Social event decomposition for constructing knowledge graph

2019 ◽  
Vol 100 ◽  
pp. 10-18 ◽  
Author(s):  
Hoang Long Nguyen ◽  
Jason J. Jung
Keyword(s):  
Knowledge Graph ◽  
Social Event ◽  
Event Decomposition ◽  
Constructing Knowledge

scholarly journals Knowledge Graphs

2021 ◽  
Vol 5 ◽  
Author(s):  
Roderic Page
Keyword(s):  
Knowledge Management ◽  
Large Scale ◽  
Personal Knowledge ◽  
Knowledge Graph ◽  
Specific Knowledge ◽  
Management Tools ◽  
Global Projects ◽  
Knowledge Graphs ◽  
Constructing Knowledge ◽  
Knowledge Management Tools

Knowledge graphs embody the idea of "everything connected to everything else." As attractive as this seems, there is a substantial gap between the dream of fully interconnected knowledge and the reality of data that is still mostly siloed, or weakly connected by shared strings such as taxonomic names. How do we move forward? Do we focus on building our own domain- or project-specific knowledge graphs, or do we engage with global projects such as Wikidata? Do we construct knowledge graphs, or focus on making our data "knowledge graph ready" by adopting structured markup in the hope that knowledge graphs will spontaneously self-assemble from that data? Do we focus on large-scale, database-driven projects (e.g., triple stores in the cloud), or do we rely on more localised and distributed approaches, such as annotations (e.g., hypothes.is), "content-hash" systems where a cryptographic hash of the data is also its identifier (Elliott et al. 2020), or the growing number of personal knowledge management tools (e.g., Roam, Obsidian, LogSeq)? This talk will share experiences (the good, bad, and the ugly) as I have tried to transition from naïve advocacy to constructing knowledge graphs (Page 2019), or participating in their construction (Page 2021).

scholarly journals Attribute Extraction Study in the Field of Military Equipment Based on Distant Supervision

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xindong You ◽  
Meijing Yang ◽  
Junmei Han ◽  
Jiangwei Ma ◽  
Gang Xiao ◽  
...  
Keyword(s):  
Knowledge Graph ◽  
Data Annotation ◽  
Military Equipment ◽  
Distant Supervision ◽  
Current State ◽  
Extraction Study ◽  
Attribute Extraction ◽  
Effective Organization ◽  
Constructing Knowledge ◽  
The Military

The effective organization and utilization of military equipment data is an important cornerstone for constructing knowledge system. Building a knowledge graph in the field of military equipment can effectively describe the relationship between entity and entity attribute information. Therefore, relevant personnel can obtain information quickly and accurately. Attribute extraction is an important part of building the knowledge graph. Given the lack of annotated data in the field of military equipment, we propose a new data annotation method, which adopts the idea of distant supervision to automatically build the attribute extraction dataset. We convert the attribute extraction task into a sequence annotation task. At the same time, we propose a RoBERTa-BiLSTM-CRF-SEL-based attribute extraction method. Firstly, a list of attribute name synonyms is constructed, then a corpus of military equipment attributes is obtained through automatic annotation of semistructured data in Baidu Encyclopedia. RoBERTa is used to obtain the vector encoding of the text. Then, input it into the entity boundary prediction layer to label the entity head and tail, and input the BiLSTM-CRF layer to predict the attribute label. The experimental results show that the proposed method can effectively perform attribute extraction in the military equipment domain. The F 1 value of the model reaches 77% on the constructed attribute extraction dataset, which outperforms the current state-of-art model.

Constructing Knowledge Graph from Big Data of Smart Grids

2019 ◽  
Author(s):  
Haichao Huang ◽  
Yaojun Chen ◽  
Bing Lou ◽  
Zhenyan Hongzhou ◽  
Jiaxian Wu ◽  
...  
Keyword(s):  
Big Data ◽  
Smart Grids ◽  
Knowledge Graph ◽  
Constructing Knowledge

scholarly journals Knowledge Graphs Representation for Event-Related E-News Articles

2021 ◽  
Vol 3 (4) ◽  
pp. 802-818
Author(s):  
M.V.P.T. Lakshika ◽  
H.A. Caldera
Keyword(s):  
Knowledge Representation ◽  
Contextual Information ◽  
Background Information ◽  
Knowledge Graph ◽  
Learning Approaches ◽  
Text Documents ◽  
Precise Knowledge ◽  
Knowledge Graphs ◽  
News Corpus ◽  
Constructing Knowledge

E-newspaper readers are overloaded with massive texts on e-news articles, and they usually mislead the reader who reads and understands information. Thus, there is an urgent need for a technology that can automatically represent the gist of these e-news articles more quickly. Currently, popular machine learning approaches have greatly improved presentation accuracy compared to traditional methods, but they cannot be accommodated with the contextual information to acquire higher-level abstraction. Recent research efforts in knowledge representation using graph approaches are neither user-driven nor flexible to deviations in the data. Thus, there is a striking concentration on constructing knowledge graphs by combining the background information related to the subjects in text documents. We propose an enhanced representation of a scalable knowledge graph by automatically extracting the information from the corpus of e-news articles and determine whether a knowledge graph can be used as an efficient application in analyzing and generating knowledge representation from the extracted e-news corpus. This knowledge graph consists of a knowledge base built using triples that automatically produce knowledge representation from e-news articles. Inclusively, it has been observed that the proposed knowledge graph generates a comprehensive and precise knowledge representation for the corpus of e-news articles.

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