Knowledge Graphs: Smart Big Data

1. Knowledge Graphs: Smart Big Data Dieter Fensel & Umutcan Şimşek STI Innsbruck, University of Innsbruck, Austria May 5, 2021

2. Knowledge Graphs 1. What are Knowledge Graphs in a Nutshell 2. Why they are so desperately needed! 1. World Wide Web 2. Virtual intelligent agents 3. Physical intelligent agents 3. How to build them effectively and efficiently? 1. Knowledge Graph Methodology 2. Knowledge Graph Generation 3. Knowledge Graph Hosting 4. Knowledge Graph Curation 5. Knowledge Graph Deployment 4. The Proof Of The Pudding Is In The Eating 1. Open Touristic Knowledge Graph 2. Chatbots 5. Conclusions 2

3. 1. Knowledge Graphs in a Nutshell Google Knowledge Graph • “A huge knowledge graph of interconnected entities and their attributes”. Amit Singhal, Senior Vice President at Google • “A knowledge based used by Google to enhance its search engine’s results with semantic-search information gathered from a wide variety of sources” http://en.wikipedia.org/wiki/Knowledge_Graph • Based on information derived from many sources including Freebase, CIA World Factbook, Wikipedia • Contains many billion facts and “Objects” 3 4/1/2021 www.sti-innsbruck.at

4. 1. Knowledge Graphs in a Nutshell very large semantic nets that integrate various and heterogenous information sources to represent knowledge about certain domains. 4 Heterogenous data from different sources can be easily integrated Large-scale Knowledge Graphs can get really big really fast No Schema at least not in the sense of Relational Databases.

5. 1. Knowledge Graphs in a Nutshell Name Instances Facts Types Relations DBpedia (English) 4,806,150 176,043,129 735 2,813 YAGO 4,595,906 25,946,870 488,469 77 Freebase 49,947,845 3,041,722,635 26,507 37,781 Wikidata 15,602,060 65,993,797 23,157 1,673 NELL 2,006,896 432,845 285 425 OpenCyc 118,499 2,413,894 45,153 18,526 Google´s Knowledge Graph 570,000,000 18,000,000,000 1,500 35,000 Google´s Knowledge Vault 45,000,000 271,000,000 1,100 4,469 Yahoo! Knowledge Graph 3,443,743 1,391,054,990 250 800 Knowledge Graphs in the wild: Large ABoxes in comparison to smaller TBoxes

6. 2. Why are Knowledge Graphs needed 1. World Wide Web 2. Virtual intelligent agents, e.g. bots 3. Physical intelligent agents, e.g. autonomous cars 6

7. World Wide Web (WWW) Technologiy for eMarketing and eCommerce The Web Search 7 • Has no central content repository (only DNS) • Early 90tees: Bookmark lists (list of magic keys ala „Open Sesame“) • Mid 90tees: various competing search engines based on different paradigms • Google made the game

8. • Helping the web to scale infinitely World Wide Web (WWW) <html> <body> <a onto=„page:Researcher“> <h2>Welcome on my homepage</html> My name is <a onto=[name=body]“> Richard Benjamins</a>. </body></html> HTML-A 1996 4/1/2021 www.sti-innsbruck.at 8

9. World Wide Web (WWW) Evolving Technologies for eMarketing and eCommerce The Web Search 9 • Has no central content repository (only DNS) • Early 90tees: Bookmark lists (list of magic keys ala „Open Sesame“) • 90tees: Bookmark lists • Google made the game. • Semantic approaches were pushed aside by matrix multiplication of word vectors. Semantic Web: NO !

10. World Wide Web (WWW) Evolving Technologies for eMarketing and eCommerce The Web Search 10

11. Google as a Query Answering Engine [2012] World Wide Web Web: Google 2.0 Semantic Web: YES ! 4/1/2021 www.sti-innsbruck.at 11

12. 2. Semantic Web: Google 2.0 12 4/1/2021 www.sti-innsbruck.at T h e W e b

13. World Wide Web (WWW) 13

19. World Wide Web (WWW) Evolving Technologies for eMarketing and eCommerce The Web Search Semantic Web Query Answering 19

20. World Wide Web (WWW) Evolving Technologies for eMarketing and eCommerce The Web Search Semantic Web Query Answering 20

21. World Wide Web (WWW) Evolving Technologies for eMarketing and eCommerce The Web Search Semantic Web Query Answering Knowledge Graph Goal and Service Oriented Dialog 21

22. World Wide Web (WWW) Evolving Technologies for eMarketing and eCommerce Knowledge Graph Goal and Service Oriented Dialog 22

23. World Wide Web (WWW) - Disciplinam 23

24. Bots Evolving Technologies for eMarketing and eCommerce The Web Search Semantic Web Query Answering Knowledge Graph Goal and Service Oriented Dialoque 24

25. Bots 4 • Misunderstandings due to lack shared common sense, i.e. world knowledge. 25

26. Bots • Text/Voice becomes mainstream. • Use cases are still basic. • Knowledge is Power! Without knowledge -> no understanding of users needs and goals. Please, book a table in a restaurant with roast pork having reasonable prices in Mayrhofen for tonight Restaurant in Mayrhofen? Has roast pork? price? Image: ©amazon.com Sorry, I don’t know how to help you! 26

27. Bots Please, book a table in a restaurant with roast pork having reasonable prices in Mayrhofen for tonight Image: ©amazon.com KG action: TableReservation type: Restaurant, offers: Roast Pork Location: Mayrhofen Price: price_level generated query: ?- tableReservationAction(), type(Restaurant), offers(RoastPork). Predefined rules: ● tableReservationAction: book a table in a given Restaurant ● type: return all elements of type <type> ● offers: return all elements that offer <offer> ● ... Query Generation NLG Extracted Knowledge Generated Language output Knowledge Graph contains deep, accurate, and up-to- date knowledge about leasurement services in Tyrol. 27

28. Bots • The quality of Intelligent Assistants depends directly on the quality of the Knowledge Graph. • Problem: “Garbage in Garbage out” • Requirements for the Knowledge Graph: • well structured (using an ontology - schema.org) • accurate information (correctness) • large and detailed coverage (completeness) • Timeliness of knowledge ==> Method- and Tool-supported Knowledge Graph Lifecycle 28

29. Bots User 1. understand Intent + Parameters 2. map Query 3. query Knowledge Graph 4. Natural Language Generation 29

30. „Armed“ physical agent How Knowledge Graphs can prevent AI from killing people 30

31. The brave New World of AI • Autonomous Driving 31

32. Failures of AI technology • In May 2016 Joshua Brown was killed by his car because its autopilot mixed up a very long car (large wheelbase) with a traffic sign. 32

33. Failures of AI technology • In May 2016 Joshua Brown was killed by his car because its auto pilot mixed up a very long car (large wheelbase) with a traffic sign. This is what the auto pilot „saw“ 33

34. Failures of AI technology • In May 2016 Joshua Brown was killed by his car because its auto pilot mixed up a very long car (large wheelbase) with a traffic sign. This is what the auto pilot „saw“ 34 Why did none of the 10,000++ engineers involved not have the trivial idea to connect the car with a Knowledge Graph containing traffic data that simply knows that there is no traffic sign?

35. Failures of AI technology • In March 2018 Elaine Herzberg was the first victim of a full autonomously driving car. 35

36. Failures of AI technology • In March 2018 Elaine Herzberg was the first victom of a full autonomously driving car. • Besides many software bugs by Uber a la Boeing a core issue was that the car assumed that pedestrians cross streets only on crosswalks. • Make assumptions explicit and confirm them with a knowledge graph. • In this case she still would be alive! 36

37. Which kind of AI do we want? This one? 37

38. 3. How to build Knowledge Graphs 1. Knowledge Graph Methodology 2. Knowledge Graph Generation 3. Knowledge Graph Hosting 4. Knowledge Graph Curation 1. Knowledge Graph Assessment 2. Knowledge Graph Cleaning 3. Knowledge Graph Enrichment 5. Knowledge Graph Deployment 38

39. Knowledge Creation Knowledge Hosting Knowledge Cleaning Knowledge Enrichment Knowledge Curation Knowledge Deployment Knowledge Assesment 3.1. KG Methodology: Process Model 39

40. 3.1. KG Methodology: Task Model Knowledge Graph Maintenance Knowledge Hosting Knowledge Curation Knowledge Deployment Knowledge Assesment Knowledge Cleaning Knowledge Enrichement Error Detection Error Correction Evaluation Correctness Completeness Knowledge Source detection Knowledge Source integration Duplicate detection Property-Value- Statements correction Knowledge Creation Edit Semi-automatic Automatic Mapping 40

41. 3.1. KG Methodology: Tools Knowledge Graph Maintenance Knowledge Hosting Knowledge Curation Knowledge Deployment Knowledge Assesment Knowledge Cleaning Knowledge Enrichement Error Detection Error Correction Evaluation Correctness Completeness Knowledge Source detection Knowledge Source integration Duplicate detection Property-Value- Statements correction Knowledge Creation Edit Semi-automatic Automatic Mapping 41 ● Semantify.it ● MongoDB ● GraphDB ● Quality Assessment Tool (QAT) ● VeriGraph ● Evaluator Duplicate Detection as a Service (DDaS) ● RocketRML ● Conversational Agents ● Knowledge Activators Tasks and their tools

