Semantic Web Services SS 2018

1. Semantic Web Services SS 2018 Semantic Services as a Part of the Future Internet and Big Data Technology Anna Fensel 14.05.2018

2. Where are we?

3. Outline • Motivation • Big Data, Smart Data, Linked (Open) Data • Semantic Web Evolution in One Slide • What is Big Data? • Public Open Data • Linked (Open) Data • Data Economy & Valorization • Future Internet – FI-WARE • Definitions, EU Initiative • Technical Examples from FI-WARE • Converged Participatory Services • Definitions • Technical Examples • Summary • References

4. MOTIVATIONSlides TAKEN from presentation of L. Nixon: “Limitations of the current internet for the future internet of services”, http://www.slideshare.net/mbasti2/sofi-servicearchitectures300910

13. Big Data, Smart Data, Linked (Open) Data

14. Going mainstream and broad Linked Open Data cloud counts 25 billion triples Open government initiatives BBC, Facebook, Google, Yahoo, etc. use semantics SPARQL becomes W3C recommendation Life science and other scientific communities use ontologies RDF, OWL become W3C recommedations Research field on ontologies and semantics appears Term „Semantic Web“ has been „seeded“, Scientific American article, Tim Berners-Lee et al. Semantic Web Evolution in One Slide 2010 2008 2004 Source: Open Knowledge Foundation 2001

15. From Semantic Web to Semantic World: Data Challenges • Large volumes of raw data to smaller volumes of „processed“ data • Streaming, new data acquisition infrastructures • Data modeling, mining, analysis, processing, distribution • Complex event processing (e.g. in-house behaviour identification) • Data which is neither „free“ nor „open“ • How to store, discover and link it • How to sell it • How to define and communicate its quality / provenance • How to get the stekeholders in the game, create marketplaces • Establishment of radically new B2B and B2C services • „Tomorrow, your carton of milk will be on the Internet“ – J. da Silva, referring to Internet of Things • But how would the services look like?

16. What is Big Data? Infromation Explosion in data and real world events (IBM) • “Big data” is a loosely-defined term • used to describe data sets so large and complex that they become awkward to work with using on-hand database management tools. • White, Tom. Hadoop: The Definitive Guide. 2009. 1st Edition. O'Reilly Media. Pg 3. • MIKE2.0, Big Data Definition http://mike2.openmethodology.org/wiki/Big_Data_Definition

17. Big Data Application Areas Picture taken from http://www-01.ibm.com/software/data/bigdata/industry.html

18. Use case : Climate Research • Eiscat and Eiscat 3D are multimillion reserch projects doing environmental research as well as evaluation of the built infrastructures. • Observation of climate: sun, troposphere, etc. • Simulations, e.g. Creation of artificial Nothern light • Run by European Incoherent Scatter Association • 1,5 Petabytes of data are generated daily (1,5 Million Gigabytes). • Processing of this data would require 1K petaFLOPS performance • Or 1 billion Euro electricity costs p.a.

19. Large Scale Reasoning • Performing deductive inference with a given set of axioms at the Web scale is practically impossible • Too manyRDF triples to process • Too much processing power is needed • Too much time is needed • LarKC aimed at contributing to an ‘infinitely scalable’ Semantic Web reasoning platform by • Giving up on 100% correctness and completeness (trading quality for size) • Include heuristic search and logic reasoning into a new process • Massive parallelization (cluster computing)

20. Volumes of Data Exceed the Availale Storage Volume Globally There is a need to throw the data away due to the limited storage space. Before throwing the data away some processing can be done at run-time • Processing streams of data as they happen

21. Data Stream Processing for Big Data • Logical reasoning in real time on multiple, heterogeneous, gigantic and inevitably noisy data streams in order to support the decision process… -- S. Ceri, E. Della Valle, F. van Harmelen and H. Stuckenschmidt, 2010 window Query engine takes stream subsets for query answering Registered Continuous Query Extremely large input streams streams of answer Picture taken from Emanuele Della Valle “Challenges, Approaches, and Solutions in Stream Reasoning”, Semantic Days 2012

22. Public Open Data - Data.gv.at

23. Data.gv.at (Vienna)

24. Open Data Vienna Challenge Contest 50 apps with OGD Vienna - now nearly 80 (March 2013) https://www.newschallenge.org/open/open-government/submission/open-government-city-of-vienna/

