![April 22, 2017 15
Task 1: Finding Patterns from Data
Stream
A Pattern Growth approach : VLDB 2017 [Under Review]](https://image.slidesharecdn.com/icdedesweb2017-170422202733/85/Construction-and-Querying-of-Dynamic-Knowledge-Graphs-15-320.jpg)
Construction and Querying of Dynamic Knowledge Graphs
- 1. NOUS: Construction and Querying of Dynamic Knowledge Graphs SUTANAY CHOUDHURY1, KHUSHBU AGARWAL1, SUMIT PUROHIT1, BAICHUAN ZHANG2, MEG PIRRUNG1, WILLIAM SMITH1, MATHEW THOMAS1 1 : Pacific Northwest National Laboratory, Richland WA 2 : Purdue University, Indianapolis IN
- 2. April 22, 2017 2 Introduction Knowledge Graphs Challenges Motivation (Use Cases) Domain Specific Querying of Web Question-answering for Climate Science Technical Approach NLP, Entity Disambiguation, Relation Learning Frequent Pattern Mining, Question Answering What’s unique Results Status and Future Work Outline
- 3. April 22, 2017 3 Knowledge Graphs: Why do we care? A collection of facts about people, places, things and relationships between them, in a given context I am a scientist. Read papers for me. I am an analyst drowning in data. Help me find interesting events I am a doctor, show me latest breakthrough to consider for this patient.
- 4. April 22, 2017 4 KG: Construction and Analytical Challenges Construction No Base KB Natural Language Processing is inherently noisy Unseen entities and relationships How do we determine its class Should we include every new relation and entity in KB? Analysis Identifying target set of user questions and ontology needed to answer them. Mapping user questions to graph analytical tasks Executing analytical tasks at scale Pattern discovery: What’s emerging and what’s fading? Question Answering: Entity based Querying (What, Who, When, Where) Hypothesis Generation (Why)
- 5. April 22, 2017 5 Introduction Knowledge Graphs Challenges Motivation (Use Cases) Domain Specific Querying of Web Question-answering for Climate Science Technical Approach NLP, Entity Disambiguation, Relation Learning Frequent Pattern Mining, Question Answering What’s unique Results Status and Future Work Outline
- 6. April 22, 2017 6 Use Case : Domain-specific Querying of the Web Input: multi-month web crawls obtained using domain expert suggestions Analytic questions: Find top manufacturers and model names Find components and features Who is popular? What are new releases? How are companies and components related? How is country and product related?
- 7. Question-answering for Climate Science DOE’s ARM program wants to make focused investments on experimental campaigns Monitor scientific literature to find which campaigns or instruments or data products are being cited Our target problems: "What are the papers written on __aerosols______?" "What datasets are used for ___aerosols______ publications?” “What primary measurements are represented in the subset of aerosol publications?” "What instruments are represented in the aerosol publications?” “What sites are most represented in the aerosol publications”? April 22, 2017 7
- 8. April 22, 2017 8 NOUS Motivation Tasks involved in building KB are not domain specific Most questions can be mapped to a set of common graph analytical tasks. Tell me about X Tell me about X in context of Y What are recent trends about X How are X and Y related Why did X do Y
- 9. April 22, 2017 9 Introduction Knowledge Graphs Challenges Motivation (Use Cases) Domain Specific Querying of Web Question-answering for Climate Science Technical Approach NLP, Entity Disambiguation, Relation Learning Frequent Pattern Mining, Question Answering What’s unique Results Status and Future Work Outline
- 10. 10 NOUS Workflow Knowledge Graph Model Streaming Data Knowledge Graph Tasks • Continuous pattern discovery. • Question Answering Knowledge Graph Verification• Natural Language processing • Collective Entity Linking • Distant supervision based relationship discovery Base KB + Streaming Data Bayesian Personalized Ranking-based link prediction to assign confidence Protected Information | Proprietary Information Apache Spark