42. 3.1. KG Methodology: Tools Knowledge Graph Maintenance Knowledge Hosting Knowledge Curation Knowledge Deployment Knowledge Assesment Knowledge Cleaning Knowledge Enrichment Error Detection Error Correction Evaluation Correctness Completeness Knowledge Source detection Knowledge Source integration Duplicate detection Property-Value- Statements correction Knowledge Creation Edit Semi-automatic Automatic Mapping 42 ● Semantify.it ● MongoDB ● GraphDB ● Quality Assessment Tool (QAT) ● VeriGraph ● Evaluator Duplicate Detection as a Service (DDaS) ● RocketRML ● Conversational Agents ● Knowledge Activators Tasks and their tools Semantify.it: A platform that enables knowledge creation (manual, via mapping, soon semi-automatic), evaluation and deployment of semantic annotations of web sites https://semantify.it

43. 3.1. KG Methodology: Tools Knowledge Graph Maintenance Knowledge Hosting Knowledge Curation Knowledge Deployment Knowledge Assesment Knowledge Cleaning Knowledge Enrichement Error Detection Error Correction Evaluation Correctness Completeness Knowledge Source detection Knowledge Source integration Duplicate detection Property-Value- Statements correction Knowledge Creation Edit Semi-automatic Automatic Mapping 43 ● Semantify.it ● MongoDB ● GraphDB ● Quality Assessment Tool (QAT) ● VeriGraph ● Evaluator Duplicate Detection as a Service (DDaS) ● RocketRML ● Conversational Agents ● Knowledge Activators Tasks and their tools RocketRML: An efficient RML Mapper implemented with NodeJS https://github.com/semantifyit/RocketRML

44. 3.1. KG Methodology: Tools Knowledge Graph Maintenance Knowledge Hosting Knowledge Curation Knowledge Deployment Knowledge Assesment Knowledge Cleaning Knowledge Enrichement Error Detection Error Correction Evaluation Correctness Completeness Knowledge Source detection Knowledge Source integration Duplicate detection Property-Value- Statements correction Knowledge Creation Edit Semi-automatic Automatic Mapping 44 ● Semantify.it ● MongoDB ● GraphDB ● Quality Assessment Tool (QAT) ● VeriGraph ● Evaluator Duplicate Detection as a Service (DDaS) ● RocketRML ● Conversational Agents ● Knowledge Activators Tasks and their tools MongoDB: Annotations for web stored as JSON-LD documents in a document store to enable immediate access to annotations of web sites https://www.mongodb.com/

45. 3.1. KG Methodology: Tools Knowledge Graph Maintenance Knowledge Hosting Knowledge Curation Knowledge Deployment Knowledge Assesment Knowledge Cleaning Knowledge Enrichement Error Detection Error Correction Evaluation Correctness Completeness Knowledge Source detection Knowledge Source integration Duplicate detection Property-Value- Statements correction Knowledge Creation Edit Semi-automatic Automatic Mapping 45 ● Semantify.it ● MongoDB ● GraphDB ● Quality Assessment Tool (QAT) ● VeriGraph ● Evaluator Duplicate Detection as a Service (DDaS) ● RocketRML ● Conversational Agents ● Knowledge Activators Tasks and their tools Ontotext GraphDB: Knowledge Graph is natively stored in a GraphDB instance to enable efficient querying and reasoning https://www.ontotext.com/products/gra phdb/

46. 3.1. KG Methodology: Task Model Knowledge Graph Maintenance Knowledge Hosting Knowledge Curation Knowledge Deployment Knowledge Assesment Knowledge Cleaning Knowledge Enrichement Error Detection Error Correction Evaluation Correctness Completeness Knowledge Source detection Knowledge Source integration Duplicate detection Property-Value- Statements correction Knowledge Creation Edit Semi-automatic Automatic Mapping 46 ● Semantify.it ● MongoDB ● GraphDB ● Quality Assessment Tool (QAT) ● VeriGraph ● Evaluator Duplicate Detection as a Service (DDaS) ● RocketRML ● Conversational Agents ● Knowledge Activators Tasks and their tools Quality Assessment Tool (QAT) calculates weighted aggregated quality scores for 18 dimensions and 36 metrics for different domains https://qat.semantify.it

47. 3.1. KG Methodology: Tools Knowledge Graph Maintenance Knowledge Hosting Knowledge Curation Knowledge Deployment Knowledge Assesment Knowledge Cleaning Knowledge Enrichement Error Detection Error Correction Evaluation Correctness Completeness Knowledge Source detection Knowledge Source integration Duplicate detection Property-Value- Statements correction Knowledge Creation Edit Semi-automatic Automatic Mapping 47 ● Semantify.it ● MongoDB ● GraphDB ● Quality Assessment Tool (QAT) ● VeriGraph ● Evaluator Duplicate Detection as a Service (DDaS) ● RocketRML ● Conversational Agents ● Knowledge Activators Tasks and their tools VeriGraph: Efficient error detection tool for instances in a Knowledge Graph based on integrity constraints https://github.com/semantifyit/VeriGraph https://semantify.it/verigraph

48. 3.1. KG Methodology: Tools Knowledge Graph Maintenance Knowledge Hosting Knowledge Curation Knowledge Deployment Knowledge Assesment Knowledge Cleaning Knowledge Enrichement Error Detection Error Correction Evaluation Correctness Completeness Knowledge Source detection Knowledge Source integration Duplicate detection Property-Value- Statements correction Knowledge Creation Edit Semi-automatic Automatic Mapping 48 ● Semantify.it ● MongoDB ● GraphDB ● Quality Assessment Tool (QAT) ● VeriGraph ● Evaluator Duplicate Detection as a Service (DDaS) ● RocketRML ● Conversational Agents ● Knowledge Activators Tasks and their tools Evaluator: Verification (against domain-specific patterns) and validation (against the content of the annotated webpage) of semantic annotations https://semantify.it/evaluator

49. 3.1. KG Methodology: Tools Knowledge Graph Maintenance Knowledge Hosting Knowledge Curation Knowledge Deployment Knowledge Assesment Knowledge Cleaning Knowledge Enrichement Error Detection Error Correction Evaluation Correctness Completeness Knowledge Source detection Knowledge Source integration Duplicate detection Property-Value- Statements correction Knowledge Creation Edit Semi-automatic Automatic Mapping 49 ● Semantify.it ● MongoDB ● GraphDB ● Quality Assessment Tool (QAT) ● VeriGraph ● Evaluator Duplicate Detection as a Service (DDaS) ● RocketRML ● Conversational Agents ● Knowledge Activators Tasks and their tools Duplication Detection as a Service: A duplication detection tool that can learn configurations for different scenarios and can be optimized for different criteria (eg. precision, recall, F1) https://github.com/julietcetera/ddaas

50. 3.1. KG Methodology: Tools Knowledge Graph Maintenance Knowledge Hosting Knowledge Curation Knowledge Deployment Knowledge Assesment Knowledge Cleaning Knowledge Enrichement Error Detection Error Correction Evaluation Correctness Completeness Knowledge Source detection Knowledge Source integration Duplicate detection Property-Value- Statements correction Knowledge Creation Edit Semi-automatic Automatic Mapping 50 ● Semantify.it ● MongoDB ● GraphDB ● Quality Assessment Tool (QAT) ● VeriGraph ● Evaluator Duplicate Detection as a Service (DDaS) ● RocketRML ● Conversational Agents ● Knowledge Activators ● Open Tourism Tasks and their tools Conversational Agents: Knowledge Graphs power conversational agents as a knowledge source and for training various machine learning models (e.g. NLU) e.g. https://onlim.com

51. 3.1. KG Methodology: Tools Knowledge Graph Maintenance Knowledge Hosting Knowledge Curation Knowledge Deployment Knowledge Assesment Knowledge Cleaning Knowledge Enrichement Error Detection Error Correction Evaluation Correctness Completeness Knowledge Source detection Knowledge Source integration Duplicate detection Property-Value- Statements correction Knowledge Creation Edit Semi-automatic Automatic Mapping 51 ● Semantify.it ● MongoDB ● GraphDB ● Quality Assessment Tool (QAT) ● VeriGraph ● Evaluator Duplicate Detection as a Service (DDaS) ● RocketRML ● Conversational Agents ● Knowledge Activators ● Open Tourism Knowledge Graphs Tasks and their tools Knowledge Activators do reasoning and curation on different (smaller) views of a Knowledge Graph to improve scalability and support different point of views

52. 3.2. Knowledge Generation 52

53. 3.2. Knowledge Generation • https://www.schema.org/ • Started in 2011 by Bing, Google, Yahoo!, and Yandex to annotate websites. • Has become de facto standard. • We use it for the website channel as well as for all other channels as an reference model for our semantic annotations. • We define domain-specific extensions (that also restrict the genericity of entire schema.org). 53

