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WP5.4 - Introduction
 Knowledge Extraction from Complementary Sources
 This activity is concerned with augmenting the semantic multimedia
metadata basis by analysis of complementary textual, speech and
semi-structured data
 Focus in first 12 months
 Joint work between DFKI, UEP and DCU on aligning event
extraction from textual football match reports with event
recognition in video coverage of the same match
 Focus in following 12 months
 Joint work between DFKI, UEP and DCU on the extension of
the event alignment work towards cross-media feature
extraction (aligning low-level image/video features with events
extracted in aligned textual and semi-structured data)
 Joint work between DFKI, UEP, TUB and GET (cross-WP
cooperation with WP3.3) on analyzing textual metadata in
primary sources (OCR applied to text detected in images).
Text-Video Mapping in the Football Domain
Alignment of extracted events from unstructured textual data and from events
that are provided by the semi-structured tabular data in the SmartWeb corpus
(DFKI) with events that were detected by the video analysis results (DCU).
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Cooperation: DFKI, UEP, DCU
Resources:
 DFKI: SmartWeb Data Set (textual and tabular match reports)
 DFKI/UEP: Additional minute-by-minute textual match reports (‚tickers‘)
from other web resources
 DCU: Video Detectors (Crowd image detector, Speech-Band Audio
Activity, On-Screen Graphics Tracking, Motion activity measure, Field Line
orientation, Close-up)
Textual and semi-structured data (tabular, XML files) are exploited as
background knowledge in filtering the video analysis results and will possibly
help in further improving the corresponding video analysis algorithms
Resources
 The SmartWeb Data Set as provided by DFKI is an experimental data
set for ontology-based information extraction and ontology learning
from text that has been compiled for the SmartWeb project.
 The data set consists of:
 An ontology on football (soccer) that is integrated with foundational
(DOLCE), general (SUMO) and task-specific (discourse, navigation)
ontologies.
 A corpus of semi-structured and textual match reports (German and
English documents) that are derived from freely available web sources.
The bilingual documents are not translations, but are aligned on the level of
a particular match (i.e. they are about the same match).
 A knowledge base of events and entities in the world cup domain that have
been automatically extracted from the German documents.
 For the purposes of the experiment described here we were mostly
interested in the events that are described by the semi-structured data.
SmartWeb Data Example
DCU: Video Analysis Data
 Framework for event detection in broadcast video of
multiple different field sports as provided by DCU
 Video detectors used by DCU
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Crowd image detector
Speech-Band Audio Activity
On-Screen Graphics Tracking
Motion activity measure
Field Line orientation
Close-up
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DFKI/UEP: Extraction of Tickers
Minute-by-minute reports from different Web resources
Ligalive.de
•
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Information Extraction from Text
Information Extraction
with DFKI Tool „SProUT“
Shallow Processing with
Unification and Typed
Feature Structures
(SProUT) tool for
multilingual shallow text
processing and information
extraction
SProUT java web service that
takes the minute-by-minute
reports as an input, parses
them and extracts a new
XML file for each minute of
a particular match
Aligning and Aggregation of Textual Events
Events alignment from various tickers
Information Extraction
Results (SProUT)
alignment
Data aggregation for later use
Example: minute 40
Minute-by-minute reports
Tabular Reports
VIDEO – TEXTUAL DATA
TIME ALIGNMENT
+ video event detection
data (features) from DCU
CROSS-MEDIA FEATURE EXTRACTION
Match vs Video Time
Freekick evaluation
Possible OCR on video
Time differences tracking
Cross-media Features
 Purpose: Cross-Media features describe information that occurs in
textual/semi-structured data as well as in video data and can therefore
be used as additional support in video analysis.
 Goal: Use video detectors aligned with events extracted from text/semistructured data as cross—media features
 Example:
Summary
 Extracted: 1200 events, 45 event-types
 After alignment: 850 events describing
five matches from World Cup 2006 Final
 170 events per game on average
 Cross-media descriptors for every
event-type
Future plans
 In WP5.4.1 continue work on mapping between results of
video analysis and complementary resource analysis in the
following way:
 Use extracted image descriptors from training data (video + aligned
text extraction) for the classification of fine-grained events in test
data (i.e. other videos) -- all based on minute-by-minute alignment
 Cooperate with TUB in Video OCR to help time video-text
alignment
 WP5.4.2 Images and text as mutually complementary
resources
 WP5.4.3: Image retrieval based on enhanced query
processing and complementary resource analysis
Mining over Football Match Data: Seeking
Associations among Explicit and Implicit Events
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Apart from identifying individual events, it might be useful to find out about
general statistical dependencies (associations) among types of events
Initial experiments carried out on a single type of resource – structured data
In the future, events extracted from text and video could be considered as well
Use of LISp-Miner tool (UEP)
 Data mining procedure 4ft-Miner mines for various types of association rules and
conditional association rules
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Potential application: Discovering new relationships to be inserted into the
domain ontology or knowledge base,
Joint Work Example