naive reasoning on rdf streams
play

Naive reasoning on RDF streams Emanuele Della Valle - PowerPoint PPT Presentation

Aug 17, 2023 •178 likes •575 views

Stream Reasoning For Linked Data M. Balduini, J-P Calbimonte, O. Corcho, D. Dell'Aglio, E. Della Valle, and J.Z. Pan http://streamreasoning.org/sr4ld2013 Naive reasoning on RDF streams Emanuele Della Valle emanuele.dellavalle@polimi.it

  1. Stream Reasoning For Linked Data M. Balduini, J-P Calbimonte, O. Corcho, D. Dell'Aglio, E. Della Valle, and J.Z. Pan http://streamreasoning.org/sr4ld2013 Naive reasoning on RDF streams Emanuele Della Valle emanuele.dellavalle@polimi.it http://emanueledellavalle.org

  2. Share, Remix, Reuse — Legally § This work is licensed under the Creative Commons Attribution 3.0 Unported License. § Your are free: • to Share — to copy, distribute and transmit the work • to Remix — to adapt the work § Under the following conditions • Attribution — You must attribute the work by inserting – “ [source http://streamreasoning.org/sr4ld2013] ” at the end of each reused slide – a credits slide stating - These slides are partially based on “ Streaming Reasoning for Linked Data 2013 ” by M. Balduini, J-P Calbimonte, O. Corcho, D. Dell'Aglio, E. Della Valle, and J.Z. Pan http://streamreasoning.org/sr4ld2013 § To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/ 2 http://streamreasoning.org/sr4ld2013

  3. Agenda § Forms of reasoning for Q/A § Naïve forms of stream reasoning for Q/A § A not so naïve form of stream reasoning for Q/A § Wrap up 3 http://streamreasoning.org/sr4ld2013

  4. MEMO: forms of reasoning for Q/A Data-driven (a.k.a. forward reasoning) § RDF Inferred Reasoner SPARQL data data ontology Query-driven – backward reasoning § RDF Reasoner SPARQL data ontology Query-driven – query rewriting (a.k.a. ontology based data access) § Rewritten SPARQL Reasoner data query ontology 4 http://streamreasoning.org/sr4ld2013

  5. Naïve Stream Reasoning Data-driven (a.k.a. forward reasoning) § RDF Inferred S2R Reasoner SPARQL data data ontology Query-driven – backward reasoning § RDF S2R Reasoner SPARQL data ontology Query-driven – query rewriting (a.k.a. ontology based data access) § Rewritten SPARQL Reasoner data S2R query ontology 5 http://streamreasoning.org/sr4ld2013

  6. Example of Stream Reasoning 1/2 § Query: measure the the impact of Alice's microposts MEMO: our running example data model Post discusses § For example Bob posts p 2 . discusses ¡ discusses ¡ p 2 ¡ p 4 ¡ p 7 ¡ discusses ¡ discusses ¡ discusses ¡ discusses ¡ p 1 ¡ p 3 ¡ p 5 ¡ p 8 ¡ discusses ¡ p 6 ¡ Alice posts p 1 . 50 ¡min ¡ago ¡ 40 ¡min ¡ago ¡ 30 ¡min ¡ago ¡ 20 ¡min ¡ago ¡ 10 ¡min ¡ago ¡ now ¡ 6 http://streamreasoning.org/sr4ld2013

  7. Example of Stream Reasoning 2/2 What impact has been my micropost p 1 creating in the last hour? Let’s count the number of microposts that discuss it … REGISTER STREAM ImpactMeter AS SELECT (count(?p) AS ?impact) FROM STREAM <http://…/fb> [RANGE 60m STEP 10m] WHERE { :Alice posts [ sr:discusses ?p ] } discusses ¡ discusses ¡ p 2 ¡ p 4 ¡ p 7 ¡ discusses ¡ Transitive property discusses ¡ discusses ¡ discusses ¡ 7! p 1 ¡ p 3 ¡ p 5 ¡ p 8 ¡ Alice posts p 1 . p 6 ¡ discusses ¡ 7 http://streamreasoning.org/sr4ld2013

