Service-enabling in Financial Domain

1. Service-enabling in Financial Domain Chapter 3

2. Introduction • Data, information and knowledge management are key activities in modern economies. • Information management involves integration of data from disparate and heterogeneous channels including information from third parties. • Financial field is a conceptually rich domain where information is complex, valuable, huge in volume, and information exchange and integration for post analysis is a key task.

3. Topics for Discussion • Description of the domain • Management of economic and financial domain • Building of explicit information model for the exchange of in the investment funds market. • Case 1: Financial content management: TIF Architecture and Implementation • Case 2: Investment Fund Market • Semantic web by Tim Berners Lee • XBRL vs OWL

4. Description of Financial domain • Huge amount of information is produced world-wide everyday • Interpretation and processing is hard and time consuming • Manual management is error-prone and consuming • Searches on these data are oftem imprecise, queries are complex. • Need efficient filtering, search and browsing mechanisms • Provision for efficient production, management and delivery technologies.

5. Ontology instances Financial Platform Architecture Economic and financial information ontology Information Consumer Interface Browse Visualize Search Search results Classification taxonomy Protege Search Engine RDQL Information consumer XML export D2R Ontology designer Content management & provisioning tools FI DB NewXL, XBRL,.. TIF: Spanish provider of IT to financial companies

6. Main Components • Content management and provision tools • Relational database management system • Information ontology (supporting taxonomies) • RDBMS to Ontological converter • Ontology design tool • Import and export facilities: import from corporate databases; export XML and Web services • Search engine based on ontology • Visualization tools for information consumers and managers

7. TIF Model • Ontology engineering • Semantic content integration • Results: Improvements • Results: Limitations/problems

8. Ontology Engineering Process • First task: design an explicit model for economic and finance creation of an ontology for the economic and financial domain covering the needs of this company (TIF) • First version of ontology was based on existing domain model and on corporate databases. • Then domain experts were involved to redefine ontology, adding missing concepts, relations and properties and evaluate the outcome of the first step. • This process resulted in four root ontology classes.

9. Root Ontology Classes • Interaction of financial and technical staff led to four distinct kinds of concepts (classes) in ontology: • Contents: types of documents and contents created by domain experts. Ex:? • Classification categories: to categorize the contents generated according to topics, sectors etc. Ex: ? • Entities: items that are references in the contents. Ex: banks, organizations, people, events, information sources. • Enumerated types: provide values (controlled vocabularies) for certain properties. • Resulting ontology provides explicit connections between contents, categories, and other concepts that were only semi-explicit in the current content management system. • Allows for more expressive and precise search capabilities. • Automation of the generation of user interfaces (query input forms, presentation views, content provision forms) • Scalable machine to machine interaction

10. Choice of ontology language • For the description of ontology RDF was chosen. • One of the W3C (World Wide Web Consortium) recommended standards. • At that time widest support available • Ontology definition tool Protégé was used for ontology engineering. • OWL, another standard from W3C will be used in future version.

11. Semantic content integration: Integration of legacy content • Engineered ontology allows for new forms of contents to be added according to the model. • How about legacy data? Data generated prior to ontology definition? • Huge volumes of information stored in corporate databases in relational model. • In order for the legacy assets to benefit from the semantic architecture, new ontological descriptions are added to the engineered model • Open source tool D2R was used to extract information from relational db using XML mapping and into RDF instances. • Jena provides persistent storage of RDF data in relational databases. • Both old data and new form of data are kept.

12. Semantic Content Integration: Ontology-based search • Search by customers, content providers, content managers, administrators to query for contents. • Search module allows for full structured search in terms of any dimensions of the ontology and allows setting different levels of detail for expressing the search query. • User profiles are used for degree of exposure of the ontology. • Lets look at an application based on Amazon ECS to understand the process specified above. • Benefits: auto generation of search forms. Ontology facilitates and automates customization. • Search module converts query into RDQL which is executed against the ontology and knowledge base yielding RDF instances which match the query. The instances are presented to the user. Visualization is controlled by visualization module.

13. Semantic content integration: Information visualization • Visualization module dynamically generates the web pages from the description of ontology instances. • Visualization concepts are defined declaratively and that can be customized. • Presentation engine dynamically selects the appropriate view. • Presentation is based on JSP, with library of custom tags: • Ontology access expressions, HTML/ Javascript to display ontology constructs, layout constructs. • Three view: extended view, summary view, minimal view • User profiles are used to control the access to information. • In this content something to read about is a topic Role based access (RBAC)

14. Semantic Content Integration: Content managers • Location of right content, ease of navigation for creation, classification, maintaining and linking contents. • Creation and management of content will be in terms of the domain ontology making use of the search and visualization modules.

15. TIF Project: Experience and Results • Implementation of the TIF architecture presented resulted in knowledge base of 180,831 RDF instances and 2,70,827 statements.

16. Improvements at TIF (section 2.4) • Definition of a completely explicit information model: improves data quality • Improved search capabilities: • Automatic generation of user interfaces • Application of inference mechanisms to obtain richer search results • Interleave structured search and standard browsing • Adapt results to user profiles • Improved visualization • Improved management

17. Problems/Limitations in TIF Design • Maintenance of the model • Scope of the model • Creation of ontologies from scratch

18. Your domain and architecture • Decide on a domain for your project • Start with the requirements • Proceed to draw an architecture diagram • Discuss the components • Discuss implementation details • Discuss limitations/scope etc.

19. Description of the domain: Investment funds market • Objective is to analyze investment funds to recommend investment choices. • Availability of information on the considered funds both last minute and historical data, provisioned by different parties and in heterogeneous formats. • Other added-value information such as risk-profitability ratios need to presented for commercial funds. • Current mechanisms are not based on uniform and explicit information model: this hampers agility, quality (error-prone) and leads to reduction of market transparency • TIF + AFINet Global • About 6000 funds • Information considered are the description of all the aspects, changes on any of these aspects, net asset value (NAV) of the funds at different points of time. • Reception of information from numerous sources • Validation, transformed and aggregated into a uniform information model • Publication of analytical indicators are calculated and published via different channels including XML syndication and direct access via different information portals (and web services).

20. Management firm Management firm Seller Investor Investment Fund Lifecycle National market supervisor Aggregation process Validation & Conversion process Aggregated information Stock markets Analysis process Added-value information

21. Creation of domain model based on XBRL • XBRL was chosen over OWL or RDF. • What is XBRL? Next slide • XBRL taxonomies were selected over OWL ontologies • These can be translated from one form to another, at least that is what the text book suggests.

22. XBRL (Taxonomy) • XBRL stands for eXtensible Business Reporting Language.  It is one of a family of "XML" languages which is becoming a standard means of communicating information between businesses and on the internet. • Taxonomies, as explained in How XBRL Works, are the dictionaries used by XBRL.  They define the specific tags for individual items of data (such as "net profit").  • Different taxonomies will be required for different financial reporting purposes.  • National jurisdictions may need their own financial reporting taxonomies to reflect their local accounting regulations.  • Many different organizations, including regulators, specific industries or even companies, may require taxonomies to cover their own business reporting needs.

23. Design of an SOA for Investment Fund Markets (IFM) • Service-enable IFM • Lets start with fundamental model • Add semantic information by adding an intermediary level

24. Summary • We studied two cases in the financial domain • We studied the architecture and implementation of TIF • We described the domain of investment fund market (IFM) and designed the SOA of for this system.