Semantic integration of traditional and web-based information sources

1. Semantic integration of traditional and web-based information sources     Gergely Lukácsy, BUTE Péter Szeredi, BUTE Péter Krauth, IQSYS Attila Bodnár, IQSYS

2. What is a mashup? • A mashup is a website or application that combines content from more than one source into an integrated experience. • The etymology of this term possibly derives from its similar use in pop music. /Wikipedia/

3. Quotes on mashups • “Web mashups, and other Web 2.0 development (e.g. Ajax) are all facets of the same phenomenon that : • information and presentation are being separated in ways that allow for novel forms of reuse.” • “The mash-up is the offspring of an environment where application developers facilitate the creation of integrated, yet highly derivative application hybrids by third parties, something they do by providing rich public APIs to their user base.”

4. What’s so special about mashups? • Content used in mashups is typically sourced from a third party via a public interface or API. • Other methods of sourcing content for mashups include web feeds (e.g. RSS or Atom), web services and screen scrapping. • Some in the community believe that only cases where public interfaces are not used count as mashups. • Many people are experimenting with mashups using Google, eBay, Amazon, Flickr, and Yahoos APIs. • Google has a mashup editor in beta. ? Mashup = Application Integration á la Web 2.0

5. What we are going to speak of? • R&D project: • Sponsored by the National Research and Development Program, 2005-2007 • Consortia: • Coordinator: IQSYS • Developer Organisations: • IQSYS, BUTE, SZTAKI • User Organisations: • OSZK, MTI, ARECO/eBolt S emantic IN tegration T echnology A pplied in G rid-like, M odel-driven A rchitectures

6. Information Integration with Sintagma Presentation and further processing Database A Application A(web service) • Separates clearly the data access and transformation layers of integration from the presentation layer • Uses a comprehensive metadata repository(Model Warehouse) • Semantics of data represented in the repository: maps local and remote metadata to each other • Data access and transformation driven by the repository Data access and transformation SINTAGMA External Application(e.g. mashup application) Application B(traditional) Database B (RDBMS, XML, RDF)

7. Search and analysis of Web data Search and analysis application (e.g. mashup) d a t a s e r v i c e m e t a d a t a m a p p i n g m e t a d a t a m a p p i n g m e t a d a t a m a p p i n g Legend: SINTAGMA-node

8. Sintagma – an approach to information integration • Key Principles: • No duplication of data: Model Warehouse vs. Data Warehouse • Communication: one-way, on-line (no modification of data, instant access) • Integration of web services as information sources supported (no modification required) • Key Components: • Manages various forms of metadata (Model Manager) • Accesses various structured and semi-structured information sources (Wrappers): • RDBMS • RDF • XML • Web Services • Preprocesses various „unstructured” information sources (Annotators): • Texts • Raster maps (labels and signs) • Excel tables • Optimises query execution: query planning using deduction (Mediator) • Data Quality Control

9. Architecture of SINTAGMA maps texts SintagmaGUI Model Manager /Model Warehouse DQ Engine(meta) Mediator(local) Model Manager(remote) Data QualityController DQ log TextAnnotator RDFWrapper XMLWrapper WSWrapper JDBCWrapper WDWrapper MapServer Web Service RDBMS RDF XML HTML Text Annotation subsystem MapAnnotator DQ Engine(native) Data Quality Control subsystem Map Annotation subsystem

10. Data Source2 Data Source1 Data Source3 Data Sourcen Model Warehouse of SINTAGMA Common, clarifiedconcepts Domain specificknowledge/ontologies Conceptual viewsof workers in a business area Special concepts of business areas Domain specific terminology Conceptual Level Integrated Conceptual Model IntegratedApplication Model External model (e.g. BPM) local Application Level unified transformed local Interface Level local local local Legend: model Source Level mapping input

11. Modelling in SINTAGMA • The Model Warehouse • content of the Model Warehouse • interface models and abstractions • ontology concepts • Use cases • Product comparison • Workflow of Equipment purchase • Web service integration demos

12. Model Warehouse • Content of the Model Warehouse • Object-oriented models • Structural properties of sources in UML Object Model • Non-structural information given as OCL Constraints • Mapping between models as abstractions • Description Logic models • Queries: source and conceptual level • Classification of models • interface • unified (application) • conceptual • Modeling: SILan – Semantic Integration Language • Describes content of Model Warehouse in textual format • Has well-defined semantics

13. Interface Models

14. Higher level models • Abstractions (data transformations) • populate higher level entities • Filter low level data (suppliers) • Transform data to appropriate higher level form (clients) • can have multiple suppliers and clients

15. Higher level models (cont’d) • Invariants • have to be satisfied by all the instances of a model element • can contain navigation • Queries • can be formulated on any model • Interface level models: directly accessing data sources • higher level models: using mediation • are interchangable with abstractions