- 11. Triple Extraction from Natural Language April 22, 2017 11 the United States government crack an iPhone that belonged to a gunman in the San Bernardino Apple repair the particular iPhone hole that the government hacked. Federal officials specify the procedure used to open the iPhone Federal officials deny to specify the procedure used to open the iPhone. Jay Kaplan, chief executive of the tech securAppley company Synack and a former National Security Agency analyst. say Apple has to earn the trust of Apples customers,” the F.B.I. crack Mr. Farook’s LegbaCore, which previously found and fixed flaws for Apple. find flaws for Apple. LegbaCore, which previously found and fixed flaws for Apple. fix flaws for Apple. The challenge: turning this into a high quality representation Use existing tools Stanford Core NLP Open IE
- 12. April 22, 2017 12 Entity Disambiguation and Relation Learning Implements Collective Entity Linking from Han et al, SIGIR 2011 Key idea: Search the graph with matches for all the above terms, build a mesh of terms and their related terms and pick the most densely connected combination Out of box performance low (higher 60%), when combined with domain-specific rules (85-90%+) Han et al in "Collective Entity Linking in Web Text: A Graph- based Method, SIGIR 2011" Relation Learning Implements Distance Supervision
- 13. April 22, 2017 13 Now we have a Graph!
- 14. 14 A Different Approach to Querying Let’s not demand users learn SQL or SPARQL Think in plain English, and we will transparently translate queries in background Protected Information | Proprietary Information
- 15. April 22, 2017 15 Task 1: Finding Patterns from Data Stream A Pattern Growth approach : VLDB 2017 [Under Review]
- 16. Task 1: Finding Patterns from Data Stream (Contd.) April 22, 2017 16 We discover behavioral patterns of drone related entities (WSJ, 2010-2015) Early in the period Towards the end Patterns help us understand the shift in a domain, and discover new trends
- 17. Task 2: Question Answering for Relationship Explanations April 22, 2017 17 Our goal is to learn “patterns of explanations” by walking the graph structure Given a new question and the knowledge of such patterns, we can generalize and answer questions about unseen entities Examples: Why would Ford buy Palladium? Ford is-a automotive company, automotive company has-part catalytic converters, catalytic converters has-material Palladium Answer pattern: A is-a company B, B has-part C, C has-dependency D Why would Sutanay visit SFO in August 15? Sutanay has-interest Machine-Learning, SIGKDD related-to Machine-Learning, SIGKDD has-location SFO, SIGKDD has-start-date August 13
- 18. Results – Explanatory Paths using coherence April 22, 2017 18
- 19. April 22, 2017 19 Use Case Results: Domain-specific Querying of the Web Input Size: 2 million+ webpages Graph construction: 64-node cluster, each node with 16 cores Graph Analytics: 16-node Spark/Hadoop cluster, each with 16 cores
- 20. April 22, 2017 20 Picking up real events by Patterns
- 21. April 22, 2017 21 What’s Phenomenal Here? We are data scientists, not drone experts We processed nearly two million web crawls with minimal custom code and built the Knowledge Graph Analyzed trends, connected dots across multiple source and presented a hypothesis: Is tracking autonomous drones and their use important? Starting with the data analysis to coming up with the question : took less than 1.5 hours
- 22. 22 Key Takeaways Open Source KB Construction and Querying Pipeline https://github.com/streaming-graphs/NOUS Version 1.0: Support for extracting standard entities, relation extraction via distant supervision, Advanced trending and explanatory questions The ability to answer queries where the answer is embedded across multiple data sources All algorithms implemented on top of Apache Spark and Scala Protected Information | Proprietary Information
- 23. April 22, 2017 23 Status and Future Work We are committed towards developing an open source community A MySQL for Knowledge Graphs! Preferred method of payment J Add test code (in Scala or Python) Break the code AND show an important class of problem Version 2.0: Improving KB quality, Information maintenance over time, Initial support for human-computer interaction. Incorporate algorithms for latest advances in NLP, ED
- 24. April 22, 2017 24 Questions?