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

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.

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

Sustainability ◽

10.3390/su10093245 ◽

2018 ◽

Vol 10 (9) ◽

pp. 3245 ◽

Cited By ~ 7

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.

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BUILD KNOWLEDGE GRAPH FROM HETEROGENEOUS DOCUMENTS

Journal of Science and Technology - IUH ◽

10.46242/jst-iuh.v47i05.761 ◽

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

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Leveraging Domain Context for Question Answering Over Knowledge Graph

Data Science and Engineering ◽

10.1007/s41019-019-00109-w ◽

2019 ◽

Vol 4 (4) ◽

pp. 323-335 ◽

Cited By ~ 3

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.

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End-to-End Argumentation Knowledge Graph Construction

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i05.6231 ◽

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.

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

Information ◽

10.3390/info12070271 ◽

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.

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Training and hackathon on building biodiversity knowledge graphs

Research Ideas and Outcomes ◽

10.3897/rio.5.e36152 ◽

2019 ◽

Vol 5 ◽

Cited By ~ 1

Author(s):

Joel Sachs ◽

Roderic Page ◽

Steven J Baskauf ◽

Jocelyn Pender ◽

Beatriz Lujan-Toro ◽

...

Keyword(s):

Knowledge Graph ◽

Graph Database ◽

Biodiversity Informatics ◽

Biodiversity Data ◽

Specific Knowledge ◽

Biodiversity Knowledge ◽

Training Event ◽

Web Infrastructure ◽

Ongoing Development ◽

Knowledge Graphs

Knowledge graphs have the potential to unite disconnected digitized biodiversity data, and there are a number of efforts underway to build biodiversity knowledge graphs. More generally, the recent popularity of knowledge graphs, driven in part by the advent and success of the Google Knowledge Graph, has breathed life into the ongoing development of semantic web infrastructure and prototypes in the biodiversity informatics community. We describe a one week training event and hackathon that focused on applying three specific knowledge graph technologies – the Neptune graph database; Metaphactory; and Wikidata - to a diverse set of biodiversity use cases.We give an overview of the training, the projects that were advanced throughout the week, and the critical discussions that emerged. We believe that the main barriers towards adoption of biodiversity knowledge graphs are the lack of understanding of knowledge graphs and the lack of adoption of shared unique identifiers. Furthermore, we believe an important advancement in the outlook of knowledge graph development is the emergence of Wikidata as an identifier broker and as a scoping tool. To remedy the current barriers towards biodiversity knowledge graph development, we recommend continued discussions at workshops and at conferences, which we expect to increase awareness and adoption of knowledge graph technologies.

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What is in the KGQA Benchmark Datasets? Survey on Challenges in Datasets for Question Answering on Knowledge Graphs

Journal on Data Semantics ◽

10.1007/s13740-021-00128-9 ◽

2021 ◽

Author(s):

Nadine Steinmetz ◽

Kai-Uwe Sattler

Keyword(s):

Natural Language ◽

Question Answering ◽

Research Evaluation ◽

Benchmark Dataset ◽

Complex Queries ◽

Benchmark Datasets ◽

Knowledge Graphs

AbstractQuestion Answering based on Knowledge Graphs (KGQA) still faces difficult challenges when transforming natural language (NL) to SPARQL queries. Simple questions only referring to one triple are answerable by most QA systems, but more complex questions requiring complex queries containing subqueries or several functions are still a tough challenge within this field of research. Evaluation results of QA systems therefore also might depend on the benchmark dataset the system has been tested on. For the purpose to give an overview and reveal specific characteristics, we examined currently available KGQA datasets regarding several challenging aspects. This paper presents a detailed look into the datasets and compares them in terms of challenges a KGQA system is facing.

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Improved Entity Linking for Simple Question Answering Over Knowledge Graph

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194021400039 ◽

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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Mobile Software Assurance Informed through Knowledge Graph Construction: The OWASP Threat of Insecure Data Storage

Journal of Computer Science Research ◽

10.30564/jcsr.v2i2.1765 ◽

2020 ◽

Vol 2 (2) ◽

Author(s):

Suzanna Schmeelk ◽

Lixin Tao

Keyword(s):

Data Storage ◽

Program Analysis ◽

Web Application ◽

Security Analysis ◽

Knowledge Graph ◽

Healthcare Applications ◽

Sensitive Data ◽

Knowledge Graphs ◽

Mobile Malware Detection ◽

Software Assurance

Many organizations, to save costs, are movinheg to t Bring Your Own Mobile Device (BYOD) model and adopting applications built by third-parties at an unprecedented rate. Our research examines software assurance methodologies specifically focusing on security analysis coverage of the program analysis for mobile malware detection, mitigation, and prevention. This research focuses on secure software development of Android applications by developing knowledge graphs for threats reported by the Open Web Application Security Project (OWASP). OWASP maintains lists of the top ten security threats to web and mobile applications. We develop knowledge graphs based on the two most recent top ten threat years and show how the knowledge graph relationships can be discovered in mobile application source code. We analyze 200+ healthcare applications from GitHub to gain an understanding of their software assurance of their developed software for one of the OWASP top ten moble threats, the threat of “Insecure Data Storage.” We find that many of the applications are storing personally identifying information (PII) in potentially vulnerable places leaving users exposed to higher risks for the loss of their sensitive data.

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TransET: Knowledge Graph Embedding with Entity Types

Electronics ◽

10.3390/electronics10121407 ◽

2021 ◽

Vol 10 (12) ◽

pp. 1407

Author(s):

Peng Wang ◽

Jing Zhou ◽

Yuzhang Liu ◽

Xingchen Zhou

Keyword(s):

Link Prediction ◽

State Of The Art ◽

Score Function ◽

Graph Embedding ◽

Vector Spaces ◽

Knowledge Graph ◽

Semantic Features ◽

Knowledge Graphs ◽

Real World Datasets ◽

Low Dimensional

Knowledge graph embedding aims to embed entities and relations into low-dimensional vector spaces. Most existing methods only focus on triple facts in knowledge graphs. In addition, models based on translation or distance measurement cannot fully represent complex relations. As well-constructed prior knowledge, entity types can be employed to learn the representations of entities and relations. In this paper, we propose a novel knowledge graph embedding model named TransET, which takes advantage of entity types to learn more semantic features. More specifically, circle convolution based on the embeddings of entity and entity types is utilized to map head entity and tail entity to type-specific representations, then translation-based score function is used to learn the presentation triples. We evaluated our model on real-world datasets with two benchmark tasks of link prediction and triple classification. Experimental results demonstrate that it outperforms state-of-the-art models in most cases.

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