54. 3.2. Knowledge Generation 54

55. 3.2. Knowledge Generation • The use of semantic annotations has experienced a tremendous surge in activity since the introduction of schema.org. • Schema.org was introduced with 297 classes and 187 properties, • which over have grown to 779 Types, 1390 Properties 15 Datatypes, 81 Enumerations and 437 Enumeration members.* • The provided corpus of • types (e.g. LocalBusiness, SkiResort, Restaurant), • properties (e.g. name, description, address), • range restrictions (e.g. Text, URL, PostalAddress), • and enumeration values (e.g. DayOfWeek, EventStatusType, ItemAvailability) covers large numbers of different domains. 55 * on 01.04.2021

56. 3.2. Knowledge Generation • We define domain-specific extensions (that also restrict the genericity of entire schema.org). • Domain Specifications: • restrict genericity and • extend domain-specificity of schema.org. • Are based on SHACL • https://schema-tourism.sti2.org/ • We use value restriction not as inference mechanism but as integrity constraint. Schema.org Domain Domain Specification 56

57. 3.2. Knowledge Generation Our Methodology: • the bottom-up part, which describes the steps of the initial annotation process; • the domain specification modeling; and • the top-down part, which applies the constructed models. 57

58. 3.2. Knowledge Generation Semantify.it1: A platform for creating, hosting, validating, verifying, and publishing schema.org annotated data • annotation of static data based on schema.org templates 🡪 Domain Specifications2 • annotation of different schemata and dynamic data based on RML3 mappings🡪 Rocket RML4 1 https://semantify.it 2 http://ds.sti2.org 3 https://rml.io 4 https://github.com/semantifyit/RocketRML 58

59. 3.2. Knowledge Generation Manual Annotation Editor 59

60. 3.2. Knowledge Generation • Semi-automatic • Annotation Editor suggests mappings/extracted information • e.g. extract information from web pages (by HTML tags). • Use partial NLU to find similarities of the content and schema.org vocabulary. • Manual adaptions needed to define and to evaluate. • Instance of the general issues of wrapper generation. 60

61. 3.2. Knowledge Generation • Mapping (more than 95% of the story) • integrate large and fast changing data sets, • map different formats to the ontology used in our Knowledge Graph, • Various frameworks: XLWrap, Mapping Master (M2), a generic XMLtoRDF tool providing a mapping document (XML document) that has a link between an XML Schema and an OWL ontology, Tripliser, GRDDL, R2RML, RML, ... • We developed an efficient mapping engine for the RDF Mapping Language RML, called RocketRML. It is a rule-based engine that efficiently processes RML mappings and creates RDF data. • The semantify.it platform features a wrapper API where these mappings can be stored and applied to corresponding data. 61

62. RML: ● Easier to learn RML than a programming language ● Easy sharing ● Mapping can be visualized ● Mapfiles can be faster to write than code ● Easily change mappings ● Rocket RML pre-compiles joins to improve performance by several order of magnitudes. RML YARRRML Matey 62 3.2. Knowledge Generation

63. 3.2. Knowledge Generation Automatic extraction of knowledge from text representations and web pages • Tasks • named entity recognition, • concept mining, text mining, • relation detection, … • Methods • Information Extraction, • Natural Language Processing (NLP), • Machine Learning (ML) 63 • Systems: • GATE (text analysis & language processing) • OpenNLP (supports most common NLP tasks) • RapidMine (data preparation, machine learning, deep learning, text mining, predictive analysis) • Ontotext / Sirma • Important when large amounts of semi- structured information is crawled (from the Web) through semantify.

64. 3.2. Knowledge Generation Evaluation of semantic annotations: • The semantify.it evaluator is a web-tool that offers the possibility to validate schema.org annotations that are scraped from websites. • Verification: The annotations are checked against plain schema.org and against domain specifications. • Validation: The annotations are checked whether they accurately describe of the content of the web site. • https://semantif.it/evaluate 64

65. 3.2. Knowledge Generation Evaluation of semantic annotations: • Notice we take the content of the web site as Golden Standard. • We do NOT evaluate the accuracy of that content in regard to the „real“ world. • We check whether a phone number confirms to the formal constraints. • We do not make robocalls to hotels. to check whether the „right“ hotel pick up the phone. 65

66. 3.2. Knowledge Generation Evaluation = Validation & Verification 66

67. 3.2. Knowledge Generation • Annotation of dynamic and active data with WASA (earlier called WSMO). • Dynamic: Actions to obtain dynamic data (e.g. weather forecast). • Active: Actions that can be taken on entities in a Knowledge Graph (e.g. a room offering of a Hotel can have BuyAction attached to it). • An action is an instance of schema.org/Action type. • Describe the invocation mechanism (e.g. endpoint, HTTP method, encoding type). • Describe input and output parameters with SHACL (another implementation of domain specifications). • Grounding and lifting for existing Web APIs. 67

68. 3.2. Knowledge Generation 68 http://actions.semantify.it 68

69. 3.2 Knowledge Generation • Semantify.actions provides an interface for annotating a Web API as a collection of schema:Action annotations. • A basic interface for annotating the essential constraints on input and output (e.g. range restrictions, minimum cardinality). • A more advanced interface is available for each property to define further constraints (e.g. relationships between properties). • Web APIs are wrapped for WASA clients: ○ Request grounding with XQuery to XML and JSON ○ Response lifting from XML and JSON to schema.org with RML ○ Utility functions can be defined in Javascript lifting and grounding (e.g., for data transformation, conditional mappings). 69

70. 3.2 Knowledge Generation EventSearch a schema:SearchAction; schema:name "Event Search"; schema:description "Search different types of events based on name location or date"; schema:actionStatus schema:PotentialActionStatus; schema:target [ a schema:EntryPoint; schema:urlTemplate :query; schema:encodingFormat "application/ld+json"; schema:contentType "application/ld+json"; schema:httpMethod "POST" ]; wasa:actionShape [ a sh:NodeShape; sh:property [ sh:path schema:object; sh:group wasa:Input; sh:class schema:Event; 70 sh:node [ sh:property [ sh:path schema:name; sh:datatype xsd:string; sh:maxCount 1 ]; sh:property [ sh:path schema:startDate; sh:datatype xsd:date; sh:maxCount 1 ]; sh:property [ sh:path schema:endDate; sh:datatype xsd:date; sh:maxCount 1 ]; ... An excerpt from a schema.org action with potential action status. The value of schema:target describes the invocation mechanism The value of wasa:actionShape contains a SHACL shape that describes the input, output and their relationship. See the full annotation at: https://bit.ly/32jiMZJ

71. 3.3. Knowledge Hosting Annotation - Tool (e.g. semantify.it) Document store (e.g. MongoDB) Graph database (e.g. GraphDB) Hosting ... Semantic Web Annotations Knowledge Graphs 71

72. 3.3. Knowledge Hosting • Semantically annotated data can be serialized to JSON-LD • storage in document store MongoDB • native JSON storage; • well-integrated in current state of the art software with NodeJS • performant search, through indexing; • Allows efficient publication of annotations on webpages; • not hardware intensive no native RDF querying with SPARQL. 72

73. 3.3. Knowledge Hosting Native storage of semantically annotated data versus relational database • Relational databases have rigid schemas: • good for data quality and optimization (query and storage) • bad for integrating heterogeneous and dynamic sources • Storing RDF data in RDBMS: • when a new type or property comes, in a graph database it is just a new node or edge; in relational databases schema needs to be rebuilt. • Triple tables may make some optimizations ineffective • complex queries over connected data can cause performance problems: too many joins (possibly self joins) • Virtualization as an alternative: • Query optimization is problematic (things can go wrong while query rewriting) • no native reasoning 73

74. 3.3. Knowledge Hosting • RDF store: GraphDB • very powerful CRUD operations • named graphs for versioning • full implementation of SPARQL • powerful reasoning over big data sets • very hardware intensive • Task 4. Developed by Ontotext https://www.ontotext.com/ 74

75. 3.4. Knowledge Curation 75

76. 3.4. Knowledge Curation 76 ● Type hierarchy ● Property definitions ● Property value assertions ● aligned with data model of schema.org Should support Formalism Knowledge Representation Formalism

77. 3.4. Knowledge Curation 77 ● Open environment ● Predominantly incomplete information ● should be usable and extendable by everyone ● Closed, controlled environment ● Complete information for restricted context ● usable and extendable with permission only Web (Enterprise) Knowledge Graph Knowledge Representation Formalism - Application Scope

78. 3.4. Knowledge Curation 78 Maximally Simple Knowledge Representation Formalism (MSKR) for schema.org-based Knowledge Graphs

79. 3.4. Knowledge Curation •We defined a simple KR formalism formalizing essentials of schema.org • Tbox: isA statements of types, domain and range definitions for properties (using them globally or locally) • Abox: isElementOf(I,t) statements, propertyValue statements p(i1,i2), and sameAs(i1,i2) statements • Enables a formal definition of the knowledge curation task: • assessment, • cleaning, and • enrichment. 79

80. 3.4. Knowledge Curation 80 ● Closed-World-Assumption ○ Verification ● Separation TBox and ABox ○ Simplification ○ Independent tasks Restrictions Extensions ● Support for disjunctive ranges ● Local properties Resource Description Framework Schema (RDFS) as basis for MSKR

