelligent technologies, knowledge graph, the backbone of artificial intelligence, has attracted much attention from both academic and industrial communities due to its powerful capability of knowledge representation and reasoning. In recent years, knowledge graph has been widely applied in different kinds of applications, such as semantic search, question answering, knowledge management and so on. Techniques for building Chinese knowledge graphs are also developing rapidly and different Chinese knowledge graphs have been constructed to support various applications. Under the background of the “One Belt One Road (OBOR)” initiative, cooperating with the countries along OBOR on studying knowledge graph techniques and applications will greatly promote the development of artificial intelligence. At the same time, the accumulated experience of China in developing knowledge graphs is also a good reference to develop non-English knowledge graphs. In this paper, we aim to introduce the techniques of constructing Chinese knowledge graphs and their applications, as well as analyse the impact of knowledge graph on OBOR. We first describe the background of OBOR, and then introduce the concept and development history of knowledge graph and typical Chinese knowledge graphs. Afterwards, we present the details of techniques for constructing Chinese knowledge graphs, and demonstrate several applications of Chinese knowledge graphs. Finally, we list some examples to explain the potential impacts of knowledge graph on OBOR.

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

Improving Question Answering over Knowledge Graphs Using Graph Summarization

2021 ◽  
pp. 489-500 ◽  
Author(s):  
Sirui Li ◽  
Kok Wai Wong ◽  
Chun Che Fung ◽  
Dengya Zhu
Keyword(s):  
Question Answering ◽  
Knowledge Graphs ◽  
Graph Summarization

scholarly journals Leveraging Domain Context for Question Answering Over Knowledge Graph

2019 ◽  
Vol 4 (4) ◽  
pp. 323-335 ◽  
Author(s):  
Peihao Tong ◽  
Qifan Zhang ◽  
Junjie Yao
Keyword(s):  
Information Needs ◽  
Question Answering ◽  
Knowledge Graph ◽  
New Approach ◽  
Rich Domain ◽  
Complex Information ◽  
Question And Answer ◽  
Knowledge Graphs ◽  
The Rich ◽  
Matching Models

Abstract With the growing availability of different knowledge graphs in a variety of domains, question answering over knowledge graph (KG-QA) becomes a prevalent information retrieval approach. Current KG-QA methods usually resort to semantic parsing, search or neural matching models. However, they cannot well tackle increasingly long input questions and complex information needs. In this work, we propose a new KG-QA approach, leveraging the rich domain context in the knowledge graph. We incorporate the new approach with question and answer domain context descriptions. Specifically, for questions, we enrich them with users’ subsequent input questions within a session and expand the input question representation. For the candidate answers, we equip them with surrounding context structures, i.e., meta-paths within the targeting knowledge graph. On top of these, we design a cross-attention mechanism to improve the question and answer matching performance. An experimental study on real datasets verifies these improvements. The new approach is especially beneficial for specific knowledge graphs with complex questions.

Data-driven construction of SPARQL queries by approximate question graph alignment in question answering over knowledge graphs

2020 ◽  
Vol 146 ◽  
pp. 113205 ◽  
Author(s):  
Mahdi Bakhshi ◽  
Mohammadali Nematbakhsh ◽  
Mehran Mohsenzadeh ◽  
Amir Masoud Rahmani
Keyword(s):  
Question Answering ◽  
Data Driven ◽  
Graph Alignment ◽  
Knowledge Graphs

scholarly journals Question Answering Over Temporal Knowledge Graphs

2021 ◽  
Author(s):  
Apoorv Saxena ◽  
Soumen Chakrabarti ◽  
Partha Talukdar
Keyword(s):  
Question Answering ◽  
Knowledge Graphs

scholarly journals MQALD: Evaluating the impact of modifiers in question answering over knowledge graphs

Semantic Web ◽  
2021 ◽  
pp. 1-17
Author(s):  
Lucia Siciliani ◽  
Pierpaolo Basile ◽  
Pasquale Lops ◽  
Giovanni Semeraro
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Query Language ◽  
State Of The Art ◽  
Translation Process ◽  
Specific Data ◽  
Data Query ◽  
Question Answering Systems ◽  
Knowledge Graphs ◽  
The Impact

Question Answering (QA) over Knowledge Graphs (KG) aims to develop a system that is capable of answering users’ questions using the information coming from one or multiple Knowledge Graphs, like DBpedia, Wikidata, and so on. Question Answering systems need to translate the user’s question, written using natural language, into a query formulated through a specific data query language that is compliant with the underlying KG. This translation process is already non-trivial when trying to answer simple questions that involve a single triple pattern. It becomes even more troublesome when trying to cope with questions that require modifiers in the final query, i.e., aggregate functions, query forms, and so on. The attention over this last aspect is growing but has never been thoroughly addressed by the existing literature. Starting from the latest advances in this field, we want to further step in this direction. This work aims to provide a publicly available dataset designed for evaluating the performance of a QA system in translating articulated questions into a specific data query language. This dataset has also been used to evaluate three QA systems available at the state of the art.

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