1. Service-enabling in Financial Domain
Chapter 3
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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.
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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
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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.
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5. Financial Platform Architecture
Economic and financial information ontology
Information
Consumer
Interface
Browse
Visualize
Search
Search
results
Classification
taxonomy
Protege
Search
Engine
Ontology
instances
RDQL
Information
consumer
XML export
D2R
Ontology
designer
Content management
& provisioning tools FI DB
NewXL, XBRL,..
TIF: Spanish provider of IT to financial companies
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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
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7. TIF Model Ontology engineering Semantic content integration
Results: Improvements
Results: Limitations/problems
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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.
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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
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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.
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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.
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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.
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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)
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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.
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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.
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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
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17. Problems/Limitations in TIF Design
Maintenance of the model
Scope of the model
Creation of ontologies from scratch
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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.
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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).
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20. Investment Fund Lifecycle
National market
supervisor
Aggregation
process
Validation &
Conversion
process
Aggregated
information
Stock markets
Management
firm
Analysis
process
Management
firm
Seller
Added-value
information
Investor
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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.
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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.
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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
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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.
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