81. 3.4. Knowledge Curation Informal definition of MSKR ● Two disjoint and finite sets of type (T) and property names (P) ● Finite number of type definitions isA(t1, t2) ○ t1 and t2 ∈ T ○ isA is reflexive & transitive ● Finite number of property definitions: ○ hasDomain(p, t) with p ∈ P and t ∈ T ○ Range definition for property p (p ∈ P), t1 and t2 ∈ T ■ Simple definition: Global property definition: hasRange(p, t2) ■ Refined definition: Local property: hasRange(p, t2) for domain t1 (hasLocalRange(p, t1, t2)) ● countable set of instance identifiers (I) ○ i, i1, i2 ∈ I ● Instance assertions: isElementOf(i, t) ○ Semantics: If isA(t1, t2) & isElementOf(i, t1) THEN isElementOf(i, t2) ● Property value assertions: p(i1, i2) ● Equality assertions: isSameAs(i1, i2) ○ symmetric, reflexive, and transitive TBox ABox 81

82. 3.4.1. Knowledge Assessment • First step to improve the quality of a KG: Assess the situation • Knowledge Assessment describes and defines the process of assessing the quality of a Knowledge Graph. • The goal is to measure the usefulness of a Knowledge Graph. • Evaluation • Overall process to determine the quality of a Knowledge Graph. • Select quality dimensions, metrics, evaluation functions, and weights for metrics and dimensions. • Evaluate representative subsets accordingly. 82

83. 3.4.1. Knowledge Assessment [Paulheim et al., 2019] identify the following subtasks: • specifying datasets and Knowledge Graphs, • specifying the evaluation protocol, • specifying the evaluation metrics, • specifying the task for task-specific evaluation, • and defining the setting in terms of intristic vs. task-based, and automatic versus human- centric evaluation, • as well as the need to keep the results reproducible. H. Paulheim, M. Sabon, M. Choches, and W. Beck: Evaluation of Knowledge Graphs. In P. A. Bonatti, S. Decker, A. Polleres, and V. Presutti: Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web, Dagstuhl Reports, 8(9):29-111, 2019. 83

84. 84 Dimensions: 1. accessibility 2. accuracy (veracity) 3. completeness 4. concise representation 5. consistent representation 6. cost-effectiveness 7. flexibility 8. interoperability 9. relevancy 10. timeliness (velocity) 11. trustworthiness 12. understandability 13. variety an extended list can be found in [Fensel et al., 2020] 3.4.1. Knowledge Assessment

85. 85 Each dimension has a set of metrics. Each metric has a calculation function: Example metric calculation from Understandability dimension: 3.4.1. Knowledge Assessment

86. 86 Some dimensions are more contextual, i.e., needs external information alongside the Knowledge Graph Example metric calculation from Relevancy dimension: 3.4.1. Knowledge Assessment

87. 87 Calculate the assessment score Calculate a weighted aggregate score for the Knowledge Graph for each domain or task. Decide on Dimension Weights Each dimension may have have different level of importance for different domains or tasks. Decide on Metric Weights Each metric may have different impact on the calculation of the dimension to which they belong 3.4.1. Knowledge Assessment

88. 88 A Running Example for Knowledge Cleaning and Enrichment 3.4.1. Knowledge Assessment

89. Domain Property Range s:LandmarksOrHistoricalBuildings s:address s:PostalAddress s:containedInPlace s:Place s:PostalAddress s:streetAddress s:Text s:addressLocality s:Text s:addressCountry s:Country s:postalCode s:Text s:TouristAttraction s:availableLanguage s:Text A subset of schema.org for the running example 89 3.4.1. Knowledge Assessment

90. URI URI i t URI i1 URI or “Literal” p i2 Instance Assertion Property Value Assertion * URI i1 URI sameAs i2 Equality Assertion * i is an instance of the type t the value of property p on instance i1 is i2 i1 is the same instance as i2 90 3.4.1. Knowledge Assessment

91. ex:Eiffel Tower s:address ex:Champ_de_Mars ex:France s:Event A broken Knowledge Graph ex: http://example.org/ s: http://schema.org/ dbpedia: http://dbpedia.org/resource/ “5 Avenue Anatole France” “Paris” “7501” s:Landmark “fr” s:VisualArtWork s:Country dbpedia:Paris_ Las_Vegas 91 3.4.1. Knowledge Assesment

92. 3.4.1. Knowledge Assessment Methodologies • Total Data Quality Management (TDQM) and Data Quality Assessment allow identifying important quality dimension and their requirements from various perspectives. • Other methodologies already defined quality metrics that allow a semi-automatic assessment based on data integrity constraints. Those are for example User-driven, Test-driven assessment and a manual assessment based on crowd's experts (Crowdsourcing-driven assessment). • Besides that, there are quality assessment approaches which use statistical distribution for measuring the correctness of statements, SPARQL queries for the identification of functional dependency violations and missing values. 92

93. 3.4.1. Knowledge Assessment Tools and Methods: • LINK-QA • using network metrics • Luzzu (Linked Open Datasets) • thirty data quality metrics based on Dataset Quality Ontology • Sieve • Flexible in expressing various quality assessment methods • SWIQA (Semantic Web Information Quality Assessment Framework) • data quality rules & quality scores for identifying wrong data • Validata • online tool for testing/validating RDF data against ShEx-schemas 93

94. 3.4.1. Knowledge Assessment Sleve: • Sieve for Data Quality Assessment is a framework which consist of two modules: • a Quality Assessment module and • a Data Fusion module • The Quality Assessment Module involves four steps: 1. Data Quality Indicator allows to define an aspect of a data set that may demonstrate the suitability of it for intended use. For example, meta-information about the creation of a data set, information about the provider, or ratings provided by the consumers. 2. Scoring Functions define the assessment of the quality indicator based on its quality dimension. Scoring functions range from simple comparisons, over set functions, aggregation functions, to more complex statistical functions, text-analysis, or network analysis methods. 3. Assessment Metric calculates the assessment score based on indicators and scoring functions. 4. Aggregate Metric allows users to aggregate new metrics that can generate new assessment values. 94

95. 3.4.1. Knowledge Assessment: Our Approach 95 ● 20 dimensions ○ e.g., Accessibility ● 42 metrics ○ e.g., Knowledge Graph offers SPARQL endpoint ● Weighting of dimensions & metrics based on ○ applications ○ domains ● sum of ○ dimension weights = 1 ○ metrics weights = 1 Quality Dimensions & Metrics

96. 3.4.1. Knowledge Assessment: Our Approach 96 Process model ● Definition of weights for different metrics and different dimensions ● Domain-specific configuration ● Score between 0 and 1 ○ for each dimension ○ adding up its weighted metrics scores ● some metrics not automatable ● Aggregated score between 0 and 1 ○ adding up weighted dimension scores

97. 3.4.1. Knowledge Assessment: Our Approach 97 Quality Assessment Tool (QAT) ● Provided as Software as a Service (Saas) ● periodically fetches information from configured data sources ○ automatically ○ on-demand planned ● User defines weights for dimensions and metrics ● Overall score accessible via ○ API ○ User Interface

98. 3.4.2. Knowledge Cleaning • The goal of knowledge cleaning is to improve the correctness of a Knowledge Graph • Major objectives are • error detection and • error correction of ● wrong instance assertions ● wrong property value assertions ● wrong equality assertions 98

99. 3.4.2. Knowledge Cleaning Tbox 99

100. 3.4.2. Knowledge Cleaning Tbox Abox 100

101. 3.4.2. Knowledge Cleaning Tbox Abox Knowledge Curation 101

102. 3.4.2. Knowledge Cleaning What Verification Validation Semantic Annotations check schema conformance and integrity constraints compare with web resource Knowledge Graphs check schema conformance and integrity constraints compare with "real" world 102

103. Actions taken to improve the accuracy of Knowledge Graphs: Error Detection Identify errors from different error sources Error Correction Correct the identified errors manually or semi-automatically 103 3.4.2. Knowledge Cleaning

104. Equality Assertions ● Syntactic errors in i1 or i2 ● Assertion is semantically wrong Property Value Assertions ● Syntactic errors in i1, i2 or p ● p does not exist in the vocabulary ● Domain and range violations ● Assertion is semantically wrong Instance Assertions ● Syntactic errors in the instance identifiers ● Type does not exist in the vocabulary ● Assertion is semantically wrong Error sources and types 3.4.2. Knowledge Cleaning 104

105. Error Detection: • Statistical approaches • Knowledge-driven approaches • Integrity Constraints 105 3.4.2. Knowledge Cleaning

106. Error Correction: • Wrong Instance assertions • There can be syntactic errors in instance identifiers ex:Eiffel Tower not valid without encoding ex:Eiffel_Tower 106 3.4.2. Knowledge Cleaning

107. Error Correction: • Wrong Instance assertions • The type may not exist in the vocabulary ex:Eiffel_Tower s:Landmark no such type exists in schema.org s:LandmarksOrHistoricalBuildings 107 3.4.2. Knowledge Cleaning

108. Error Correction: • Wrong Instance assertions • The assertion may be semantically wrong ex:Eiffel_Tower s:Event the Eiffel Tower is not an Event. (at least in this example) delete the instance assertion 108 3.4.2. Knowledge Cleaning

