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Creating Knowledge out of Interlinked Data

LOD2 Presentation . 02.09.2010 . Page http://lod2.euAKSW, Universität Leipzig

Norman Heino

Browsing and Editing RDF Knowledge bases with OntoWiki and RDFauthor

OntoWiki


KAIST LOD2 Workshop • 2011-08-16 • Daejeon • Page http://lod2.eu

Schedule

Semantic Wikis

OntoWiki

Semantics Aware Editing with RDFauthor

Use Cases

2



Semantic Wikis

3



Wikiwiki Concepts

Everyone can edit anything

Content is edited in the same way as structure is

Activity can be watched and reviewed by everyone

Ward Cunningham

4



Semantic Wikis

Two approaches:

• Text-based wiki w/ semantic layer (e.g. Semantic MediaWiki)

• Form-based RDF data wiki (e.g. OntoWiki)

5



Semantic MediaWiki

Semantic

store

MediaWiki DB

(MySQL)

Storage

Abstraction

Storage

Implementation

Parsing RenderingInline

QueriesSetup

Lan-

guage

Java-

Scripts

+

CSS

OWL

Export...

Datatype API

Data processing

Type:String

Type:Date

Type:Number

...

Page display and

manipulation

Special

pages

DB interface

MediaWiki

Webserver (Apache)

Semantic

MediaWiki

Setu

pLanguage

syste

m

Fig. 1. Architecture of SMW’s main components in relation to MediaWiki.

Semantic MediaWiki (SMW) is a semantically enhanced wiki engine that enablesusers to annotate the wiki’s contents with explicit, machine-readable information.Using this semantic data, SMW addresses core problems of today’s wikis:

• Consistency of content: The same information often occurs on many pages. Howcan one ensure that information in di↵erent parts of the system is consistent,especially as it can be changed in a distributed way?• Accessing knowledge: Large wikis have thousands of pages. Finding and com-

paring information from di↵erent pages is challenging and time-consuming.• Reusing knowledge: Many wikis are driven by the wish to make information

accessible to many people. But the rigid, text-based content of classical wikiscan only be used by reading pages in a browser or similar application.

SMW is free software, available as an extension of the popular wiki engine Media-Wiki. Figure 1 provides an overview of SMW’s core components which we willdiscuss in more detail throughout this paper. The integration between MediaWikiand SMW is based on MediaWiki’s extension mechanism: SMW registers for cer-tain events or requests, and MediaWiki calls SMW functions when needed. SMWthus does not overwrite any part of MediaWiki, and can be added to existing wikiswithout much migration cost. Usage information about SMW, installation instruc-tions, and the complete documentation are found at SMW’s homepage. 1

Next, Section 2 explains how structural information is collected in SMW, and howthis data relates to the OWL DL ontology language. Section 3 surveys SMW’s mainfeatures for wiki users: semantic browsing, semantic queries, and data exchangeon the Semantic Web. Queries are the most powerful way of retrieving data fromSMW, and their syntax and semantics is presented in detail. The practical use ofSMW is the topic of Section 4, where we consider existing usage patterns in (non-semantic) Wikipedia, usage statistics from a medium-sized SMW site, and typicalcurrent uses of SMW. Section 5 focusses on performance, first by discussing mea-

1 http://ontoworld.org/wiki/SMW

2

• Semantic extension to MediaWiki

• Page-centric

• Pre-configured properties

• Queryable semantic overlay graph

6



OntoWiki

6 Heino, Dietzold, Martin, Auer

2.1 Architecture Overview

As depicted in figure 2, the OntoWiki Application Framework consists ofthree separate layers. The persistence layer consists of the Erfurt API whichprovides an interface to di↵erent RDF stores. In addition to the Erfurt API,the application layer is built by a) the underlying Zend Framework1 and b) anAPI for OntoWiki extension development. With the exception of templates,the user interface layer is primarily active on the client side, providing theCSS framework, a JavaScript UI API, RDFa widgets and HTML templatesgenerated on the Web-server side.

Application Layer

OntoWiki API Zend Framework

Persistence Layer (Erfurt API)

RDF Store

Sto

re A

da

pte

r

Authentication, ACL, Versioning, …

User Interface Layer

CSS Framework

OntoWiki UI API

RDFa Widgets

Templates

Fig. 2 The OntoWiki Application Framework with its three layers: persistence layer,application layer, user interface layer

2.2 Persistence Layer

Persistent data storage as well as associated functionality such as versioningand access control are provided by the Erfurt API. This API consists of thecomponents described in the subsequent paragraphs.

