1. Ontology-based Information Extraction for Business Intelligence
Horacio Saggion, Adam Funk, Diana Maynard, Kalina Bontcheva
Natural Language Processing Group
University of Sheffield
United Kingdom

2. Outline The MUSING Project Ontology-based IE
MUSING Natural Language Processing Technology
MUSING applications
Customisation
Results
Conclusions & Future Work

3. MUSING project
Business Intelligence (BI) is the process of finding, gathering, aggregating, and analysing information for decision making
Many systems in BI are portals which allow business analysts access to information
It is the work of the business analyst to dig into the documents in order to extract useful facts for decision making
MUSING is a 7th Framework Programme Project from the European Commission which promotes the adoption of BI tools based on semantic-based knowledge and content systems
Analytical techniques traditionally used in BI rely on structured information and hardly ever use qualitative information which the industry is keen in using (e.g. opinions)
One of the goals of MUSING is to use structured as well as unstructured information for decision making

4. Ontology-based Information Extraction (OBIE)
Information extraction (IE) is a technology which extracts key pieces of information from text
generic: identify specific name mentions in text (person names, location names, money, etc.)
specific: populate a structured representation (e.g. template) with “strings” from text (e.g., full information on a joint venture)
OBIE is the process of finding in text and other sources concepts, instances, and relations expressed in an Ontology

5. Ontology-based Information Extraction (OBIE)
Extracting information about a company requires for example identify the Company Name; Company Address; Parent Organization; Shareholders; etc.
These associated pieces of information should be asserted as properties values of the company instance
Statements for populating the ontology need to be created ( “Alcoa Inc” hasAlias “Alcoa”; “Alcoa Inc” hasWebPage “ etc.)

6. Ontology-based Information Extraction in MUSING
DATA SOURCE
PROVIDER
DOMAIN EXPERT
ONTOLOGY CURATOR
USER
DOCUMENT
MUSING
ONTOLOGY
DOCUMENT
COLLECTOR
USER INPUT
DOCUMENT
MUSING APPLICATION
MUSING DATA
REPOSITORY
ONTOLOGY-BASED
DOCUMENT
ANNOTATION
REGION
SELECTION
MODEL
ECONOMIC
INDICATORS
REGION
RANK
ENTERPRISE
INTELLIGENCE
COMPANY
INFORMATION
ANNOTATED
DOCUMENT
REPORT
DOMAIN EXPERT
ONTOLOGY
POPULATION
KNOWLEDGE
BASE
INSTANCES
&
RELATIONS

7. Data Sources and Ontology
Data sources are provided by MUSING partners and include balance sheets, company profiles, press data, web data, etc. (some private data)
Il Sole 24 ORE, CreditReform data
Companies’ web pages (main, “about us”, “contact us”, etc.)
Wikipedia, CIA Fact Book, etc.
Ontology is manually developed through interaction with domain experts and ontology curators
It extends the PROTON ontology and covers the financial, international, and IT operative risk domain

8. Partial Ontology View

9. Natural Language Processing Technology
The OBIE system for English is being developed using the GATE system ( the German and Italian systems are based on Sprout tools developed by DFKI
GATE components used include: tokeniser, sentence splitter; parts-of-speech tagger; morphological analyser; parsers; etc.
GATE comes with an extraction system called ANNIE, it targets only a small fraction of the MUSING application domain

10. Natural Language Processing Technology
Main components adapted for MUSING applications are gazetteer lists and grammars used for named entity recognition
New components include
an ontology mapping component – entities are mapped into specific classes in the given ontology
a component creates RDF statements for ontology population based on the application specification
for example create a company instance with all its properties as found in the text

11. Cross-source Coreference
One important problem to be addressed in extraction from multiple source is deciding if a person name – or any other name - occurring in two different sources refer to the same individual.
Given a set of documents containing a given person name we apply an agglomerative clustering algorithm, at the end documents referring to the same individual belong in the same cluster
The algorithm uses vector representations of the documents (terms and weights)
We experimented with two types of terms: words and entity names and our results indicate that a representation using one specific type of name (i.e., Organization) achieves state-of-the-art performance however performance varies depending on the data set

12. MUSING Applications
A number of applications have been specified to demonstrate the use of semantic-based technology in BI – some examples include
Collecting Company Information from multiple multilingual sources (English, German, Italian) to provide up-to-date information on competitors
Identifying Chances of success in regions in a particular country
Identify appropriate partners to do business with
Creation of a Joint Ventures Database from multiple sources

13. Region Selection Application
Given information on a company and the desired form of internationalisation (e.g., export, direct investment, alliance) the application provides a ranking of regions which indicate the most suitable places for the type of business
A number of social, political geographical and economic indicators or variables such as the surface, labour costs, tax rates, population, literacy rates, etc. of regions have to be collected to feed an statistical model

14. Region Selection Application
Data sources used for the OBIE application are statistics from governmental sources and available region profiles found on the Web (e.g. Wikipedia)
Gazetteer lists contain location names and associated information together with keywords to help identify the key information
Grammars use contextual information and named entities to identify the target variables
“unemployment rate of 25% (2001)”
Extraction performance obtained: F-score > 80%

15. Region Selection Application: example
Tamil Nadu
...
Population (2001) 62,405,679 (6)
Density 478/km
<rdf>
<indicator:Measurement rdf:ID="Measurement_91567">
<indicator:hasValue>478</indicator:hasValue>
<indicator:hasPoliticalRegion
rdf:resource=".../int/region#TamilNadu" />
<indicator:hasIndicator
rdf:resource=".../int/indicator#DENS" />
<time:hasTimeSlice xmlns:time=".../general/time#">
<time:TimeSlice rdf:ID="TimeSlice_40715">
<time:hasTemporalEntity>
<time:ProperInstantYear rdf:ID="ProperInstantYear_57895">
<time:year rdf:datatype="#int">2001</time:year>
</time:ProperInstantYear>
</time:hasTemporalEntity>
</time:TimeSlice>
</time:hasTimeSlice>
</indicator:Measurement>
</rdf>

16. Conclusions and Future Work
MUSING integrates ontology-based extraction as a useful tool for Business Intelligence
The NLP applications analyse documents and populate a knowledge base
A number of practical applications have been defined which will use the KB’s stored facts. Extraction technology and performance so far is promising
Our future work will concentrate on
the full problem of ontology population including a cross-source coreference mechanism
the identification of qualitative information (such as opinions) e.g. to model company reputation
moving from a rule-based system to a machine learning approach