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.

scholarly journals Knowledge Graphs for COVID-19: An Exploratory Review of the Current Landscape

2021 ◽  
Vol 11 (4) ◽  
pp. 300
Author(s):  
Avishek Chatterjee ◽  
Cosimo Nardi ◽  
Cary Oberije ◽  
Philippe Lambin
Keyword(s):  
Information Needs ◽  
Drug Repurposing ◽  
Research Literature ◽  
Google Scholar ◽  
Knowledge Graph ◽  
Search Term ◽  
Complex Information ◽  
Big Picture ◽  
Improving Patient Care ◽  
Keyword Searches

Background: Searching through the COVID-19 research literature to gain actionable clinical insight is a formidable task, even for experts. The usefulness of this corpus in terms of improving patient care is tied to the ability to see the big picture that emerges when the studies are seen in conjunction rather than in isolation. When the answer to a search query requires linking together multiple pieces of information across documents, simple keyword searches are insufficient. To answer such complex information needs, an innovative artificial intelligence (AI) technology named a knowledge graph (KG) could prove to be effective. Methods: We conducted an exploratory literature review of KG applications in the context of COVID-19. The search term used was “covid-19 knowledge graph”. In addition to PubMed, the first five pages of search results for Google Scholar and Google were considered for inclusion. Google Scholar was used to include non-peer-reviewed or non-indexed articles such as pre-prints and conference proceedings. Google was used to identify companies or consortiums active in this domain that have not published any literature, peer-reviewed or otherwise. Results: Our search yielded 34 results on PubMed and 50 results each on Google and Google Scholar. We found KGs being used for facilitating literature search, drug repurposing, clinical trial mapping, and risk factor analysis. Conclusions: Our synopses of these works make a compelling case for the utility of this nascent field of research.

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

Analyzing the Text of Clinical Literature for Question Answering

2010 ◽  
pp. 190-220
Author(s):  
Yun Niu ◽  
Graeme Hirst
Keyword(s):  
Information Needs ◽  
Question Answering ◽  
Named Entities ◽  
Semantic Class ◽  
Complex Information ◽  
Semantic Classes ◽  
Clinical Literature ◽  
Clinical Scenarios ◽  
Natural Language Question ◽  
Language Question

The task of question answering (QA) is to find an accurate and precise answer to a natural language question in some predefined text. Most existing QA systems handle fact-based questions that usually take named entities as the answers. In this chapter, the authors take clinical QA as an example to deal with more complex information needs. They propose an approach using Semantic class analysis as the organizing principle to answer clinical questions. They investigate three Semantic classes that correspond to roles in the commonly accepted PICO format of describing clinical scenarios. The three Semantic classes are: the description of the patient (or the problem), the intervention used to treat the problem, and the clinical outcome. The authors focus on automatic analysis of two important properties of the Semantic classes.

scholarly journals ROBOKOP: an abstraction layer and user interface for knowledge graphs to support question answering

2019 ◽  
Vol 35 (24) ◽  
pp. 5382-5384 ◽  
Author(s):  
Kenneth Morton ◽  
Patrick Wang ◽  
Chris Bizon ◽  
Steven Cox ◽  
James Balhoff ◽  
...  
Keyword(s):  
User Interface ◽  
Question Answering ◽  
Supplementary Information ◽  
Knowledge Graph ◽  
Graph Database ◽  
Database Queries ◽  
Complex Queries ◽  
Abstraction Layer ◽  
Software License ◽  
Knowledge Graphs

Abstract Summary Knowledge graphs (KGs) are quickly becoming a common-place tool for storing relationships between entities from which higher-level reasoning can be conducted. KGs are typically stored in a graph-database format, and graph-database queries can be used to answer questions of interest that have been posed by users such as biomedical researchers. For simple queries, the inclusion of direct connections in the KG and the storage and analysis of query results are straightforward; however, for complex queries, these capabilities become exponentially more challenging with each increase in complexity of the query. For instance, one relatively complex query can yield a KG with hundreds of thousands of query results. Thus, the ability to efficiently query, store, rank and explore sub-graphs of a complex KG represents a major challenge to any effort designed to exploit the use of KGs for applications in biomedical research and other domains. We present Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways as an abstraction layer and user interface to more easily query KGs and store, rank and explore query results. Availability and implementation An instance of the ROBOKOP UI for exploration of the ROBOKOP Knowledge Graph can be found at http://robokop.renci.org. The ROBOKOP Knowledge Graph can be accessed at http://robokopkg.renci.org. Code and instructions for building and deploying ROBOKOP are available under the MIT open software license from https://github.com/NCATS-Gamma/robokop. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals End-to-End Argumentation Knowledge Graph Construction

2020 ◽  
Vol 34 (05) ◽  
pp. 7367-7374
Author(s):  
Khalid Al-Khatib ◽  
Yufang Hou ◽  
Henning Wachsmuth ◽  
Charles Jochim ◽  
Francesca Bonin ◽  
...  
Keyword(s):  
Large Scale ◽  
Question Answering ◽  
Knowledge Graph ◽  
Exploratory Search ◽  
Text Generation ◽  
Fake News ◽  
High Quality ◽  
Web Based ◽  
Knowledge Graphs ◽  
End To End

This paper studies the end-to-end construction of an argumentation knowledge graph that is intended to support argument synthesis, argumentative question answering, or fake news detection, among others. The study is motivated by the proven effectiveness of knowledge graphs for interpretable and controllable text generation and exploratory search. Original in our work is that we propose a model of the knowledge encapsulated in arguments. Based on this model, we build a new corpus that comprises about 16k manual annotations of 4740 claims with instances of the model's elements, and we develop an end-to-end framework that automatically identifies all modeled types of instances. The results of experiments show the potential of the framework for building a web-based argumentation graph that is of high quality and large scale.