109. Error Correction: • Wrong property value assertions • There may be syntactic errors in instance or property identifier in an assertion. ex:Eiffel Tower i is not valid without encoding ex:Eiffel_Tower ex:Champ_de_Mars 109 3.4.2. Knowledge Cleaning

110. Error Correction: • Wrong property value assertions • There is no property p in the vocabulary ex:Eiffel_Tower no such property exists in schema.org ex:Champ_de_Mars 110 3.4.2. Knowledge Cleaning

111. Error Correction: • Wrong property value assertions • The type (t) of i1 is not in the domain of property (p) ex:Eiffel_Tower not in the domain of s:availableLanguage “fr” s:LandmarksOrHistoricalBuildings s:TouristAttraction add new instance assertion 111 3.4.2. Knowledge Cleaning

112. Error Correction: • Wrong property value assertions • The type (t) of i2 is not in the range of p for any of the types in its domains. ex:Eiffel Tower ex:Champ_de_Mars s:VisualArtWork not in the range of s:containedInPlace s:Place delete the wrong assertion and add new instance assertion 112 3.4.2. Knowledge Cleaning

113. Error Correction: • Wrong property value assertions • The property value assertion is semantically wrong. ex:Eiffel Tower s:address “75001” “75007” fix the value wrong ZIP code 113 3.4.2. Knowledge Cleaning

114. Error Correction: • Wrong equality assertions • Either i1 or i2 or both may be syntactically wrong • Fix the issue in a manner similar to previous error types. 114 3.4.2. Knowledge Cleaning

115. Error Correction: • Wrong equality assertions • The equality assertion may be semantically wrong. ex:Eiffel Tower dbpedia:Paris_ Las_Vegas two related, but different things delete the assertion or create a “weaker” link 115 3.4.2. Knowledge Cleaning

116. Tools: • The existing tools mainly focus on detection of errors. Common approaches: • Statistical distribution of instance and property value assertions • Integrity constraints with SPARQL and shapes. • Correction approaches typically use certain heuristics for syntactical errors and external trusted Knowledge Graphs for other error types. 116 3.4.2. Knowledge Cleaning

117. Tools: • Automating detection of semantically wrong assertions is tricky. How do we touch the “real world”? • Take an existing, trustworthy Knowledge Graph as an oracle • See the websites from where annotations are collected as the source of truth. • Similar to Semantify.it Validator approach 117 3.4.2. Knowledge Cleaning

118. Methods & Tools: • HoloClean ● Use of integrity constraints, ● external data, ● quantitative statistics. ● Steps • separate entry datasets into noisy and clean dataset • assign uncertainty score over the value of noisy datasets • compute marginal probability for each value to be repaired. 118 3.4.2. Knowledge Cleaning

119. 3.4.2. Knowledge Cleaning Methods & Tools: • HoloClean ● use of integrity constraints, ● external data, and ● quantitative statistics. ● Steps • separate entry datasets into noisy and clean dataset • assign uncertainty score over the value of noisy datasets • compute marginal probability for each value to be repaired • SDValidate ● uses statistical distribution functions ● three steps: • compute relative predicate frequency for each statement • each statement selected in first step -> assign score of confidence • apply threshold of confidence. • Similar steps for instance assertions. 119

120. 3.4.2. Knowledge Cleaning Methods & Tools: • The LOD Laundromat ● cleans Linked Open Data ● takes SPARQL endpoint/archived dataset as entry dataset ● guesses the serialisation format ● identifies syntax errors using a library while parsing RDF ● saves RDF data in canonical format 120

121. 3.4.2. Knowledge Cleaning Methods & Tools: • The LOD Laundromat ● cleans Linked Open Data ● takes SPARQL endpoint/archived dataset as entry dataset ● guesses the serialisation format ● identifies syntax errors using a library while parsing RDF ● saves RDF data in canonical format • KATARA ● identifies correct & incorrect data ● generates possible corrections for wrong data 121

122. 3.4.2. Knowledge Cleaning Methods & Tools: • The LOD Laundromat ● cleans Linked Open Data ● takes SPARQL endpoint/archived dataset as entry dataset ● guesses the serialisation format ● identifies syntax errors using a library while parsing RDF ● saves RDF data in canonical format • KATARA ● identifies correct & incorrect data ● generates possible corrections for wrong data • SPIN ● SPARQL Constraint Language ● generates SPARQL Query templates based on data quality problems • inconsistency • lack of comprehensibility • heterogeneity • Redundancy • Nowadays, SPIN has turned into SHACL, a language for validating RDF graphs. 122

123. 3.4.2. Knowledge Cleaning: System survey • Verification • Quality Assessment Frameworks such as Luzzu (A Quality Assessment Framework for Linked Open Datasets), Sieve (Linked Data Quality Assessment and Fusion), SWIQA (Semantic Web Information Quality Assessment Framework), and WIQA (Web Information Quality Assessment Framework). • Approaches that check the conformance of RDF graphs against specifications: Alegro Graph Tool, RDFUnit, SHACL (Shapes Constraint Language) and ShEx (Shape Expressions), Stardog ICV, TopBraid, and Validata. • Tools that use statistical distributions to predict the types of instances (e.g., SDType) and to detect erroneous relationships that connect two resources (e.g., HoloClean, SDValidate). • More approaches: KATARA, LOD Laundromat, … • Validation • Fact validation frameworks: COPAAL (Corroborative Fact Validation), DeFacto (Deep Fact Validation), FactCheck, FacTify, Leopard, Surface, S3K, and TISCO. 123

124. 3.4.2. Knowledge Cleaning: Our approach • VeriGraph: Verification framework for large Knowledge Graphs. It detects errors by verifying the instances in a Knowledge Graph against a set of given domains- specific patterns (expressed in SHACL). • Validation report shows the inconsistencies found (including a human readable path to the error). 124

125. 3.4.2. Knowledge Cleaning: Our approach Verification process: Only the necessary subset of a KG is loaded into the memory per DS (i.e. a SHACL shape). The constraints are checked on the memory. No one SPARQL query per constraint component approach. 125

126. 3.4.2. Knowledge Cleaning: Our approach Implemented in the semantify.it platform 126 Several improvements are ongoing (e.g. better handling of cyclic data) https://github.com/semantifyit/VeriGraph https://semantify.it/verigraph

127. 3.4.3 Knowledge Enrichment • The goal of knowledge enrichment is to improve the completeness of a Knowledge Graph by adding new statements. • The process of Knowledge Enrichment has the following phases: • New Knowledge Source detection • New Knowledge Source integration (URI normalization) • Integrate the instances: • Duplicate detection and alignment • Property-Value-Statements correction. 127

128. 128 Integrate the Instances Two major issues: 1. Identifying and resolving duplicates 1. Resolving conflicting property value assertions. Identify New Sources This process can be automated to some extend for Open Knowledge Graphs. Identifying proprietary sources automatically is tricky. Integrate the Schema The relevant parts of the schemas of new sources are mapped to schema.org. 3.4.3 Knowledge Enrichment Process Model

129. 3.4.3 Knowledge Enrichment • Knowledge Source detection: search for additional sources of assertions for the Knowledge Graph • Open sources • Closed sources • Knowledge Source integration • Tbox: define mappings • Abox: integrate new assertions into the the Knowledge Graph • Identifying and resolving duplicates • Invalid property statements such as domain/range violations and having multiple values for a unique property, also known in the data quality literature as contradicting or uncertain attribute value resolution. 129

130. 3.4.3 Knowledge Enrichment • New Knowledge Source integration (URI normalization). • URIs are critical for identifying instances and integrating them in a Knowledge Graph. • Find canonical URIs for instances in a Knowledge Graph from the best external sources. 130

131. 3.4.3 Knowledge Enrichment 131 1 2 3

132. 3.4.3 Knowledge Enrichment • Synthetic URI generation • Generate a URI for every incoming instance • Advantage: really efficient • Disadvantage: No integration of external sources “for free” • Bottom-up URI search • Start from a seed source and follow the same as links • Advantage: No pre-selected sources, potentially larger number of sources can be discovered • Disadvantage: Leaves non-RDF sources out. 132 Alternative Approaches to URI Normalization

133. 3.4.3 Knowledge Enrichment • Identification by Description • No URIs - Instances are identified with their property values • Advantage: No effort for creating/finding URIs • Disadvantage: Not web friendly. Very challenging to refer to individual entities. • Google Search • Use certain property values as part of a Google search query and use the search results as URIs • Advantage: URIs are already ranked • Disdvantage: No control over ranking algorithm. No guarantee that URIs are cool* 133 Alternative Approaches to URI Normalization * “Cool URIs don’t change”: https://www.w3.org/Provider/Style/URI

134. 3.4.3 Knowledge Enrichment Duplicate detection: 134 https://www.cs.umd.edu/~getoor/Tutorials/ER_VLDB2012.pdf

135. 3.4.3. Knowledge Enrichment Methods and tools for duplicate detection and resolution: • Silk is a framework for achieving entity linking. • It tackles three tasks: 1. link discovery that defines similarity metrics to calculate a total similarity value for a pair of entities. 2. evaluation of the correctness and completeness of generated links, and 3. a protocol for maintaining data that allows source dataset and target dataset to exchange generated link sets. 135

