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Advanced use RDFS

Mariano Rodriguez-Muro,
Free University of Bozen-Bolzano
+

Disclaimer


License




A few examples from these slides has been taken from




This work is licensed under a
Creative Commons Attribution-Share Alike 3.0 License
(http://creativecommons.org/licenses/by-sa/3.0/)

Semantic Web for the working Ontologist. Chapter 6.

Some of the slides on the use of taxonomies are based on:


http://info.earley.com/webinar-replay-business-value-taxonomyaug-2012
+

Reading material


Semantic Web for the working Ontologist. Chapter 6
http://proquest.safaribooksonline.com/book/-/9780123859655
+

Uses of RDFS (and ontology)


Application oriented uses






Application behavior without coding
Data integration through vocabulary alignment, integration
Controlled vocabularies

Formal ontology:


Definition of taxonomies, e.g., parent/broader, child/narrower,
etc.

Taxonomy/Ontology can be used to create business/data
value
Taxonomy can open the door for new kinds of data
management
+
Enterprise
+

Content Management


Increase the control/productivity that the enterprise has over
their data to increase internal productivity, customer
satisfaction, etc.



Why not “just Google” your sites? These do not work in the
enterprise





Back links and
Statistics

In the enterprise, granularity is small
+

Search enhancement


Search enhancement





Examples:






Finding content (DB., entries, document collections, etc) relevant
to a query, but tagged with an alternative name
Key is search by metadata and organized metadata

Add synonyms to a query
Language/translation
Include more general terms

Precision vs Recall. The focus here is recall, get all “relevant”
content.
+ Browsing and Navigation: Search overload

User doesn’t know what he
wants precisely
+ Browsing and Navigation: Search overload

Facets

Give control to the user
+ Browsing and Navigation: Search overload

Note: Taxonomy is not navigation
+

Browsing and navigation, results


Faceted navigation in e-commerce:


Findability



Conversions



Sales



Market size



Customer satisfaction



etc.



Studies show that faceted navigation in enterprise content
easily increases all these aspects in hard benchmarks.



See presentation by Earley & Associates
+

Content Reuse – Taxonomy in
Content Management


Many use cases



Examples, knowledge management, content finding, etc.





Look at business processes, group at targeted users
Useful when knowledge is large, and it needs to be accessible fast

A Taxonomy can be used to


Define content and document types (e.g., “Article”)



Define the fields that will describe attributes (e.g., tag a
document with “Industry”)



Define the actual values of certain fields (e.g., the list of values for
the attribute “Industry” might include “Construction”,
“Information Technology”, “Utilities”, etc.)
+

Example: Knowledge management


Portal development


Service a functional organization, e.g., call centers, technical field
services



Key: Changing content



Requires: Access to the latest's and best value always

Call centers representatives required 50% less time to solve a problem
with correctly organized information.
Earley & Associates, 2012
Average reactive time per incident: 10.35hrs
Knowledge Helpful Average Reactive TPI: 5.45hrs
Knowledge Helpful Time Saved Per Incident: 43%
+

Content reuse: Improved
Management of Marketing Assets


Type: Magazine Ads



Channel: Print



Target Demographic: Parents



Country: US



Language: English



Concept: Rebellion



Brand: Settletra



Do your kids:
 Have discipline problems?
 Trouble paying attention?
 Trouble getting along?

Maybe It’s time to findout how
Settletra can help
+

Content reuse
+

Content reuse: result


Requirement




Question




Do we have material for this
campaign

No?




Images for campaigns

Produce new material

Use taxonomies to improve
search



$1.25M /yr through digital
asset management and
increased image reuse (Earley
& Associates)
+
Public Entities
+

The power of large, curated
taxonomies


Many large taxonomies developed in the context of large
national and international projects



Large amount of knowledge



Clean knowledge (manually curated)



General knowledge (cover domains rather than applications)



Reusable to provide valuable services
+

Taxonomies resources


Taxonomy resources:


http://www.taxonomywarehouse.com/



http://www.taxobank.org/



http://www.taxotips.com/resources/sources/



http://id.loc.gov/



http://id.loc.gov/authorities/subjects/sh85112348.html



http://bioportal.bioontology.org/



http://www.w3.org/2001/sw/wiki/SKOS/Datasets
Some of these are actually
ONTOLOGY repositories
+

Taxonomies in Biology


Taxonomies in Biology have been developed for a long time





Large investment world wide
Deployed in applications today

Include wide range of Biology subjects



Medical terminologies





Macro and Micro biology (Genes, Human Anatomy)
Etc.

Started as knowledge management/sharing, now
applications are being built.
•
•
•
•

Download
Traverse
Search
Comment

Mapping
Services

•
•
•

Create
Download
Upload

Widgets

•
•
•

Tree-view
Auto-complete
Graph-view

Ontology
Services

http://rest.bioontology.org

Views

Annotation

Term recognition

Data Access

Fetch “data”
annotated with a
given term

http://bioportal.bioontology.org
Annotation service
Process textual metadata to automatically tag
text with as many ontology terms as possible.

90 million calls,
~700 GB of data
Annotating Clinical Text
Resource index

Pubmed Abstracts
Adverse Events (AERS)
GEO
:
Clinical Trials
Drug Bank
+

Semantic Annotation services


Semantic Annotations services are very wide spread



Topics include:


General purpose (based on DBPedia URI’s for example)



Specialized


Music



Movies



Libraries



Biology



http://lmgtfy.com/?q=semantic+annotator



http://dbpedia-spotlight.github.com/demo/index.html
+
Summary
+

Summary: RDFS


Ontology languages,
Ontologies



RDFS language and inferences



Common use patterns of RDFS
inferences



Taxonomies


Their value and use in the
enterprise



Collections and applications

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