A Joint Model for Representation Learning of Tibetan Knowledge Graph Based on Encyclopedia

2021 ◽  
Vol 20 (2) ◽  
pp. 1-17
Author(s):  
Yuan Sun ◽  
Andong Chen ◽  
Chaofan Chen ◽  
Tianci Xia ◽  
Xiaobing Zhao
Keyword(s):  
Neural Networks ◽  
Language Processing ◽  
Representation Learning ◽  
Joint Model ◽  
Graph Representation ◽  
Knowledge Graph ◽  
High Resource ◽  
Complex Information ◽  
Language Knowledge ◽  
Knowledge Graphs

Learning the representation of a knowledge graph is critical to the field of natural language processing. There is a lot of research for English knowledge graph representation. However, for the low-resource languages, such as Tibetan, how to represent sparse knowledge graphs is a key problem. In this article, aiming at scarcity of Tibetan knowledge graphs, we extend the Tibetan knowledge graph by using the triples of the high-resource language knowledge graphs and Point of Information map information. To improve the representation learning of the Tibetan knowledge graph, we propose a joint model to merge structure and entity description information based on the Translating Embeddings and Convolution Neural Networks models. In addition, to solve the segmentation errors, we use character and word embedding to learn more complex information in Tibetan. Finally, the experimental results show that our model can make a better representation of the Tibetan knowledge graph than the baseline.

scholarly journals Challenges, Techniques, and Trends of Simple Knowledge Graph Question Answering: A Survey

Information ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 271
Author(s):  
Mohammad Yani ◽  
Adila Alfa Krisnadhi
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Common Type ◽  
Knowledge Graph ◽  
Simple Question ◽  
Comprehensive Survey ◽  
Knowledge Graphs ◽  
Comprehensive Study

Simple questions are the most common type of questions used for evaluating a knowledge graph question answering (KGQA). A simple question is a question whose answer can be captured by a factoid statement with one relation or predicate. Knowledge graph question answering (KGQA) systems are systems whose aim is to automatically answer natural language questions (NLQs) over knowledge graphs (KGs). There are varieties of researches with different approaches in this area. However, the lack of a comprehensive study to focus on addressing simple questions from all aspects is tangible. In this paper, we present a comprehensive survey of answering simple questions to classify available techniques and compare their advantages and drawbacks in order to have better insights of existing issues and recommendations to direct future works.

scholarly journals Querying Knowledge Graphs in Natural Language

2020 ◽  
Author(s):  
Shiqi Liang ◽  
Kurt Stockinger ◽  
Tarcisio Mendes de Farias ◽  
Maria Anisimova ◽  
Manuel Gil
Keyword(s):  
Natural Language ◽  
Information Needs ◽  
Large Scale ◽  
Question Answering ◽  
Deep Understanding ◽  
Network Models ◽  
Query Languages ◽  
End Users ◽  
Neural Network Models ◽  
Knowledge Graphs

Abstract Knowledge graphs are a powerful concept for querying large amounts of data. These knowledge graphs are typically enormous and are often not easily accessible to end-users because they require specialized knowledge in query languages such as SPARQL. Moreover, end-users need a deep understanding of the structure of the underlying data models often based on the Resource Description Framework (RDF). This drawback has led to the development of Question-Answering (QA) systems that enable end-users to express their information needs in natural language. While existing systems simplify user access, there is still room for improvement in the accuracy of these systems. In this paper we propose a new QA system for translating natural language questions into SPARQL queries. The key idea is to break up the translation process into 5 smaller, more manageable sub-tasks and use ensemble machine learning methods as well as Tree-LSTM-based neural network models to automatically learn and translate a natural language question into a SPARQL query. The performance of our proposed QA system is empirically evaluated using the two renowned benchmarks - the 7th Question Answering over Linked Data Challenge (QALD-7) and the Large-Scale Complex Question Answering Dataset (LC-QuAD). Experimental results show that our QA system outperforms the state-of-art systems by 15% on the QALD-7 dataset and by 48% on the LC-QuAD dataset, respectively. In addition, we make our source code available.

Improved Entity Linking for Simple Question Answering Over Knowledge Graph

2021 ◽  
Vol 31 (01) ◽  
pp. 55-80
Author(s):  
Kai Chen ◽  
Guohua Shen ◽  
Zhiqiu Huang ◽  
Haijuan Wang
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Knowledge Graph ◽  
Ranking Algorithm ◽  
Entity Linking ◽  
Question Answering Systems ◽  
Simple Question ◽  
Knowledge Graphs ◽  
Relation Prediction ◽  
The Impact

Question Answering systems over Knowledge Graphs (KG) answer natural language questions using facts contained in a knowledge graph, and Simple Question Answering over Knowledge Graphs (KG-SimpleQA) means that the question can be answered by a single fact. Entity linking, which is a core component of KG-SimpleQA, detects the entities mentioned in questions, and links them to the actual entity in KG. However, traditional methods ignore some information of entities, especially entity types, which leads to the emergence of entity ambiguity problem. Besides, entity linking suffers from out-of-vocabulary (OOV) problem due to the limitation of pre-trained word embeddings. To address these problems, we encode questions in a novel way and encode the features contained in the entities in a multilevel way. To evaluate the enhancement of the whole KG-SimpleQA brought by our improved entity linking, we utilize a relatively simple approach for relation prediction. Besides, to reduce the impact of losing the feature during the encoding procedure, we utilize a ranking algorithm to re-rank (entity, relation) pairs. According to the experimental results, our method for entity linking achieves an accuracy of 81.8% that beats the state-of-the-art methods, and our improved entity linking brings a boost of 5.6% for the whole KG-SimpleQA.

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