  8. Naïve data-driven stream reasoning § Memo RDF Inferred S2R Reasoner SPARQL data data 0 ontology p 1 ¡ now ¡ 8 http://streamreasoning.org/sr4ld2013

  9. Naïve data-driven stream reasoning § Memo RDF Inferred S2R Reasoner SPARQL data data 1 ontology p 2 ¡ discusses ¡ p 1 ¡ 10 ¡min ¡ago ¡ now ¡ 9 http://streamreasoning.org/sr4ld2013

  10. Naïve data-driven stream reasoning § Memo RDF Inferred S2R Reasoner SPARQL data data 2 ontology p 2 ¡ discusses ¡ discusses ¡ p 1 ¡ p 3 ¡ 20 ¡min ¡ago ¡ 10 ¡min ¡ago ¡ now ¡ 10 http://streamreasoning.org/sr4ld2013

  11. Naïve data-driven stream reasoning § Memo RDF Inferred S2R Reasoner SPARQL data data 3 ontology discusses ¡ p 2 ¡ p 4 ¡ discusses ¡ discusses ¡ p 1 ¡ p 3 ¡ 30 ¡min ¡ago ¡ 20 ¡min ¡ago ¡ 10 ¡min ¡ago ¡ now ¡ 11 http://streamreasoning.org/sr4ld2013

  12. Naïve data-driven stream reasoning § Memo RDF Inferred S2R Reasoner SPARQL data data 5 ontology The entire discusses ¡ p 2 ¡ p 4 ¡ inference process discusses ¡ is repeated each time the SR2 discusses ¡ discusses ¡ p 1 ¡ p 3 ¡ p 5 ¡ operator delivers new RDF data discusses ¡ p 6 ¡ 40 ¡min ¡ago ¡ 30 ¡min ¡ago ¡ 20 ¡min ¡ago ¡ 10 ¡min ¡ago ¡ now ¡ now ¡ 12 http://streamreasoning.org/sr4ld2013

  13. Naïve data-driven stream reasoning § Memo RDF Inferred S2R Reasoner SPARQL data data 7 ontology discusses ¡ discusses ¡ p 2 ¡ p 4 ¡ p 7 ¡ discusses ¡ discusses ¡ discusses ¡ discusses ¡ p 1 ¡ p 3 ¡ p 5 ¡ p 8 ¡ discusses ¡ p 6 ¡ 50 ¡min ¡ago ¡ 40 ¡min ¡ago ¡ 30 ¡min ¡ago ¡ 20 ¡min ¡ago ¡ 10 ¡min ¡ago ¡ now ¡ 13 http://streamreasoning.org/sr4ld2013

  14. Naïve data-driven stream reasoning § Memo RDF Inferred S2R Reasoner SPARQL data data 7 ontology discusses ¡ discusses ¡ p 2 ¡ p 4 ¡ p 7 ¡ discusses ¡ discusses ¡ discusses ¡ discusses ¡ p 1 ¡ p 3 ¡ p 5 ¡ p 8 ¡ p 6 ¡ discusses ¡ 50 ¡min ¡ago ¡ 40 ¡min ¡ago ¡ 30 ¡min ¡ago ¡ 20 ¡min ¡ago ¡ 10 ¡min ¡ago ¡ now ¡ 14 http://streamreasoning.org/sr4ld2013

  15. Naïve data-driven stream reasoning § Memo RDF Inferred S2R Reasoner SPARQL data data 0 ontology The reasoner discusses ¡ discusses ¡ p 2 ¡ p 4 ¡ p 7 ¡ infers data that is irrelevant to query answering discusses ¡ discusses ¡ p 3 ¡ p 5 ¡ p 8 ¡ discusses ¡ p 6 ¡ 50 ¡min ¡ago ¡ 40 ¡min ¡ago ¡ 30 ¡min ¡ago ¡ 20 ¡min ¡ago ¡ 10 ¡min ¡ago ¡ now ¡ 15 http://streamreasoning.org/sr4ld2013