16. Conceptual Models

17. Conceptual models (cont’d) • These models encapsulate concepts given in Description Logic formalism

18. Use case 1: Product comparison • Goal: find products that are similar to the products in a host system • Information sources • catalogues from various vendors in Excel • database of the host system • Problems to solve • heterogenity of the catalogues: preprocessing • algorithm for product comparison

19. Solution in SINTAGMA Model Warehouse Similar Products Product comparison Unified Products Catalogue Host Database Excel MySQL XML Excel Excel Preprocessing

20. Use case 2: Equipment purchase

21. Equipment purchase in an organisation • Scenario • Each department maintains a wish-list of equipments • There are vendors who provide products to departments • Vendors sell different types of products (vendor A sells printers and toners, Vendor B monitors and printers etc.) • The financial department dynamically designates a preferred vendor for each product • Questions: is there any expensive order? what is teh total ? etc. • Information Sources: • Department’s wish-list: • relational database with columns description, category, e.g.: „we have run out of paper”, „15/18” • Financial department: • Web service, with operation determining where to buy a given product, e.g.: (15,8) -> (A4 paper, 4, 23) • Vendors: • Heterogenous web service which return prices, units and delivery date, e.g.: 23 -> (12, 1, 2007-07-01)

22. Event Driven Process Chain

23. Solution in Sintagma

24. Use case 3: Web Service Integration • Integrating Amazon and Barnes&Noble • Integrating RSS-sources (e.g. origo, nol, index, metro) • Integrating World Championship Results (20o2 and 2006)

25. Barnesandnoble.comweb service Amazon.comweb service Currency exchange service Integrating Amazon and Barnes&Noble Conceptual Level Price comparison Availability under limit in HUF AmazonBN Application Level Barnes&Noble Interface Level currency Amazon Legend: model mapping Source Level query input

26. Result(2006 WC)Web Service Result(2002 WC)Web service Integrating results of World Championships Positions Team positions by year derivation Team matchesby year Team matches First FourTeams Conceptual Level No of matches played by teams grouping Optimised WC matches Teams in both WCs transformation Matches in both WCs Unified WC matches Application Level Matches of teams combination Interface Level 2002 WC 2006 WC Legend: model Score: n-m Match Id: 0-63 Score1: n Score2: m Match Id: 1-64 mapping Source Level query input

27. Nol.hu RSS source Origo.huRSS-source VIP data-base metro.hu RSS source Index.hu RSS-source Integrating RSS-feeds TextAnnotator opposition goverment Search forhigh level concepts (e.g. political conflicts) Conceptual Level Search for occurances of a specific word(e.g. „budapest”) Unified RSS-feeds members of Application Level combination nol Interface Level metro index origo VIP Legend: model mapping Source Level query input

28. Summary • The system • is a semantic information integration tool • handles various structured sources • relational, various semi-structured sources and web services • preprocesses various unstructured sources • texts, maps, tables • uses logic / constraint logic programming • can be used in mashup creation • disciplined and flexible approach to data access in mashups • separates data integration from mashup presentation logic • resolves semantic and technical differences in sources

29. Real estate search - Trulia • A real estate search engine that helps you find homes for sale and provides real estate information at the local level to help you make better decisions in the process. Trulia pulls in real estate data from partnerships with thousands of brokers and agents and displays it on a Google Maps interface. • Trulia shows you how sales prices have been trending where it matters—in your county, city, ZIP code and neighborhood. They also offer heat maps and real estate guides. • http://www.trulia.com/#start

30. Hotel Guide - Trivop • The self-proclaimed first videoguide for hotels doesn’t disappoint. Locate hotels on this Google Maps + Hotel mashup and view user-created videos of the hotels. This gives a much better view of a prospective hotel before visiting. • Currently looks like they only have hotels in England and France, but with their recruiting efforts one can only assume Trivop will becoming to a region near you. • http://www.trivop.com

31. Visual Music search – Music Map • Visual music search application mashed with Amazon data. Choose and artist and album, see related artists in an abstract tree graph. Wicked. • http://www.dimvision.com/musicmap/

32. Search for Popular Music – Hype Machine • The Hype Machine follows music blog discussion. Every day, hundreds of people around the world write about music they love. • The Hype Machine tracks a variety of MP3 blogs. If a post contains MP3 links, it adds those links to its database and displays them on the front page. • Some of the frequently accessed tracks are cached by the Hype Machine server, much like Google Search caches web pages, to reduce load on the bloggers' servers and protect their bandwidth. Those tracks are NOT available for download, but you can preview them via the "listen" links that are next to each track or using your media player. • The blog that posted a particular track is identified under every track by name and with a "read post" link that leads to the blog post itself. If you enjoyed a track someone posted, stop by and let them know! • You can purchase CDs and individual tracks by using the "amazon" and "itunes" links that appear next to most tracks. Each purchase you make via the Amazon and iTunes links supports both the artists and the Hype Machine. Please buy and enjoy. • http://hypem.com/