Unsupervised Approach for Knowledge-Graph Creation from Conversation: The Use of Intent Supervision for Slot Filling

2021 ◽  
Author(s):  
Zishan Ahmad ◽  
Asif Ekbal ◽  
Shubhashis Sengupta ◽  
Anutosh Maitra ◽  
Roshni Ramnani ◽  
...  
Keyword(s):  
Knowledge Graph ◽  
Unsupervised Approach ◽  
Slot Filling

scholarly journals Unsupervised Embedding Enhancements of Knowledge Graphs using Textual Associations

2019 ◽  
Author(s):  
Neil Veira ◽  
Brian Keng ◽  
Kanchana Padmanabhan ◽  
Andreas Veneris
Keyword(s):  
Link Prediction ◽  
Graph Embedding ◽  
Structured Data ◽  
Knowledge Graph ◽  
Sources Of Information ◽  
Textual Information ◽  
Text Document ◽  
First Case ◽  
Textual Data ◽  
Unsupervised Approach

Knowledge graph embeddings are instrumental for representing and learning from multi-relational data, with recent embedding models showing high effectiveness for inferring new facts from existing databases. However, such precisely structured data is usually limited in quantity and in scope. Therefore, to fully optimize the embeddings it is important to also consider more widely available sources of information such as text. This paper describes an unsupervised approach to incorporate textual information by augmenting entity embeddings with embeddings of associated words. The approach does not modify the optimization objective for the knowledge graph embedding, which allows it to be integrated with existing embedding models. Two distinct forms of textual data are considered, with different embedding enhancements proposed for each case. In the first case, each entity has an associated text document that describes it. In the second case, a text document is not available, and instead entities occur as words or phrases in an unstructured corpus of text fragments. Experiments show that both methods can offer improvement on the link prediction task when applied to many different knowledge graph embedding models.

Discover Relations in the Industry 4.0 Standards Via Unsupervised Learning on Knowledge Graph Embeddings

2021 ◽  
Vol 2 (3) ◽  
pp. 336-347
Author(s):  
Ariam Rivas ◽  
Irlan Grangel-Gonzalez ◽  
Diego Collarana ◽  
Jens Lehmann ◽  
Maria-esther Vidal
Keyword(s):  
Data Model ◽  
Industry 4.0 ◽  
Knowledge Graph ◽  
Similarity Metrics ◽  
Graph Embeddings ◽  
Graph Data ◽  
Detection Algorithms ◽  
Unsupervised Approach ◽  
Main Components ◽  
Smart Factories

Industry 4.0 (I4.0) standards and standardization frameworks provide a unified way to describe smart factories. Standards specify the main components, systems, and processes inside a smart factory and the interaction among all of them. Furthermore, standardization frameworks classify standards according to their functions into layers and dimensions. Albeit informative, frameworks can categorize similar standards differently. As a result, interoperability conflicts are generated whenever smart factories are described with miss-classified standards. Approaches like ontologies and knowledge graphs enable the integration of standards and frameworks in a structured way. They also encode the meaning of the standards, known relations among them, as well as their classification according to existing frameworks. This structured modeling of the I4.0 landscape using a graph data model provides the basis for graph-based analytical methods to uncover alignments among standards. This paper contributes to analyzing the relatedness among standards and frameworks; it presents an unsupervised approach for discovering links among standards. The proposed method resorts to knowledge graph embeddings to determine relatedness among standards-based on similarity metrics. The proposed method is agnostic to the technique followed to create the embeddings and to the similarity measure. Building on the similarity values, community detection algorithms can automatically create communities of highly similar standards. Our approach follows the homophily principle, and assumes that related standards are together in a community. Thus, alignments across standards are predicted and interoperability issues across them are solved. We empirically evaluate our approach on a knowledge graph of 249 I4.0 standards using the Trans$^*$ family of embedding models for knowledge graph entities. Our results are promising and suggest that relations among standards can be detected accurately.

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

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.

Build A Knowledge Graph

2019 ◽  
Author(s):  
Jemmy Wiratama
Keyword(s):  
Knowledge Graph

I'm an Science & Technology enthusiast. I still learn how to build a knowledge graph and how to write a paper.

ClockWork-RNN Based Architectures for Slot Filling

2017 ◽  
Author(s):  
Despoina Georgiadou ◽  
Vassilios Diakoloukas ◽  
Vassilios Tsiaras ◽  
Vassilios Digalakis
Keyword(s):  
Slot Filling
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