  16. Naïve query-driven (backward) stream reasoning § Memo RDF S2R Reasoner SPARQL data 0 ontology p 1 ¡ now ¡ 16 http://streamreasoning.org/sr4ld2013

  17. Naïve query-driven (backward) stream reasoning § Memo RDF Inferred S2R Reasoner SPARQL data data 1 ontology p 2 ¡ discusses ¡ p 1 ¡ 10 ¡min ¡ago ¡ now ¡ 17 http://streamreasoning.org/sr4ld2013

  18. Naïve query-driven (backward) stream reasoning § Memo RDF Inferred S2R Reasoner SPARQL data data 2 ontology p 2 ¡ discusses ¡ discusses ¡ p 1 ¡ p 3 ¡ 20 ¡min ¡ago ¡ 10 ¡min ¡ago ¡ now ¡ 18 http://streamreasoning.org/sr4ld2013

  19. Naïve query-driven (backward) stream reasoning § Memo RDF Inferred S2R Reasoner SPARQL data data 3 ontology discusses ¡ p 2 ¡ p 4 ¡ discusses ¡ discusses ¡ p 1 ¡ p 3 ¡ 30 ¡min ¡ago ¡ 20 ¡min ¡ago ¡ 10 ¡min ¡ago ¡ now ¡ 19 http://streamreasoning.org/sr4ld2013

  20. Naïve query-driven (backward) stream reasoning § Memo RDF Inferred S2R Reasoner SPARQL data data 5 ontology The entire discusses ¡ p 2 ¡ p 4 ¡ inference process discusses ¡ is repeated each time the SR2 discusses ¡ discusses ¡ p 1 ¡ p 3 ¡ p 5 ¡ operator delivers new RDF data discusses ¡ p 6 ¡ 40 ¡min ¡ago ¡ 30 ¡min ¡ago ¡ 20 ¡min ¡ago ¡ 10 ¡min ¡ago ¡ now ¡ now ¡ 20 http://streamreasoning.org/sr4ld2013

  21. Naïve query-driven (backward) stream reasoning § Memo RDF Inferred S2R Reasoner SPARQL data data 7 ontology discusses ¡ discusses ¡ p 2 ¡ p 4 ¡ p 7 ¡ discusses ¡ discusses ¡ discusses ¡ discusses ¡ p 1 ¡ p 3 ¡ p 5 ¡ p 8 ¡ discusses ¡ p 6 ¡ 50 ¡min ¡ago ¡ 40 ¡min ¡ago ¡ 30 ¡min ¡ago ¡ 20 ¡min ¡ago ¡ 10 ¡min ¡ago ¡ now ¡ 21 http://streamreasoning.org/sr4ld2013

  22. Naïve query-driven (backward) stream reasoning § Memo RDF Inferred S2R Reasoner SPARQL data data 7 ontology discusses ¡ discusses ¡ p 2 ¡ p 4 ¡ p 7 ¡ discusses ¡ discusses ¡ discusses ¡ discusses ¡ p 1 ¡ p 3 ¡ p 5 ¡ p 8 ¡ p 6 ¡ discusses ¡ 50 ¡min ¡ago ¡ 40 ¡min ¡ago ¡ 30 ¡min ¡ago ¡ 20 ¡min ¡ago ¡ 10 ¡min ¡ago ¡ now ¡ 22 http://streamreasoning.org/sr4ld2013

  23. Naïve query-driven (backward) stream reasoning § Memo RDF Inferred S2R Reasoner SPARQL data data 0 ontology The backward discusses ¡ discusses ¡ p 2 ¡ p 4 ¡ p 7 ¡ reasoner would not even start! discusses ¡ discusses ¡ p 3 ¡ p 5 ¡ p 8 ¡ discusses ¡ p 6 ¡ 50 ¡min ¡ago ¡ 40 ¡min ¡ago ¡ 30 ¡min ¡ago ¡ 20 ¡min ¡ago ¡ 10 ¡min ¡ago ¡ now ¡ 23 http://streamreasoning.org/sr4ld2013

