Odsc 2019 entity_reputation_knowledge_graph
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This document discusses using knowledge graphs to extract insights from unstructured data. It presents a data flow architecture that uses natural language processing to extract entities and relationships from social media posts and construct a knowledge graph. Graph inference algorithms like degree centrality are then used to determine popular entities. This provides actionable insights into how products are performing based on individuals' reviews on social media. The document also provides some lessons learned around knowledge graph construction and querying.
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Do Your Projects With Technology Experts
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Visit : www.lemenizinfotech.com / www.ieeemaster.com
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from influential users and rank their Twitter handles so
that dangerous tweets can be avoided going public on
Twitter before fact-checking which can hurt the sentiments
of people and can take the shape of violence. The study
aims to analyse and rank twitter users according to their
influential power and extremism of their tweets to help
prevent major protests and violent events. We scraped top
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source based and content based features along with a
knowledge graph which generates the score and rank the
twitter users according to the scores. Our aim with this
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regards to their impact and influence. We use a technique
that takes into consideration both source based and
content-based features of tweets to generate the ranking of
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1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
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1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
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1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
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Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
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CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: [email protected]
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
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and importance of its presence in the network. In this paper,
we focus on detecting vital nodes which are part of multiple
dense sub-graphs and play a major role in connecting other
nodes thereby forming a global network. Here, we analyze the
data of some major data mining conferences to explore if there
exist some nodes which play a major role in submission and
acceptance of paper in those conferences. We try to figure out
the relationship between the Programme Committee members
and the authors and their co-authors. We look for the top authors
of these conferences. Furthermore, we mine the patterns in which
some authors’ and their co-authors’ papers are being accepted.
In order to understand the underlying dynamics of the network
over time we examine how these patterns change periodically and
with what factors. Lastly, we add to this a comparative study
stating the affiliation of authors and examine if local influence of
their ethnicity does exists with respect to continents where these
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Odsc 2019 entity_reputation_knowledge_graph
- 1. ENTITY CO-OCCURENCE and ENTITY REPUTATION FROM UNSTRUCTURED DATA USING KNOWLEDGE GRAPH By Venkatraman.J Senior data software engineer, Metapack, London
- 2. AGENDA • Motivation • Knowledge graph basic introduction • Problem statement • Data flow architecture • Conclusion
- 3. MOTIVATION • Most of the Data is very unstructured in nature. • Not every problem needs ML/Deep learning models. • Large amount of datasets are connected in this world. • Connected or Linked data provides valuable insights quickly.
- 4. KNOWLEDGE GRAPH • Knowledge graphs encode structured information of entities and their rich relations. • Captures relationship between individual items providing a model and access patterns that can be processed automatically by machines. • Entities are represented using nodes and relationships as edges between entities. • Data representation is also named as triples – (Subject, Predicate, Object) • Very much compared to Ontology where Ontology captures relationships between concepts, data and entities within a particular domain. Eg. Dbpedia, Yago, WordNet. • Use cases powered by knowledge graph – Improving search relevance, Question answering applications, Recommendation engines.
- 6. PROBLEM STATEMENT • Individuals review products on social media. • Actionable insight to find out how the product is doing in market.
- 7. KNOWLEDGE GRAPH CONSTRUCTION • NLP techniques to do Information extraction techniques to extract entities and relationship across entities • Entities are represented using nodes and relationships among entities as edges • Entities in twitter feeds are persons, products and location. • Relationships are likes and dislikes of individual person about a product, relation to other users.
- 8. GRAPH INFERENCE • Centrality algorithms – Degree, Pagerank, Closeness. • Degree centrality – Used for determining popular nodes in the graph. • Degree centrality measures the number of incoming and outgoing relations from a node. Entities that have the highest degree centrality score are considered very popular.
- 10. LEARNING • Tweets are easy to get but quality of data is very poor and too noisy. • Graph querying is not same as SQL. Querying works as pattern matching. Understand the internals of query language is needed to write efficient queries and debug problems. • Understand the data model represented in graph. • Start with small graph and iterate on before building a bigger one. • Neo4j is ACID compliant like RDBMS, watch out for multiple writers writing to Database. • Deploy containerized applications and orchestrate using Docker swarm or Kubernetes to scale up.
- 11. CONCLUSION • Identify the problems that can be solved using graph theory and connected data. • Scoring via Graph model can augment or support the results received from ML/DL models. • Papers related to knowledge graph: http://ceur-ws.org/Vol-2306/paper9.pdf https://aclweb.org/anthology/D18-2024 Questions?