1http://framework.zend.com/

http://erfurt-framework.org/

• RDF data wiki

• Resource centric

• Generic and custom views

• Façet and set-based browsing

• Collaborative authoring

• based on Erfurt framework

7



OntoWiki

8



Architecture

Application Layer

OntoWiki API Zend Framework

User Interface Layer

CSS Framework

OntoWiki UI API

RDFa Widgets

Templates

9

Persistence Layer (Erfurt API)

RDF Store

Sto

re A

da

pte

r

Authentication, ACL, Versioning, …

http://erfurt-framework.org/



Vision

Generic data wiki for RDF models

• no data model mismatch (structured vs. unstructured)

Application framework for:

• Knowledge-intensive applications

• Distributed user groups

10



Interfaces

SPARQL Endpoint

Linked Data Endpoint

WebDAV

REST API

Command Line Interface

LDAP

11



Extensibility

Plugins

Views/Templates

Themes

Localizations

12



Access Control

Model (graph) based

• partitioning via owl:imports

Action based (predefined)

• register new user

• reset password

13



Other Features

Facet-based browsing

Inline editing

Auto-adaptive user interface

Resource auto-suggestion

SPARQL Query Editor

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COMPSAC 2011 • 2011-07-19 • Munich • Page http://lod2.eu15



RDFauthor

19



RDFa

Images: http://www.w3.org/TR/xhtml-rdfa-primer/

Annotating XML documents with RDF

Human and machine-readable

MVC – declare view in model language

20



Knowledge Engineering with RDFa

XHTML+

RDFaWeb

Server+

RDF Store

Edit

SPARQL/Update

HTTP

RDFa page, updatable knowledge store

"Intelligent" editing components (widgets)

Supporting the user

21



Implementation

HTML Form• Widget selection/form creation

RDF Store

• Update propagation

Extracted Triples

• Client-side page processing XHTML

+RDFa

• Page creation

22



Use Case I

23



Web content and Linked Data

Same content

• readable by humans

• processable by machines

Strategy: OntoWiki as backend, custom frontent

24



OntoWiki Site Extension

Erfurt Framework

RDFa

RDFauthor Zend Framework(Zend_View)

Linked Data

Virtuoso RDF Store

BibSonomy

RSS/Atom

Blog Posts Twitter

Site Vocabulary(foaf, doap)

Instance Data

Taxonomy(skos)

expressed in expressed in

consumes

uses

exposes

imports

syndicates

updates

exposed as

exposed as

uses

is built upon

SPARQL Software component

Exchange format/practice

External service

Represented knowledge

Figure 1. OntoWiki-CMS overall architecture and building blocks.

narios. It makes no assumptions on the data model and canthus be used with any RDF knowledge base. For managingWeb content, we developed a core ontology (see Section IV),a skos-based taxonomy for navigation and populated bothwith instance data representing the Web site content andmetadata. OntoWiki provides a configureable navigationhierarchy that can be used to display skos hierarchies. Inaddition, it has search capabilities and leverages resourceinterlinks in order to provide different paths for browsingknowledge bases. Being a wiki, it also fosters versioningof changes to a resource, discussion and editing, which aredescribed below.

The OntoWiki backend automatically creates pages anno-tated with RDFa. For editing content, these builtin semanticannotations are leveraged to automatically create an editingform. The system in use here has been made availableseparately as RDFauthor [9]. To this end, it incorporatesseveral technologies:

• semantics-aware widgets that support the user whileediting content by automatically suggesting resourcesto link to based on queries to the local knowledge storeand Sindice2.

• Updates are sent back the the RDF store via SPAR-QL/Update – an update extension to the current speci-fication currently in standardization.Extensibility: OntoWiki started as an RDF-based data

wiki with emphasis on collaboration but has meanwhileevolved into a comprehensive framework for developingSemantic Web applications [1]. This involved not only thedevelopment of a sophisticated extension interface allowingfor a wide range of customizations but also the additionof several access and consumption interfaces allowing On-

2http://sindice.com/

toWiki installations to play both a provider and a consumerrole in the emerging Web of Data.

Evolution: The loosely typed data model of RDF en-courages continuous evolution and refinement of knowledgebases. With EvoPat, OntoWiki supports this in a declarative,pattern-based manner [10]. Such basic evolution patternsconsist of three components: a set of variables, a SPARQLselect query selecting a number of resources under evolution,and a SPARQL/Update query template that is executed foreach resulting resource of the select query. In addition, basicpatterns can be combined to form compound patterns –suitable for more complex evolution scenarios.

C. Access Interfaces

In addition to human-targeted graphical user interfaces,OntoWiki supports a number of machine-accessible datainterfaces. These are based on established Semantic Webstandards like SPARQL or accepted best practices likepublication and consumption of Linked Data.

Worth mentioning are a SPARQL Endpoint, allowing allresources managed in an OntoWiki be queried over the Web,a Linked Data interface for publishing resources accordingto accepted publication principles3, as well as SemanticPingback [11] which adapts the pingpack idea to LinkedData providing a notification mechanism for resource usage.