  24. Naïve query-driven stream reasoning by query rewriting § MEMO Rewritten SPARQL Reasoner data S2R query ontology § It is not that straight forward :-( • Lack of a standard query language for DSMS and CEP • Lack of a well-understood operational semantics for DSMS and CEP (cf. SECRET by I. Botan et al., PVLDB 3(1), 2010) • Lack of expressiveness in OWL2QL – Temporal reasoning – Aggregates – Functions 24 http://streamreasoning.org/sr4ld2013

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

STAT 339 Naive Bayes Classification 8-10 March 2017 Colin Reimer Dawson Outline Naive Bayes

STAT 339 Naive Bayes Classification 8-10 March 2017 Colin Reimer Dawson Outline Naive Bayes

STAT 339 Naive Bayes Classification 8-10 March 2017 Colin Reimer Dawson Outline Naive Bayes Classification Naive Bayes Classifier Using MLE Parameter Estimates Bayesian Naive Bayes Continuous Features Mixed Feature Types Back to Supervised

530 views • 21 slides
CSE 143 Streams as C++ Classes Streams are C++ classes Streams have lots of built-in

CSE 143 Streams as C++ Classes Streams are C++ classes Streams have lots of built-in

CSE 143 Streams as C++ Classes Streams are C++ classes Streams have lots of built-in methods We use the . syntax to access member More Stream I/O functions, as usual. inFile.get(ch); // get a character outFile.put(ch); //

209 views • 3 slides
Introduction and RDF streams Daniele DellAglio dellaglio@ifi.uzh.ch http://dellaglio.org

Introduction and RDF streams Daniele DellAglio dellaglio@ifi.uzh.ch http://dellaglio.org

How to Build a Stream Reasoning Application D. Dell'Aglio, E. Della Valle, T. Le-Pham, A. Mileo, and R. Tommasini http://streamreasoning.org/events/streamapp2017 Introduction and RDF streams Daniele DellAglio dellaglio@ifi.uzh.ch

708 views • 21 slides
Introduction to Machine Learning Classification: Naive Bayes Learning goals 15 Understand the

Introduction to Machine Learning Classification: Naive Bayes Learning goals 15 Understand the

Introduction to Machine Learning Classification: Naive Bayes Learning goals 15 Understand the idea of Naive 10 Response Bayes x2 a b 5 Understand in which sense 0 Naive Bayes is a special QDA 0 5 10 15 x1 model NAIVE BAYES

317 views • 6 slides
Towards a Logic-Based Framework for Analyzing Stream Reasoning Harald Beck Minh Dao-Tran Thomas

Towards a Logic-Based Framework for Analyzing Stream Reasoning Harald Beck Minh Dao-Tran Thomas

Towards a Logic-Based Framework for Analyzing Stream Reasoning Harald Beck Minh Dao-Tran Thomas Eiter Michael Fink 3rd International Workshop on Ordering and Reasoning October 20, 2014 Motivation Streams Logical Framework Conclusions

547 views • 52 slides
PLUGIN CLASSIFIERS: NAIVE BAYES, LDA, PLUGIN CLASSIFIERS: NAIVE BAYES, LDA, LOGISTIC REGRESSION

PLUGIN CLASSIFIERS: NAIVE BAYES, LDA, PLUGIN CLASSIFIERS: NAIVE BAYES, LDA, LOGISTIC REGRESSION

PLUGIN CLASSIFIERS: NAIVE BAYES, LDA, PLUGIN CLASSIFIERS: NAIVE BAYES, LDA, LOGISTIC REGRESSION LOGISTIC REGRESSION Matthieu R Bloch Tuesday, January 28, 2020 1 LOGISTICS LOGISTICS TAs and Office hours Monday: Mehrdad (TSRB 523a) -

261 views • 11 slides
CHAPTER-4 1 LOGIC AND REASONING ! Knowledge and ! Reasoning in Knowledge- Reasoning Based

CHAPTER-4 1 LOGIC AND REASONING ! Knowledge and ! Reasoning in Knowledge- Reasoning Based

CHAPTER-4 1 LOGIC AND REASONING ! Knowledge and ! Reasoning in Knowledge- Reasoning Based Systems ! syntax and semantics ! shallow and deep reasoning ! validity and satisfiability ! forward and backward ! logic languages chaining ! alternative