136. 3.4.3. Knowledge Enrichment Methods and tools for duplicate detection and resolution: • Legato is a linking tool based on indexing techniques. • It implements the following steps: 1. data cleaning that filters properties from two input datasets. For example, properties that do not help the comparison. 2. Instance profiling that creates instance profiles based on Concise Bounded Description for the source. 3. Pre-matching that applies indexing techniques (it takes TF-IDF values), filters such as tokenization and stop-words removal, and cosine similarity to preselect the entity links. 4. Link repairing that validates each link produced by searching for a link to a target source. 136

137. 3.4.3. Knowledge Enrichment Methods and tools for duplicate detection and resolution: • SERIMI tries to match instances between two datasets. • It has three steps: • property selection, allows users to select relevant properties from source dataset, • the selection of candidates from a target dataset, uses string matching of properties to select a set of candidates, and • the disambiguation of candidates, measures the similarity for each candidate applying a contrast model, which returns a degree of confidence. • ADEL, Duke, Dedupe, LIMES, ... 137

138. 3.4.3. Knowledge Enrichment Property-Value-Statements correction: • KnoFuss allows data fusion using different methods. • The workflow of KnoFuss is as follows: 1. It receives a dataset to be integrated into the target dataset, 2. It performs co-referencing using a similarity method, detects conflicts utilizing ontological constraints, and resolve inconsistencies 3. It produces a dataset to be integrated into the target dataset. 138

139. 3.4.3. Knowledge Enrichment Property-Value-Statements correction: • ODCleanStore is a framework for cleaning, linking, quality assessment, and fusing RDF data. • The fusion module allows users to configure conflict resolution strategies based on provenance and quality metadata. e.g. : 1. an arbitrary value, ANY, MIN, MAX, SHORTEST or LONGEST is selected from the conflicting values, 2. computes AVG, MEDIAN, CONCAT of conflicting values, 3. the value with the highest (BEST) aggregate quality is selected, 4. the value with the newest (LATEST) time is selected, and 5. ALL input values are preserved. 139

140. 3.4.3. Knowledge Enrichment Property-Value-Statements correction: • Sieve, is a framework that consists of two modules; a Quality assessment module and a Data Fusion module. • The Data Fusion module describes various fusion policies that are applied for fusing conflicting values. • FAG, FuSem, MumMer, … 140

141. ex:Eiffel_Tower s:address ex:Champ_de_Mars ex:France s:LandmarksOrHistoricalBuildings “5 Avenue Anatole France” “Paris” “75007” s:TouristAttraction s:Place “fr” s:Country 3.4.3. Knowledge Enrichment ex: http://example.org/ s: http://schema.org/ 141

142. wd:Q243 wd:Q2873520 wd:Q142 wd:Q217925 31.03.1889 wd:Q778243 “Eiffel Tower” “Champ de Mars” “France” “Stephen Sauvestre” “avenue Anatole-France” An excerpt from the Wikidata entity of Eiffel Tower 142 3.4.3. Knowledge Enrichment

143. Assume, we want to enrich the landmarks in our Knowledge Graph. Integrate the Instances Identify New Sources Integrate the Schema 143 3.4.3. Knowledge Enrichment assumption: highest ranked external source

144. Integrate the Instances Identify New Sources Integrate the Schema Schema.org Type Wikidata Type Schema.org Property* Wikidata Property LandmarksOrHistoricalBuildings landmark address/streetAddress located on street.label address/addressCountry country ex:architect architect ex:openingDate date of official opening *Includes properties from an extension 144 3.4.3. Knowledge Enrichment assumption: highest ranked external source

145. Integrate the Instances Identify New Sources Integrate the Schema Duplicates wd:Q243 “Eiffel Tower” ex:Eiffel_Tower We found a duplicate instance after integrating landmark instances from Wikidata. Identification of duplicates is typically done by applying similarity metrics to a set of property values on both instances. 145 3.4.3. Knowledge Enrichment assumption: highest ranked external source

146. Integrate the Instances Identify New Sources Integrate the Schema Duplicates Conflicting Property Values ex:Eiffel_Tower s:address “5 Avenue Anatole France” “Champ de Mars” “avenue Anatole- France” Too many street addresses! Delete two property value assertions not even a street 146 3.4.3. Knowledge Enrichment

147. 3.4.3. Knowledge Enrichment: Our approach • Enrichment Framework: Identifies duplicates in Knowledge Graphs and resolves conflicting property values. • Workflow: • Input: a Knowledge Graph. • Duplicate Detection Process: semi-automatic feature selection, data normalization, setup (e.g. similarity metrics), run, and duplicate entities viewer. • Resolving Conflicting Property Values: define fusion strategies (e.g. decides what to do based on similarity values), run, monitoring fusion process Work- in-progress. • Output: Report of duplicate entities found and fused. 147

148. 3.4.3. Knowledge Enrichment: Our approach Duplicate Detection as a Service (DDaaS) is a service-oriented framework that allows linking duplicate instances within a Knowledge Graph or among Knowledge Graphs. • Input: Knowledge Graph(s) • Workflow: First, the dataset(s) is indexed, second, DDaaS performs a blocking procedure that filters obvious non-duplicates, afterwards, it compares all left instances, finally, DDaaS computes a similarity score for all instances. • Output: Report of instances together with a similarity value between 0 and 1. 148

149. 3.4.3. Knowledge Enrichment: Our approach DDaaS consists of multiple services for different tasks: • Indexing and blocking allows the indexing of datasets to perform very fast search operations and the filtering of obvious non-matching pairs to reduce the number of comparisons. • Record matching calculates an aggregated score between two instances based on the similarity of all their common attributes. • Gold standard selects a representative subset as a training model to help improve the configuration learning service. • Configuration learning generates a tuned configuration that efficiently maximizes the identification of duplicates. 149

150. 3.4.3. Knowledge Enrichment: Our approach • DDaaS development was influenced by three other tools; Duke, LIMES, and Silk. • We evaluate DDaaS and the three tools over two datasets; Restaurants and SPIMBENCH. • Results of this first iteration show great potential and leave much space for possible future improvements, such as blocking enhancements, less network overhead, orchestration by using an Apache Kafka instance, and many more. 150 https://www.cs.utexas.edu/users/ml/riddle/ https://project- hobbit.eu/challenges/om2020/

151. 3.4.4. Knowledge Deployment • Building, implementing, and curating Knowledge Graphs is a time- consuming and costly activity. • Integrating large amounts of facts from heterogeneous information sources does not come for free. • [Paulheim, 2018] estimates the average cost for one fact in a Knowledge Graph between $0,1 and $6 depending on the amount of mechanization. 151

152. 3.4.4. Knowledge Deployment Name Instances Facts Types Relations DBpedia (English) 4,806,150 176,043,129 735 2,813 YAGO 4,595,906 25,946,870 488,469 77 Freebase 49,947,845 3,041,722,635 26,507 37,781 Wikidata 15,602,060 65,993,797 23,157 1,673 NELL 2,006,896 432,845 285 425 OpenCyc 118,499 2,413,894 45,153 18,526 Google´s Knowledge Graph 570,000,000 18,000,000,000 1,500 35,000 Google´s Knowledge Vault 45,000,000 271,000,000 1,100 4,469 Yahoo! Knowledge Graph 3,443,743 1,391,054,990 250 800 152

153. 3.4.4. Knowledge Deployment • We build a knowledge access layer on top of the Knowledge Graph helping to connect this resource to applications. • Knowledge management technology: • based on graph‐based repositories host the Knowledge Graph (as a semantic data lake). • The knowledge management layer is responsible for storing, managing and providing semantic description of resources. • Inference engines based on deductive reasoning engines: • implements agents that defines view on this graph together with context data on user requests. • It accesses the graph to gain data for its reasoning that provides input to the dialogue engine interacting with the human user. 153

154. 3.4.4. Knowledge Deployment What are the reasons: • Scalability issues (Trillions of triples) • Context refinement for (support different points of view) • introduce rich constraints (Knowledge Cleaning) • additional knowledge derivation (Knowledge Enrichment). • Provide a reusable application layer / middle ware on top of a knowledge graph • access rights • integrates additional information sources from the application • context, • personalization, • task etc. 154

155. 3.4.4. Knowledge Deployment 155 Knowledge Graph Input

156. 3.4.4. Knowledge Deployment 156 Knowledge Access & Representation Layer Components

157. 3.4.4. Knowledge Deployment 157 Knowledge Activator

158. 3.4.4. Knowledge Deployment 158 Knowledge Activator - Specifications & Engines

159. 3.4.4. Knowledge Deployment 159 Knowledge Access & Representation Layer

160. 3.4.4. Knowledge Deployment 160 Knowledge can be deployed to power various applications. Examples are: • semantic annotations on the web (the main development reason in the beginning), • data analytics, and • conversational agents.