D. Exploration Interfaces

For exploring semantic content, OntoWiki provides sev-eral exploration interfaces that are appropriate for a widerange of use cases. For instance, it offers generic views,domain-specific browsing interfaces, facet-based browsingand graphical query builders in addition to full-text and

3http://sites.wiwiss.fu-berlin.de/suhl/bizer/pub/LinkedDataTutorial/

Architecture of the approach

25

Load and render

template

Load CBD, interpret

properties



Linked Data request

Forwardapplication/rdf+xml, text/turtle, …

Export RDF

http://lod2.eu/Welcome.rdf

Forward and rewrite

internal links

text/html

http://lod2.eu/Welcome.htmlAccept?

404

yes

no

∃ URI?Request

http://lod2.eu/Welcome

26



Use Case II

27



Orchestra-tion

Service

Virtuoso FOX

CMS

Wrapper

push (content) annotations (RDF) – async

text

annotations

OntoWiki injection

crawlednews

optional

Extraction and Storage Layer

Wrapper Layer

Orchestration and Curation Layer

push (curation changes)

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a scms:Request

a sioc:Item

xsd:string

xsd:string

xsd:string

scms:document

dc:title

dc:description

content:encoded

scms:annotate

scms:annotate

a rdf:Resource

scms:callbackEndpoint

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Text

broadly used vocabularies Annotea20 and Autotag 21. In particular, we addedthe following constructs:

– scms:beginIndex denotes the index in a literal value string at which a par-ticular annotation or keyphrase begins;

– scms:endIndex stands for the index in a literal value string at which aparticular annotation or keyphrase ends;

– scms:means marks the URI assigned to a named entity identified for anannotation;

– scms:tool provides the URI of tool that found the annotation and– scmsann is the namespace for the annotation classes, i.e, location, person,

organization and miscellaneous.

Thanks to the multi-core architecture of current servers, FOX is almost asfast as the slowest tool in its pipeline and thus as time-e⇥cient as state-of-the-arttools, given that the overhead due to the merging of the results via the neuralnetwork is of only a few milliseconds. Still, as our evaluation shows, these fewmilliseconds overhead can lead to an increase of more than 13% F-Score (seeSection 6). The output of FOX for our example is shown in Listing 3. This isthe output that is forwarded to the orchestration service, which adds provenanceinformation to the RDF before sending an answer to the callback URI providedby the wrapper. By these means, we ensure that the wrapper can write the RDFain the write segment of the item content.

a ann:Annot

ation

a rdf:Resour

ce

xsd:string

scms:means

ann:body

xsd:integer

xsd:integer

scms:beginIndex

scms:endIndex

a rdf:Resour

cescms:tool

(a) named entity annotation

a ctag:AutoT

ag

a rdf:Resour

ce

ctag:means

xsd:string

ctag:label

a rdf:Resour

cescms:tool

anyProp

(b) keyword annotation

Fig. 5. Vocabularies used by FOX for representing named entities (a) and keywords(b)

20 http://www.w3.org/2000/10/annotation-ns#21 http://commontag.org/ns#

30


KAIST LOD2 Workshop • 2011-08-16 • Daejeon • Page http://lod2.eu31



resulting reference data contained 20 location, 78 organization and 11 persontokens. Note that both data sets are of very di�erent nature as the first containsa large number of organizations and a relatively small number of locations whilethe second consists mainly of locations. In both cases, the annotation was carriedout independently from the automatic extraction of named entities.

The results of our evaluation are shown in Table 1. CS follows a very con-servative strategy, which leads to it having very high precision scores of up to100% in some experiments. Yet, its conservative strategy leads to a recall whichis mostly significantly inferior to that of SCMS. The only category within whichCS outperforms SCMS is the detection of persons in the actors profile data.This is due to it detecting 6 out of the 11 person tokens in the data set, whileSCMS only detected 5. In all other cases, SCMS outperforms CS by up to 13%F-score (detection of organizations in the country profiles data set). Overall,SCMS outperform CS by 7% F-score on country profiles and almost 8% F-scoreon actors.

Country Profiles Actors Profiles

Entity Type Measure FOX CS FOX CS

Location Precision 98% 100% 83.33% 100%Recall 94.23% 78.85% 90% 70%F-Score 96.08% 88.17% 86.54% 82.35%

Organization Precision 73.33% 100% 57.14% 90.91%Recall 68.75% 40% 69.23% 47.44%F-Score 70.97% 57.14% 62.72% 62.35%

Person Precision – – 100% 100%Recall – – 45.45% 54.55%F-Score – – 62.5% 70.59%

Overall Precision 93.97% 100% 85.16% 98.2%Recall 91.60% 74.79% 70.64% 52.29%F-Score 92.77% 85.58% 77.22% 68.24%

Table 1. Evaluation results on country and actors profiles. The superior F-score foreach category is in bold font.

7 Conclusion

In this paper, we presented the SCMS framework for extracting structured datafrom CMS content. We presented the architecture of our approach and explainedhow each of its components work. In addition, we explained the vocabulariesutilized by the components of our framework. The flexibility of our approach isensured by the combination of RDF messages that can be easily extended and

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