616 views • 23 slides
Stream Bank Stabilization in Open Space Streams in open space There are approximately 35

Stream Bank Stabilization in Open Space Streams in open space There are approximately 35

Stream Bank Stabilization in Open Space Streams in open space There are approximately 35 miles of streams that flow through Open Space. The streams range in size from small head water streams to the Middle Patuxent River. Streams

349 views • 30 slides
Probabilistic Reasoning; Probabilistic Reasoning; Network-based reasoning Network-based

Probabilistic Reasoning; Probabilistic Reasoning; Network-based reasoning Network-based

Probabilistic Reasoning; Probabilistic Reasoning; Network-based reasoning Network-based reasoning COMPSCI 276, Fall 2014 Set 1: Introduction and Background Rina Dechter (Reading: Pearl chapter 1-2, Darwiche chapters 1,3) 1 Why/What/How

700 views • 45 slides
Evidential and Causal Reasoning Much reasoning in AI can be seen as evidential reasoning ,

Evidential and Causal Reasoning Much reasoning in AI can be seen as evidential reasoning ,

Evidential and Causal Reasoning Much reasoning in AI can be seen as evidential reasoning , (observations to a theory) followed by causal reasoning (theory to predictions). Diagnosis Given symptoms, evidential reasoning leads to hypotheses about

243 views • 8 slides
Data Streams Many large sources of data are generated as streams of updates: IP Network

Data Streams Many large sources of data are generated as streams of updates: IP Network

Summarizing and Mining Skewed Data Streams Graham Cormode cormode@bell-labs.com Flip Korn, S. Muthukrishnan, Divesh Srivastava Data Streams Many large sources of data are generated as streams of updates: IP Network traffic data Text:

485 views • 44 slides
Fault-Tolerant Resource Reasoning Gian Ntzik , Pedro da Rocha Pinto and Philippa Gardner Imperial

Fault-Tolerant Resource Reasoning Gian Ntzik , Pedro da Rocha Pinto and Philippa Gardner Imperial

Fault-Tolerant Resource Reasoning Gian Ntzik , Pedro da Rocha Pinto and Philippa Gardner Imperial College London { gn408,pmd09,pg } @imperial.ac.uk January 13, 2016 1/22 Example: A naive bank transfer widthdraw ( from , amount ); deposit ( to ,

557 views • 24 slides
CSE 473: Artificial Intelligence Autumn 2010 Machine Learning: Naive Bayes and Perceptron Luke

CSE 473: Artificial Intelligence Autumn 2010 Machine Learning: Naive Bayes and Perceptron Luke

CSE 473: Artificial Intelligence Autumn 2010 Machine Learning: Naive Bayes and Perceptron Luke Zettlemoyer Many slides over the course adapted from Dan Klein. 1 Outline Learning: Naive Bayes and Perceptron Naive Bayes models

837 views • 57 slides
Data Streams Many large sources of data are generated as streams of updates: IP Network

Data Streams Many large sources of data are generated as streams of updates: IP Network

Summarizing and Mining Skewed Data Streams Graham Cormode cormode@bell-labs.com S. Muthukrishnan muthu@cs.rutgers.edu Data Streams Many large sources of data are generated as streams of updates: IP Network traffic data Text: email/

535 views • 20 slides
SECTION 1: Introductions Code Reasoning Forward Reasoning CODE REASONING +

SECTION 1: Introductions Code Reasoning Forward Reasoning CODE REASONING +

OUTLINE SECTION 1: Introductions Code Reasoning Forward Reasoning CODE REASONING + Backward Reasoning Weaker vs. Stronger statements Version control VERSION CONTROL CSE 331 Summer 2018 slides borrowed and

763 views • 11 slides
Streams and File I/O Fundamentals of Computer Science Outline Overview of Streams and File

Streams and File I/O Fundamentals of Computer Science Outline Overview of Streams and File

Streams and File I/O Fundamentals of Computer Science Outline Overview of Streams and File I/O Buffering Text File I/O Binary File I/O Streams Stream : an object that either delivers data to its destination (screen, file,

770 views • 33 slides
Kotlin/Native concurrency model nikolay igotti@JetBrains What do we want from concurrency?