25. Public Open Data • Openess: Open Data is about changing behaviour • Heterogenity: Different vocabularies are used • Interlinkage: Need to link these data sets to prevent data silos •  Linked Open Data

26. Web of Documents Fundamental elements: Names (URIs) Documents (Resources) described by HTML, XML, etc. Interactions via HTTP (Hyper)Links between documents or anchors in these documents Shortcomings: Untyped links Web search engines fail on complex queries Motivation: From a Web of Documents to a Web of Data Hyperlinks “Documents”

27. Web of Documents Web of Data Motivation: From a Web of Documents to a Web of Data Typed Links Hyperlinks “Documents” “Things”

28. Characteristics: Links between arbitrary things (e.g., persons, locations, events, buildings) Structure of data on Web pages is made explicit Things described on Web pages are named and get URIs Links between things are made explicit and are typed Web of Data Motivation: From a Web of Documents to a Web of Data Typed Links “Things”

29. 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 about 3.5 billion facts about 500 million objects

30. Semantic Web: knowledge graph & rich snippets

31. Linked Data – a definition and principles • Linked Data is about the use of Semantic Web technologies to publish structured data on the Web and set links between data sources. Figure from C. Bizer

32. 5-star Linked OPEN Data ★ Available on the web (whatever format) but with an open licence, to be Open Data ★★ Available as machine-readable structured data (e.g. excel instead of image scan of a table) ★★★ as (2) plus non-proprietary format (e.g. CSV instead of excel) ★★★★ All the above plus, Use open standards from W3C (URIs, RDF and SPARQL) to identify things, so that people can point at your stuff ★★★★★ All the above, plus: Link your data to other people’s data to provide context

33. Linked Open Data – silver bullet for data integration • Linked Open Data can be seen as a global data integration platform • Heterogeneous data items from different data sets are linked to each other following the Linked Data principles • Widely deployed vocabularies (e.g. FOAF) provide the predicates to specify links between data items • Data integration with LOD requires: • Access to Linked Data • HTTP, SPARQL endpoints, RDF dumps • Crawling and caching • Normalize vocabularies – data sets that overlap in content use different vocabularies • Use schema mapping techniques based on rules (e.g. RIF, SWRL) or query languages (e.g. SPARQL Construct, etc.) • Resolve identifies – data sets that overlap in content use different URIs for the same real world entities • Use manual merging or approaches such as SILK (part of Linked Data Integration Framework) or LIMES • Filter data • Use SIVE ((part of Linked Data Integration Framework) See: http://www4.wiwiss.fu-berlin.de/bizer/ldif/

34. What is Data Economy? • Non tangible assets (i.e. data) play a significant role in the creation of economic value • Data is nowadays more important than, for example, search or advertisement • The value of the data, its potential to be used to create new products and services, is more important than the data itself

35. Why a Data Economy? • New businesses can be built on the back of these data: Data are an essential raw material for a wide range of new information products and services which build on new possibilities to analyse and visualise data from different sources. Facilitating re-use of these raw data will create jobs and thus stimulate growth. • More Transparency: Open data is a powerful tool to increase the transparency of public administration, improving the visibility of previously inaccessible information, informing citizens and business about policies, public spending and outcomes. • Evidence-based policy making and administrative efficiency: The availability of solid EU-wide public data will lead to better evidence-based policy making at all levels of government, resulting in better public services and more efficient public spending. See: http://europa.eu/rapid/pressReleasesAction.do?reference=MEMO11/891&format=HTML&aged=0&language=EN&guiLanguage=en

36. Combining Open Data and Services – Tourist Map Austria • Use LOD to integrate and lookup data about • places and routes • time-tables for public transport • hiking trails • ski slopes • points-of-interest

37. Combining Open Data and Services – Tourist Map Austria LOD data sets • Open Streetmap • Google Places • Databases of government • TIRIS • DVT • Tourism & Ticketing association • IVB (busses and trams) • OEBB (trains) • Ärztekammer • Supermarket chains: listing of products • Hofer and similar: weekly offers • ASFINAG: Traffic/Congestion data • Herold (yellow pages) • City archive • Museums/Zoo • News sources like TT (Tyrol's major daily newspaper) • Statistik Austria • Innsbruck Airport (travel times, airline schedules) • ZAMG (Weather) • University of Innsbruck (Curricula, student statistics, study possibilities) • IKB (electricity, water consumption) • Entertainment facilities (Stadtcafe, Cinema...) • Special offers (Groupon)