161. 3.4.4. Knowledge Deployment 161 { "@context": "http://schema.org", "@type": "Recipe", "image": "https://img.chefkoch- cdn.de/rezepte/1031841208350942/bilder/1324326/crop- 960x540/kaiserschmarrn-tiroler-landgasthofrezept.jpg", "name": "Kaiserschmarrn - Tiroler Landgasthofrezept", "recipeIngredient": [ "100 g Rosinen", "5 EL Rum oder Cognac oder Wasser", "6 Eigelb", "1 Pck. Bourbon-Vanillezucker", "1 EL, gehäuft Zucker", "1 Prise(n) Salz", "250 g Mehl", "500 ml Milch", "50 g Butter , zerlassen", "6 Eiweiß", "4 TL Puderzucker", "n. B. Butter zum Braten, ca. 15 - 25 g je Pfanne" ], ….

162. 3.4.4. Knowledge Deployment 162 Data analytics based on Tyrolean Tourism Knowledge Graph • Historical data stored in Tyrolean Tourism Knowledge Graph in different Named Graphs. • Each Named Graph has provenance information attached. • A SPARQL query calculates average Minimum and maximum prices across different named graphs that were imported from TVB Mayrhofen data sources for accommodation (i.e. feratel).

163. 3.4.4. Knowledge Deployment: Service-driven Dialog 163 • Scalable dialog system development • Knowledge Graph containing semantic web service annotations must decouple dialog systems from web services they consume. • An example of decoupling knowledge from the communication channel. • Example: A dialog system generated from feratel API, later extended with ticketmaster API for events, without re-programming the backend. • https://dialsws.xyz

164. 3.4.4. Deployment: Service-driven Dialog 164 User 1. understand Intent + Parameters 2. select web service schema:Action instances 3. invoke Result with potential actions 4. Natural Language Generation “need a room for 2 in mayrhofen from Friday to Sunday”

165. 3.4.4. Deployment: Service-driven Dialog 165 User 1. understand Intent + Parameters 2. select web service schema:Action instances 3. invoke Result with potential actions 4. Natural Language Generation “need a room for 2 in mayrhofen from Friday to Sunday” Classifies to an intent and extracts slot values

166. 3.4.4. Deployment: Service-driven Dialog 166 User 1. understand Intent + Parameters 2. select web service schema:Action instances 3. invoke Result with potential actions 4. Natural Language Generation Intent: SearchLodgingReservation checkinDate: 23.04.2021 checkoutDate : 25.04.2021 numAdults: 2 location: Mayrhofen “need a room for 2 in mayrhofen from Friday to Sunday” Classifies to an intent and extracts slot values

167. 3.4.4. Deployment: Service-driven Dialog 167 User 1. understand Intent + Parameters 2. select web service schema:Action instances 3. invoke Result with potential actions 4. Natural Language Generation Intent: SearchLodgingReservation checkinDate: 23.04.2021 checkoutDate : 25.04.2021 numAdults: 2 location: Mayrhofen check constraints defined by the relevant action shapes based on the values extracted from user utterances “need a room for 2 in mayrhofen from Friday to Sunday” Classifies to an intent and extracts slot values

168. 3.4.4. Deployment: Service-driven Dialog 168 User 1. understand Intent + Parameters 2. select web service schema:Action instances 3. invoke Result with potential actions 4. Natural Language Generation Intent: SearchLodgingReservation checkinDate: 23.04.2021 checkoutDate : 25.04.2021 numAdults: 2 location: Mayrhofen check constraints defined by the relevant action shapes based on the values extracted from user utterances “need a room for 2 in mayrhofen from Friday to Sunday” Classifies to an intent and extracts slot values Suitable actions for the intent are selected and further input parameters collected through dialog if necessary

169. 3.4.4. Deployment: Service-driven Dialog 169 User 1. understand Intent + Parameters 2. select web service schema:Action instances 3. invoke Result with potential actions 4. Natural Language Generation Intent: SearchLodgingReservation checkinDate: 23.04.2021 checkoutDate : 25.04.2021 numAdults: 2 location: Mayrhofen check constraints defined by the relevant action shapes based on the values extracted from user utterances “need a room for 2 in mayrhofen from Friday to Sunday” Classifies to an intent and extracts slot values Suitable actions for the intent are selected and further input parameters collected through dialog if necessary Selected action is invoked with a schema.org action annotation that contains the provided values { “@type”: “SearchAction” “object”: “LodgingReservation”, “checkInDate”: “23.04.2021” “checkOutDate”: “25.04.2021” …

170. 3.4.4. Deployment: Service-driven Dialog 170 User 1. understand Intent + Parameters 2. select web service schema:Action instances 3. invoke Result with potential actions 4. Natural Language Generation Intent: SearchLodgingReservation checkinDate: 23.04.2021 checkoutDate : 25.04.2021 numAdults: 2 location: Mayrhofen check constraints defined by the relevant action shapes based on the values extracted from user utterances schema:LodgingReservation instances with various offers are returned with potential actions (e.g. BuyAction) “need a room for 2 in mayrhofen from Friday to Sunday” Classifies to an intent and extracts slot values Suitable actions for the intent are selected and further input parameters collected through dialog if necessary Selected action is invoked with a schema.org action annotation that contains the provided values { “@type”: “SearchAction” “object”: “LodgingReservation”, “checkInDate”: “23.04.2021” “checkOutDate”: “25.04.2021” …

171. 3.4.4. Deployment: Service-driven Dialog 171 User 1. understand Intent + Parameters 2. select web service schema:Action instances 3. invoke Result with potential actions 4. Natural Language Generation Intent: SearchLodgingReservation checkinDate: 23.04.2021 checkoutDate : 25.04.2021 numAdults: 2 location: Mayrhofen check constraints defined by the relevant action shapes based on the values extracted from user utterances schema:LodgingReservation instances with various offers are returned with potential actions (e.g. BuyAction) “need a room for 2 in mayrhofen from Friday to Sunday” Classifies to an intent and extracts slot values Suitable actions for the intent are selected and further input parameters collected through dialog if necessary Selected action is invoked with a schema.org action annotation that contains the provided values { “@type”: “SearchAction” “object”: “LodgingReservation”, “checkInDate”: “23.04.2021” “checkOutDate”: “25.04.2021” … The results are turned into NL statements. Potential actions are presented to the user to continue with the conversation: “Doubleroom for 220 EUR. Do you want to buy this offer?”

172. 4. The Proof Of The Pudding Is In The Eating • Open Touristic Knowledge Graphs • Tyrolean Tourism Knowledge Graph: Integrates heterogenous data from 10+ Destination Management Organizations (DMO) - 12B+ triples. University prototype and show case. • German Tourism Knowledge Graph - Integrates heterogenous data from the Regional Marketing Organizations (LMO*) in Germany. Real world application • Dialog systems • Conversational agents that help users to achieve their goals with the help of Knowledge Graphs: • Knowledge Graphs as a source of domain knowledge (about static, dynamic and active data) • Knowledge Graphs for training Natural Language Understanding models 172 * Landesmarketing Organization

173. 4.1 Open Touristic Knowledge Graph What is an Open Knowledge Graph? In essence: a graph database that complies with the 5* Open Data Principles: 173 https://5stardata.info open license machine readable open format complies with rdf linking to other data sets

174. 4.1 Open Touristic Knowledge Graph Prominent examples of Open Knowledge Graphs? The Linked Open Data Cloud: Contains (as of May 2020): ● 1.255 data sets ● 16,174 links 174 https://lod-cloud.net/

175. 4.1 Open Touristic Knowledge Graph Linked Open Data (LOD) Use LOD to integrate and lookup data about ● hiking trails ● places and routes ● points-of-interest ● ski slopes ● time-tables for public transport 175 175 hiking trails points-of-interest ski slopes Time-tables for public transport

176. 4.1.1 Tyrolean Tourism Knowledge Graph We build the Tyrol Knowledge Graph (TKG) as a nucleus for this initiative • It is a five star linked open data set published in GraphDB providing a SPARQL endpoint for the provisioning of touristic data of Tyrol, Austria. • The TKG currently contains data about touristic infrastructure like accommodation businesses, restaurants, points of interests, events, recipes, et. • These data of the TKG fall under three categories of data: • Static data is information which is rarely changing like the address of a hotel. • Dynamic data is fast changing information, like availabilities and prices. • Active data describe actions that can be executed. • Currently the TKG contains over 12B+ statements, 55% are explicit and 45% are inferred. • https://tirol.kg/ 176

177. 4.1.1 Tyrolean Tourism Knowledge Graph • The Tyrol Knowledge Graph (TKG) integrates and connects data from 11 DMOs and several sources including: • touristic data sources: • open data sources: • It includes entities of the following (schema.org) types: • LocalBusiness • POIs, Infrastructure • SportsActivityLocations (e.g. Trails, SkiResorts) • Events • Offers • WebCams • Mobility and Transport 177

178. 4.1.1 Tyrolean Tourism Knowledge Graph SkiRoute CableCar Slope SkiResort Touristic Knowledge Graph excerpt SkiResort, Lifts, Slopes, WebCams ChairLift WebCam Data Visualisation (based on GraphDB) containedInPlace SkiLift TBar SnowReport subClassOf containedInPlace 178

179. 4.1.1 Tyrolean Tourism Knowledge Graph The Tyrol Knowledge Graph is used to answer questions such as: • “Where can I have traditional Tyrolean food when going cross country skiing?” • “Show me WebCams near Kölner Haus” • “How many people are living in Serfaus?” 179

