Building, Growing and Serving Large Knowledge Graphs with Human-in-the-Loop

1. Yunyao Li HILDA @ SIGMOD’23 | Apple Inc. | June 18, 2023 Building, Growing and Serving Large Knowledge Graphs with Human-in-the-Loop

2. Knowledge Bases Image Source: https://www.csee.umbc.edu/courses/graduate/691/fall22/kg/

3. Example: Financial Content Knowledge Base Financial Reports Ontology [VLDB’2017] Creation and Interaction with Large-scale Domain-Specific Knowledge Bases. XML Knowledge Extraction Overall Architecture: A Simplified View Linking Fusion KG Construction Transforming >31,000 companies 439 industries ~170,000 insiders ~100 millions financial metrics ~22,000 industry KPIs Financial Content KB KG Services QA APIs

4. Example: Saga Structured Knowledge Sources Real-time Sources Ontology Unstructured Knowledge Sources Linking Fusion KG Construction KG Knowledge Extraction KG Services QA Semantic Annotation … … Embedding Services [SIGMOD’23] Growing and Serving Large Open-domain Knowledge Graphs. [SIGMOD’22] Saga: A Platform for Continuous Construction and Serving of Knowledge at Scale Transforming Overall Architecture: A Simplified View

5. Human-in-the-Loop Throughout the Entire Life Cycle of KG Construction, Growth, and Services Data Labeling Development Deployment

6. Human-in-the-Loop Throughout the Entire Life Cycle of KG Construction, Growth, and Services Data Labeling Development Deployment Learner raw data labeled data 1. Improve quality 2. Increase efficiency 3. Decrease skill requirements

7. Example 1: Scale Fact Collection Missing / stale facts Missing Facts Query Synthesizer QA System candidate facts Baseline New Facts

8. Example 1: Scale Fact Collection Missing / stale facts Missing Facts Query Synthesizer QA System candidate facts Baseline New Facts Query-by-Committee Missing Facts Query Synthesizer QA System candidate facts New Facts QA System Q1 QA System … … … … … Qn QbC Selector AnswerSet1 AnswerSetn [EMNLP-DaSH’2022] Improving Human Annotation Effectiveness for Fact Collection by Identifying the Most Relevant Answers Success Rate fact collection 25%

9. Example 1: Scale Fact Collection Missing / stale facts Missing Facts Query Synthesizer QA System candidate facts Baseline New Facts Open Domain Knowledge Extraction [SIGMOD’23] Growing and Serving Large Open-domain Knowledge Graphs. Throughput vs. manual fact collection >100x Missing Facts Query Synthesizer Web Search candidate facts w/ lower-confidence New Facts Knowledge Extractor Fact Corroboration

10. Example 2: Semantic Role Labeling SRL

11. Example 2a: Crowd-in-the-Loop Curation An hybrid approach Corpus raw data Corpus predicated annotations Annotation Task Corpus curated annotations Task Router Difficult tasks are curated by experts Easier tasks are curated by crowd [EMNLP’17] CROWD-IN-THE-LOOP: A Hybrid Approach for Annotating Semantic Roles

12. Example 2a: Effectiveness of Crowd-in-the-Loop 9% F1 vs. SRL model ↑ 66.4% Expert efforts ↓

13. Example 2b: Better Workflow Performs Ever Better vs. SRL model ↑ Expert efforts ↓ 10% F1 vs. SRL model ↑ 87.3% Expert efforts ↓ Filter unlikely options Select from likely options Expert resolve hard cases [EMNLP’20 (Finding)] A Novel Workflow for Accurately and Efficiently Crowdsourcing Predicate Senses and Argument Labels

14. Human-in-the-Loop Throughout the Entire Life Cycle of KG construction, growth, and services Data Labeling Development Deployment Learner Scale data labeling raw data labeled data IDE Better IDE for model building

15. Different Tooling for Different Users Full-fledged IDE AI Engineers AI Engineers/Data Scientists Visual IDE [ACL’12] WizIE: A Best Practices Guided Development Environment for Information Extraction [CHI’13] I can do text analytics!: designing development tools for novice developers [VLDB’15] VINERy: A Visual IDE for Information Extraction [KDD’19] Declarative Text Understanding with SystemT. (hands-on tutorial) Entity Extraction in AIOps https://www.ibm.com/cloud/blog/entity-extraction-in-aiops IBM InfoSphere BigInsights Text Analytics Eclipse Tooling IBM Watson Knowledge Studio. Advanced Rule Editor http://ibm.biz/VineryIE