38. Combining Open Data and Services – Tourist Map Austria • Data and services from destination sites integrated for recommendation and booking of • Hotels • Restaurants • Cultural and entertainment events • Sightseeing • Shops

39. Combining Open Data and Services – Tourist Map Austria • Web scraping integration • Create wrappers for current web sites and extract data automatically • Many Web scraping tools available on the market

40. “There's No Money in Linked (Open) Data” http://knoesis.wright.edu/faculty/pascal/pub/nomoneylod.pdf • It turns out that using LOD datasets in realistic settings is not always easy. • Surprisingly, in many cases the underlying issues are not technical but legal barriers erected by the LD data publishers. • Generally, mostly non-technical but socio-economical barriers hamper the reuse of date (do patents and IPR protections hamper or facilitate knowledge reuse?). • Business intelligence • Dynamic Data • On the fly generation of data

41. Future Internet – FI-WAREFor this part, follow presentation of F.-M. Facca: “FIWARE Primer - Learn FIWARE in 60 Minutes”, http://www.slideshare.net/chicco785/fiware-primer-learn-fiware-in-60-minutes

42. Converged Participatory services

43. Research Aim Converged Semantic Services For Empowering Participation Aims: • Enabling efficient participation vs. current social network silos and groups • More possible roles for an individual • More roles at a time for an individual • More matching and satisfying roles for an individual => Motivation, added value and revenue increase Technologically that means: • Benefiting from data and services reuse at the maximum • Enabling participators to establish added value new and converged services on top of the data • commercially re-applying them across platforms =>There is a need to „understand“ and interlink content and objects coming from heterogeneous numerous sources

44. Young People‘s Participation • Psychology perspective: „Child-Adult“

45. Participation in Terms of Social Media

46. 90-9-1 Rule for Participation Inequality • Web use follows a Zipf distribution • Also applicable to social media • Also to working groups? • Is that wrong? • In some cases (e.g. inappropriate match), yes. • In many cases (e.g. dissemination effect), no. Jakob Nielsen, http://www.useit.com/alertbox/participation_inequality.html

47. Participation is Linked to Value • Participation level relates to the value one gets from participation • Participation also has a value in itself Lurkers‘ Perspective

48. Participation is Linked to a Role 1 person: gatherer or hunter 2 persons: gatherer and hunter? • Problem with the role choice starts from the moment where there is a choice. Having more persons implies: • fine-grained devision of labor and service economy, • community as a regulator on which roles are appropriate and which not, as well as their values.

49. Impact of Roles/Relations and their Weights on Ontology Evolution Dynamics • People and relations are inherently associated with / connected to / can be decomposed into concepts and properties. • See also: Peter Mika, „Ontologies are Us: A Unified Model of Social Networks and Semantics”. International Semantic Web Conference 2005: 522-536. • Changing the roles drive social, ontology and market evolution. • One of the important drive factors are the quantity of concepts/people relating to another concept/person via a specific property (hub vs. stub), e.g. a property spouse is stronger than friend. Thus, the networks are self-restructuring depending on the roles and weights put on them. • See also: Zhdanova, A.V., Predoiu, L., Pellegrini, T., Fensel, D. "A Social Networking Model of a Web Community". In Proceedings of the 10th International Symposium on Social Communication, 22-26 January 2007, Santiago de Cuba, Cuba, ISBN: 959-7174-08-1, pp. 537-541 (2007).

50. Convergence • “Telecommunications convergence, network convergence or simply convergence are broad terms used to describe emerging telecommunications technologies, and network architecture used to migrate multiple communications services into a single network.[1] Specifically this involves the converging of previously distinct media such as telephony and data communications into common interfaces on single devices.” • Wikipedia • Convergent technologies/services include: • IP Multimedia Subsystem • Session Initiation Protocol • IPTV • Voice over IP • Voice call continuity • Digital video broadcasting - handheld