concept based semantic search

© Semantic Web Company – http://www.semantic-web.at/ 1

Concept-based, semantic search

Andreas BlumauerSemantic Web Companywww.semantic-web.at

http://www.semantic-web.at/


1. What means „concept-based“?2. Concept-tagging3. Semantic search

• Faceted search• Similarity search

4. Semantics as a means for ‚interpretation‘

5. Topic pages6. Three levels of semantic search

Content/agenda



What is a concept?The semiotic triangle

concept

objectlabel

A-Class

A-Klasse

W 176

Mental model of „A-Class“

anotherobject

Another mental model of „A-Class“



4

A-Klasse

W 176

http://voc.org.com/core/54

compact car

Each concept has a unique URI and can have various multi-lingual labels. Additionaly, it can have various types of semantic relations with other concepts. W3C´s SKOS standard describes a pre-defined set of semantic relations especially for controlled vocabularies.

prefLabel (de)

prefLabel

bro

ader

Daimler AGprefLabel


prefLabelA 250 Sport

nar

row

er

Vehicle manufacturing

company

prefLabel

bro

ader

A-Class

altLabel

prefLabel (en)

related


AMGprefLabel



Daimler-Benz hiddenLabel


nar

row

er

related

Mercedes-AMG

altLabel

Concept-based enterprise vocabulary



Concept-tagging vs. Term-tagging

Enterprise vocabulary

--- ------ --- --- ---- ----- ----

--- ---- --- - --- --- ---- -----

------

Concept Tagging

Content from CMS

Term Tagging

‚Term-tags‘ become a ‚concept‘as part of the enterprise vocabulary

Concept-tagging is done on top of concepts which are already part of the enterprise vocabulary, thus contextualised and linked to other concepts.

Term-tagging means that tags are extracted from text (automatically via text mining) which are not part of the controlled vocabulary yet.

Term-tags can be inserted into the enterprise vocabulary. This extends and refines the vocabulary more and more.



Concept-tagging: pre-condition for semantic search

W 176

--- -- ----- -- ------ ---- ---

------ --- ------ --- --

A 250 Sport ---- ----- ---- ----

---- ---

search

A-Class

W 176

prefLabel


nar

row

er

altLabel



Traditional search methods vs. semantic search

W 176

--- -- -- --- ------ ---

------ --- ---- --- ----------A

250 Sport ---- ----- ---- ----

---- ---

search

A-Class

W 176

prefLabel


nar

row

er

altLabel

--- -- ----- -- ------ ---- ---

------ --- ------ --- --- ----A 250 Sport

---- ----- ---- ---- ---- ---

Traditional:Can the search phrasebe found literallyin the document?

Semantic:Can the search phrasebe found analogously?



Semantics as a means for interpretation

Semantics helps to make different language levels or various perspectives comparable.

Example: Vendors and their customers quite often talk different languages. Wrong or sometimes time-consuming ‚translations‘ and interpretations have to be done by the customers themselves.

Example: The state of knowledge of employees can be quite divergent. Semantics as a search assistant can serve especially less experienced colleagues.

W 176

----- --- ------ --- ---- ---- --- --- -A 250 Sport ---- ----- ---- ----

---- ---

search

A-Class

W 176

prefLabel


nar

row

er

altLabel



9

Concept-/thesaurus-based facet classification of documents is as precise as the classification scheme used by the enterprise thesaurus itself. In consideration of all different labels of concepts and their transitive hierarchical relations, a more precise facet classification can be realised than with traditional term-based methods.

---- --- -- -- Daimler-Benz ------ --- ------ ----

----- ---- ---- ---- ------ --

----- ------ --- ------ -- ------- ----

----- ---- ---- ---- AMG --

---- --- --------

Vehicle manufacturer (2)

Daimler AG (2)

COMPANY

#2

#1Synonyms and hidden labels:#1 is also classified as ‚Daimler AG‘ because ‚Daimler-Benz‘ is also (an old) name for ‚Daimler AG‘.

Transitivity:#2 is categorized as ‚vehicle manufacturer‘ too, because in our thesaurus ‚AMG‘ is narrower (is part of) of ‚Daimler‘ which is a ‚vehicle manufacturer‘.

AMG (1)


Concept-based high-precision facet classification


10

Content-authors as well as end-users can benefit from similarity search (content recommendation), e.g. by ‚skim reading‘ or by the avoidance of duplicated work. Even if two documents have no words in common they can be classified as similar when using a concept-based text analysis.

--- ---- ----- -- ------ -- --- ------

-- Mercedes-AMG

-------- --- -------- -- W 176 ---- ----- ---- ---- ---- ---

--- -- AMG

--- ------ --- ------ -- ---- -- ---- ----- --- --A 250

Sport ---- ----- ---- ---- ----

--- ------ -------- ----


AMGprefLabel

Mercedes-AMG

altLabel

A-Class

W 176


A 250 Sport

nar

row

er



Similarity search: efficient re-use of existing information


Topic Pages: Mashups for a fast 360O view

11

Short description

Relatedconcepts

Geo search

Artic

les (tw

itter, v

ideos e

tc.) c

an

be re

trieved

fro

m v

ario

us c

on

ten

t sou

rces

API

http://

CMS



Linked Data: complex queries on top of standard technologies

12

IndustryNews

Example: Find industry news which mention countries or regions, in which our export volume increased by more than 10% over the last 5 years an which mention either one of our products and/or a competitor.

(Federated) SPARQL Queries

Export statistics



Conclusio 1: The three levels of semantic search

Concept-based search

Term-based search

Linked Data based search

Semantics is calculated by text analysis. Example: Because „Dieter Zetsche“ frequently occurs together with „Daimler AG“ in a text the algorithm assumes that those two phrases relate somehow to each other. Term-based methods are less precise than the two from further above.

Semantics is explicitly available by using controlled vocabularies and thesauri. Thesauri are the basis for precise text analysis and to build a semantic index. Building knowledge models is especially cost-efficient for larger organisations since a more precise search can be provided.

Semantics is explicitly available via linked knowledge models. Content from various sources and deparments can be linked and mashed on top of an explicit meta data layer. Complex queries which use data from many sources can be made by using the standard query language SPARQL.

2011

2005

2014Year in which theunderlying technology will be/has been rolled out.

No Standards



Conclusio 2: Explicit metadata layer

Metadata: • Stored and processed separately from data• Metadata management is part of the enterprise information management

strategy

HRMarketing/Sales

Research ProductionData Data

Data Data



Semantic Web Company GmbHMariahilfer Strasse 70/81070 ViennaAustria

http://www.semantic-web.at/ http://poolparty.biz

http://twitter.com/semwebcompany

Andreas BlumauerManaging [email protected]

15© Semantic Web Company – http://www.semantic-web.at/

“Thank you for your time and please forward any comments or questions to me to get more information on our product or linked data & vocabularies!”

http://www.semantic-web.at/

http://poolparty.biz/



concept based semantic search

Documents

amg http

sport http

cms semantic web company

comcore355 http

comcore97 http

search phrasew

search assistant

thesaurus amg