180. 4.1.2 German Tourism Knowledge Graph • The German Tourism Knowledge Graph will integrate semantically annotated tourism-related data from 16 LMOs/Magic Cities. • Contracted by the Deutsche Zentrale für Tourismus (DZT), planned to be finished in 2022. • An ecosystem for Knowledge Creation, Hosting and Deployment is being built. • Read more at: https://open-data-germany.org/open-data-germany/ 180

181. 4.1.2 German Tourism Knowledge Graph • Knowledge Creation • Scalable Import API based on schema.org and domain-specific patterns • Provenance tracking • Machine-readable licenses for the data at different levels (e.g. organization and instance) • Knowledge Hosting • GraphDB with reasoning support • Interfaces for exporting subgraphs of the Knowledge Graph (e.g. per LMO) • Knowledge Curation • Assessment: on-demand and periodic • Cleaning: Integrity constraint-based detection and semi-automated correction • Enrichment: Duplicate detection and linking of external sources • Knowledge Deployment • SPARQL endpoint with GeoSPARQL support • Various visualization options: graph, tabular and map • Access via a RESTful API 181

182. 4.1.2 German Tourism Knowledge Graph 182 https://open-data-germany.org/knowledge-graph-kuenstliche-intelligenz/ The Knowledge Graph will foster the development of intelligent applications for German tourism

183. 4.1.2 German Tourism Knowledge Graph • Compliant with the standards developed by The Open Data Tourism Alliance (ODTA). • The consortium consists of 183

184. 4.1.2 German Tourism Knowledge Graph The Open Data Tourism Alliance (ODTA) • develops a de-facto standard for semantic annotation of touristic content, data, and services in the D-A-CH area and Italy (the group was formerly known as DACH-KG) • based on schema.org and its adaptation by Domain Specifications • it should become the backbone of an 5* Open Data Knowledge Graph for touristic data in D-A-CH and Italy *) The dataset gets awarded one star if the data are provided under an open license. **) Two stars, if the data are available as structured data. ***) Three stars, if the data are also available in a non-proprietary format. ****) Four stars if URIs are used, that the data can be referenced and *****) five stars, if the data set are linked to other data sets that can provide context. https://www.tourismuszukunft.de/2019/05/dach-kg-neue-ergebnisse-naechste-schritte-beim-thema-open-data/ 184

185. 4.1.2 German Tourism Knowledge Graph Members of ODTA • Touristic experts from the DACH-region (Germany (D), Austria (A), Switzerland (CH)) and Italy (South-Tyrol) • the Austrian and German touristic associations, • LTOs (Tirol, Vorarlberg, Wien, Brandenburg, Thüringen, …) • Associated: DMOs (Mayrhofen, Seefeld, …) • STI Innsbruck and STI International • Planned is an extension by technology providers (Datacycle, Feratel, Hubermedia, infomax, LandinSicht, Onlim, Outdooractive, TSO, ...) 185

186. 4.2. Virtual Agents • The global conversational AI market is expected to grow from USD 4.8 billion in 2020 to USD 13.9 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 21.9% during the forecast period. • Google Trends shows, that interest in chatbots has increased almost 5 times from 2015- 2020. In fact, 1.4 billion people use messaging apps and are willing to talk to chatbots. • For businesses, chatbots are the brand communication with the largest growth - with chatbot usage seeing a 92% use increase since 2019. • 67% of worldwide consumers interacted with a chatbot to get customer support between 2019-2020. Their willingness to engage with chatbots in a variety of ways, with usage for purchases, meeting scheduling and mail list sign-ups was more than doubling from 2019 to 2020. 186 https://acquire.io/blog/chatbots-trends/ https://www.drift.com/blog/state-of-conversational-marketing/ https://www.invespcro.com/blog/chatbots-customer-service/ https://www.marketsandmarkets.com/Market-Reports/conversational-ai-market-49043506.html https://research.aimultiple.com/chatbot-stats/

187. • Voice has become more users' first choice for mobile search. 65% of consumers ages 25-49 years old talk to their voice-enabled devices daily. • Artificial intelligence-based voice assistance (AI-voice) are becoming a primary user interface for all digital devices – including smartphones, smart speakers, personal computers, automobiles, and home appliances. • In 2020, more than 1 billion devices worldwide were equipped with Google’s AI-voice Assistant, and more than hundred million Amazon Alexa devices have been sold up to then – and neither number accounts for devices equipped with voice assistants from Apple, Microsoft, Samsung, or across the digital worlds of China and Asia. • Around 4.2 billion digital voice assistants are currently being used in devices around the world. By 2024, the number of digital voice assistants will reach 8.4 billion units – a number higher than the world’s population. 4.2. Virtual Agents 187 https://blog.google/products/assistant/ces-2020-google-assistant/ https://www.pwc.com/us/en/services/consulting/library/consumer-intelligence-series/voice-assistants.html https://searchengineland.com/voice-gaining-on-mobile-browser-as-top-choice-for-smartphone-based-search-313433 https://www.statista.com/statistics/973815/worldwide-digital-voice-assistant-in-use/ https://www.theverge.com/2019/1/4/18168565/amazon-alexa-devices-how-many-sold-number-100-million-dave-limp

188. 4.2. Virtual Agents Onlim • A pioneer in automating customer communication via Conversational AI • Enterprise solutions for making data and knowledge available for conversational interfaces. • Team of 35+ highly experienced AI experts, specialists in semantics and data science; experienced management team. • Academic Spin-off of University of Innsbruck. • HQ in Austria. 188 Tourism Retail Finance Education Utilities

189. 4.2. Virtual Agents Onlim 189 Knowledge Graph – Data platform Conversational & Analytics platform Multi-channel platform Integrates chatbots with data, delivers insights, and optimizes conversations & the knowledge graph. Connects the chatbot to website chat, voice assistants, messengers, phone systems and external APIs. Integrates enterprise data and services, interlinks them and applies semantics for machine understanding of these data.

190. 4.2. Virtual Agents 190 Starting with tourism we have entered various other customers and sectors: Tourism Retail Finance Education Utilities

191. 4.2. Virtual Agents Our aim: • Establish a maximally automated knowledge lifecycle: NLU training, Query generation, Querying and representing world knowledge, as well as Natural Language Generation. • Automatically distribute knowledge into all available channels. • Core are methodologies, methods, and tools to generate, host, curate, deploy, and access Knowledge Graphs containing frillions of statements from heterogeneous, distributed, and dynamic sources. 191

192. 5. Conclusions: What are Knowledge Graphs? • A big mess of frillions of facts describing the world from multiple point of views. • A Rosseta stone allowing humans and machines to exchange meaning. • Merging smart and big data into one new paradigm of explainable Artificial Intelligence and explicit representation of large-scale knowledge (not data). • Capturing large fraction of human knowledge explicitly turning it into a brain for/of human kind. • A thrilling area of research and engineering with large impact and deep issues to be resolved which you may want to join. 192

193. 5. Conclusions: Why KGs are important? • Knowledge Graphs are enabling technology for: • Virtual agents (Information search, eMarketing, and eCommerce) • Cyperphysical Systems (Internet of the Thing, Smart Meters, etc.) • Physical Agents (drones, cars, satellites, androids, etc.) • Because knowledge is power and without you look like a foolish. • Statistical Analysis or matrix multiplication of large data volumes can bring you a long way but lack the integration of world knowledge and the understandable interaction (explainable AI) with humans. 193

194. 5. Conclusions • Turning them into a useful resource for problem-solving is not a trivial task as there are various challenges: • Size / Scalability • Heterogenity • Active and Dynamic data • Quality: Correctness and Completeness 194

195. 5. Conclusions • We developed a methodology for developing and maintaining (large) KGs: • Task and process model • A work bench (semantify) • A large number of commercial applications (Onlim) • Large and open knowledge graphs • Knowledge Graph enabled chat bot solutions 195

196. 5. Conclusions Knowledge Graph Maintenance Knowledge Hosting Knowledge Curation Knowledge Deployment Knowledge Assesment Knowledge Cleaning Knowledge Enrichement Error Detection Error Correction Evaluation Correctness Completeness Knowledge Source detection Knowledge Source integration Duplicate detection Property-Value- Statements correction Knowledge Creation Edit Semi-automatic Automatic Mapping 196

197. 5. Conclusions: Workbench Semantify.it A platform for • creation • evaluation and • deployment of web annotations and knowledge graphs. https://semantify.it Research prototype proceed with caution! 197

198. 5. Conclusions: Applications 198 • Knowledge graphs become the future storage for digital assets for companies. • Knowledge will be accessed and unlocked literally by asking questions in text/voice. • Access to knowledge will be through multi channels: Chatbots, Voice Assistants, Telephone Assistants, Search Widgets, Augmented Reality, Virtual Reality, etc. • Applications will reach all verticals: utility, retail, tourism, banking, etc.

199. 5. Conclusion Lorem ipsum • Lorem ipsum 199

200. 5. Conclusion 200

201. Epilogue 4/1/2021 www.sti-innsbruck.at 201

209. 4/1/2021 www.sti-innsbruck.at 209 Epilogue

210. 210 Questions?

Knowledge Graphs: Smart Big Data

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