Kotlin/Native concurrency model nikolay igotti@JetBrains What do we want from concurrency?

Kotlin/Native concurrency model nikolay igotti@JetBrains What do we want from concurrency? Do many things concurrently Easily offload tasks Get notified once task a task is done Share state safely Mutate state safely

268 views • 23 slides
USER PERCEPTION OF DELETING INSTANT MESSAGES EuroUSEC18, London, UK, 23 April 2018 Theodor

USER PERCEPTION OF DELETING INSTANT MESSAGES EuroUSEC18, London, UK, 23 April 2018 Theodor

USER PERCEPTION OF DELETING INSTANT MESSAGES EuroUSEC18, London, UK, 23 April 2018 Theodor Schnitzler, Christine Utz, Florian M. Farke, Christina Ppper, Markus Drmuth OVERVIEW Instant Messaging New WhatsApp feature introduced October

202 views • 17 slides
Analyzing the Impact of GDPR on Storage Systems Aashaka Shah, Vinay Banakar, Supreeth Shastri

Analyzing the Impact of GDPR on Storage Systems Aashaka Shah, Vinay Banakar, Supreeth Shastri

Analyzing the Impact of GDPR on Storage Systems Aashaka Shah, Vinay Banakar, Supreeth Shastri Melissa Wasserman and Vijay Chidambaram General Data Protection Regulation (GDPR) May 25, 2018 Fundamental right Adopted after 2 years of public debate.

168 views • 16 slides
Goals SQL: Data Definition Language CREATE ALTER DROP SQL: Data Manipulation

Goals SQL: Data Definition Language CREATE ALTER DROP SQL: Data Manipulation

IT360: Applied Database Systems SQL: Structured Query Language DDL and DML (w/o SELECT) (Chapter 7 in Kroenke) 1 Goals SQL: Data Definition Language CREATE ALTER DROP SQL: Data Manipulation Language INSERT DELETE

293 views • 7 slides
Fundamental Algorithms Chapter 9: Hash Tables Dirk Pfl uger Winter 2010/11 D. Pfl uger:

Fundamental Algorithms Chapter 9: Hash Tables Dirk Pfl uger Winter 2010/11 D. Pfl uger:

Technische Universit at M unchen Fundamental Algorithms Chapter 9: Hash Tables Dirk Pfl uger Winter 2010/11 D. Pfl uger: Fundamental Algorithms Chapter 9: Hash Tables, Winter 2010/11 1 Technische Universit at M unchen

247 views • 20 slides
Performance Improvement of Btrfs Miao Xie &lt;miaox@cn.fujitsu.com&gt; Li Zefan

Performance Improvement of Btrfs Miao Xie &lt;miaox@cn.fujitsu.com&gt; Li Zefan

Performance Improvement of Btrfs Miao Xie &lt;miaox@cn.fujitsu.com&gt; Li Zefan &lt;lizf@cn.fujitsu.com&gt; Agenda Comparison between Btrfs and Ext3/4 Issue analysis (We have investigated) Small file sequential read Large file

785 views • 36 slides
Deleting an edge 5. Deletioncontraction and graph polynomials For a graph G and e E ,

Deleting an edge 5. Deletioncontraction and graph polynomials For a graph G and e E ,

S-72.2420 / T-79.5203 The deletioncontraction algorithm and graph polynomials 1 S-72.2420 / T-79.5203 The deletioncontraction algorithm and graph polynomials 3 Deleting an edge 5. Deletioncontraction and graph

199 views • 9 slides
UNVEIL: A Large-Scale, Automated Approach to Detecting Ransomware Paul Weliczko

UNVEIL: A Large-Scale, Automated Approach to Detecting Ransomware Paul Weliczko

UNVEIL: A Large-Scale, Automated Approach to Detecting Ransomware Paul Weliczko https://arstechnica.com/information-technology/2012/11/mushrooming-growth-of-ransomware- extorts-5-million-a-year/

298 views • 13 slides

More recommend