16. Human-in-the-Loop Throughout the Entire Life Cycle of KG construction, growth, and services Data Labeling Development Deployment Learner Scale data labeling raw data labeled data IDE Better IDE for model building Learner Human-machine co-creation

17. Transparent Linguistic Models for Contract Understanding Watson Discovery Content Intelligence [NAACL’21] Development of an Enterprise-Grade Contract Understanding System, (Industry Track)

18. HEIDL: Human & Machine Co-Creation via Neural-Symbolic AI [ACL’19] HEIDL: Learning Linguistic Expressions with Deep Learning and Human-in-the-Loop. [EMNLP’20] Learning Explainable Linguistic Expressions with Neural Inductive Logic Programming for Sentence Classification In use for major IBM customer engagements Raises the abstraction level for domain experts to interact with

19. HEIDL: Human & Machine Co-Creation via Neural-Symbolic AI [ACL’19] HEIDL: Learning Linguistic Expressions with Deep Learning and Human-in-the-Loop. [EMNLP’20] Learning Explainable Linguistic Expressions with Neural Inductive Logic Programming for Sentence Classification In use for major IBM customer engagements Raises the abstraction level for domain experts to interact with

20. Human-in-the-Loop Throughout the Entire Life Cycle of KG construction, growth, and services Data Labeling Development Deployment Learner Scale data labeling raw data labeled data IDE Better IDE for model building Learner Human-machine co-creation Learner Curb data hunger with interactive learning

21. Case 1: Example-Driven Extraction Via pattern induction [CHI’17] SEER: Auto-Generating Information Extraction Rules from User-Specified Examples [SIGMOD’17] Synthesizing Extraction Rules from User Examples with SEER. SIGMOD’2017 [AAAI’22 (demo)] InteractEva: A Simulation-based Evaluation Framework for Interactive AI Systems [AAAI’22] A Simulation-Based Evaluation Framework for Interactive AI Systems and Its Application. IBM Watson Discovery (Beta in Plus since Oct. 2021) http://ibm.biz/SEER_IE, https://ibm.biz/WDSPressReleaseNov

25. Case 2: Entity Normalization & Variant Generation Learning Structured Representations Capture Entity Semantic Structure [COLING’2018] Exploiting Structure in Representation of Named Entities using Active Learning. [ICDE’2018] LUSTRE: An Interactive System for Entity Structured Representation and Variant Generation. Generated normalizers for Watson Discovery [AAAI’2020] PARTNER: Human-in-the-Loop Entity Name Understanding with Deep Learning. [EMNLP’2020] Learning Structured Representations of Entity Names using Active Learning and Weak Supervision. “Bank of America N.A.” “Bank of America National Association” Synthesizing Normalization and Variant Generation Functions

26. Case 3: Deep Document Understanding Document Ingestion [WACV 2021] Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Context. [AAAI’21] KAAPA: Knowledge Aware Answers from PDF Analysis. [ACL-CORD-19’21] CORD-19: The COVID-19 Open Research Dataset Bringing IBM NLP capabilities to the CORD-19 Dataset. http://ibm.biz/CORD19-IBM IBM Watson Discovery JSON/HTML Wide Variety in PDF Tables Table with graphic lines Table with visual clues only Complex table with multi-row/column headers Table interleaved with text and charts

27. Case 3: TableLab TableLab: Easy Customization via Adaptive Deep Learning [IUI’2021] TableLab: An Interactive Table Extraction System with Adaptive Deep Learning.

28. Case 3: Deep Document Understanding TableLab: Easy Customization via Adaptive Deep Learning [IUI’2021] TableLab: An Interactive Table Extraction System with Adaptive Deep Learning.

29. Case 3: Customization vis TableLab Table Boundary Detection Preliminary Results Method CEDAR EDGAR Invoices Appraisals Health Docs GTE 0.94 0.84 0.47 0.85 0.93 GTE with Retraining 0.96 0.91 0.92 0.96 0.98 Method CEDAR EDGAR Invoices Appraisals Health Docs GTE 0.88 0.62 0.42 0.71 0.55 GTE with Retraining 0.90 0.82 0.68 0.90 0.77 Cell Adjacency Detection Dataset 20 pages with tables per category: 10 for retraining, 10 for testing Evaluation Metric F1 metric for Table Boundary and Cell Adjacency as de fi ned in [1] [1] Göbel et al. “A Methodology for Evaluating Algorithms for Table Understanding in PDF Documents”. DocEng '12

30. Case 4: Label Sleuth An open-source no-code system for text annotation and building text classifiers [EMNLP’2022] Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours https://www.label-sleuth.org 1. From task definition to working model in hours! 2. Extensible backend to integrate new model architectures or active learning techniques

31. Human-in-the-Loop Throughout the Entire Life Cycle of KG construction, growth, and services Data Labeling Development Deployment Learner Scale data labeling raw data labeled data IDE Better IDE for model building Learner Human-machine co-creation Learner Curb data hunger with interactive learning AutoML Scale model building via AutoML

32. AutoAI for Text AutoText [AAAI’21] AutoText: An End-to-End AutoAI Framework for Text. [NeurIPS 2022] AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning. IBM Developer API https://developer.ibm.com/learningpaths/get-started-autoai-for-text-api Example Use Case: Scale ML Product Model for Text Classification >30% Reduction in combined training and prediction time Auto weight tuning & HPO >10x Speed-up in training at comparable quality Auto classifier selection

33. Human-in-the-Loop Throughout the Entire Life Cycle of KG construction, growth, and services Data Labeling Development Deployment Learner Scale data labeling raw data labeled data IDE Better IDE for model building Learner Human-machine co-creation Learner Curb data hunger with interactive learning AutoML Scale model building via AutoML

34. Human-in-the-Loop Throughout the Entire Life Cycle of KG construction, growth, and services Data Labeling Development Deployment Learner Scale data labeling raw data labeled data IDE Better IDE for model building Learner Human-machine co-creation Learner Curb data hunger with interactive learning AutoML Scale model building via AutoML Query Log Tickets User feedback influence the entire life cycle

35. Quality Evaluation 1. Measure what matters for end users 2. Identify the root cause of failures 3. Track improvements in individual components as they evolve The key requirements - Who won the Paris Paris, France Paris Masters

36. Overall Evaluation Framework A Human-in-the-Loop Process Annotation Quality Metrics Dataset Collection Tooling and annotation guidelines for graders Evaluation Human in the loop to annotate/ grade queries Logs Synthetic Queries Knowledge Graph Metrics End to End Metrics Query Understanding Metrics

37. Visual Tooling of Metrics and Loss Buckets - Example Errors: - Entity Prediction Error: “Who won Paris” (Paris Masters/Paris–Roubaix) - Missing Fact: “When is the oscars in 2026” - Fact is not present because date/location is not published yet) - Unrecognized Entity in KG: ”Who is princess noor horse” Facilitate Opportunity Analysis

38. Visual Diagnosis [DaSH@KDD. 2020] WhyFlow: Explaining Errors in Data Flows Interactively.

39. ModelLens Visual interactive tool for model improvement [CSCW’19] ModelLens: An Interactive System to Support the Model Improvement Practices of Data Science Teams.

40. So how will EVERYTHING change with LLMs? Many exciting challenges and opportunities

41. Thanks! IBM (including interns): • Shivakumar Vaithyanathan • Lucian Popa • Ron Fagin • Sriram Raghavan • Rajasekar Krishnamurthy • Fred Reiss • Laura Chiticariu • Benny Kimelfeld • Mauricio Hernadez • Eser Kandogan • Huaiyu Zhu • Kun Qian • Dakuo Wang • Maeda Hanafi Many amazing collaborators and interns … Apple (including interns): • Ihab Ilyas • Theodoros Rekatsinas • Umar Farooq Minhas • Ali Mousavi • Jefferey Pound • Anil Pacaci • Shihabur R. Chowdhury • Hongyu Ren • Jason Mohoney • Kun Qian • Yiwen Sun • Yisi Sang • Saloni Potdar • … … Universities: • Azza Abouzeid (NYU-Abu Dhabi) • H. V. Jagadish (U. Of Michigan) • Fei Xia (U. Of Washington) • Kevin Chen-Chuan Chang (UIUC) • ChengXiang Zhai (UIUC) • Domenico Lembo(Sapienza University of Rome) • Dragomir R. Radev (Yale) • Jonathan K. Kummerfeld (U. Of Michigan) • Walter S. Lasecki (U. Of Michigan) • Toby Li (U. of Notre Dame) • Rishabh Iyer (UT Dallas) • Eduard C. Dragut (Temple Univ.) • … …. • Douglas Burdick’ • Alan Akbik • Nancy Wang • Prithiviraj Sen • Marina Danilevsky • Poornima Chozhiyath Raman • Sudarshan Rangarajan • Ramiya Venkatachalam • Kiran Kate • Eyal Shnarch • Ishan Jindal • Yiwei Yang • Nikita Bhutani • … ….

Building, Growing and Serving Large Knowledge Graphs with Human-in-the-Loop

Building, Growing and Serving Large Knowledge Graphs with Human-in-the-Loop

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