=Paper=
{{Paper
|id=None
|storemode=property
|title=Annotation component for a Semantic Wiki
|pdfUrl=https://ceur-ws.org/Vol-632/paper07.pdf
|volume=Vol-632
|dblpUrl=https://dblp.org/rec/conf/semwiki/SchmidtS10
}}
==Annotation component for a Semantic Wiki ==
https://ceur-ws.org/Vol-632/paper07.pdf
Annotation Component in KiWi
Marek Schmidt and Pavel Smrž
Faculty of Information Technology
Brno University of Technology
Božetěchova 2, 612 66 Brno, Czech Republic
E-mail: {ischmidt,smrz}@fit.vutbr.cz
Abstract. This paper deals with key functionalities of the KiWi an-
notation component and shows how it enables seamless combination of
informal and formal knowledge and transformation of the former to the
latter. It demonstrates how the advanced KiWi features, such as nested
content items, reasoning and information extraction, can be used to-
gether to make rich semantic annotation easy and useful.
1 Introduction
The original wiki systems employ specific wiki languages to edit content. Such
languages can easily be extended to allow semantic annotations, which is the ap-
proach taken by various semantic wiki systems, such as Semantic MediaWiki [2].
Other approaches to semantic data editing, as, e.g., in OntoWiki [1], provide a
rich interface to edit RDF. However, these annotations are not integrated into
wiki text content. In KiWi [4], we combine semantic annotations directly with
the text content of the wiki pages and provide advanced user interfaces support-
ing the annotation process with the help of suggestions coming from information
extraction.
2 Knowledge Representation in KiWi
KiWi data model is designed to integrate both formal and informal knowledge [4].
A core entity is a content item which may contain an XHTML text content or any
other kind of multimedia. A title and a list of tags are associated with content
items. Each content item corresponds to exactly one resource, which enables
adding arbitrary RDF statements about the content item. The 1:1 relationship
between a content item and a resource reflects a usual practice in semantic wikis.
However, having only this linking mechanism is limiting as it is then not possible
to represent formal statements about other entities than the current page. Some
wikis, such as Semperwiki [3], allow defining an about entity, independent of the
page, to describe other entities than the current page.
The KiWi nested content items and fragments allow for a more granular
and more natural annotation. Fragments enable annotating arbitrary segments
of text with arbitrary tags, comments and RDF metadata, which is akin to
2 Marek Schmidt and Pavel Smrž
annotating a paper with a marker, enhanced with semantics. Nested content
items are used for annotating whole sections of text with arbitrary metadata.
While no explicit about resource as in Semperwiki is supported in KiWi itself,
such behaviour can be implemented in KiWi using the native KiWi reasoning
support by creating rules. It is thus possible to define an ‘about’ rule, such that
nested item would act as a proxy for a different resource, and any RDF triple
assigned to the nested item could automatically be inferred on the referenced
resource.
3 User Interface for Information Extraction
The information extraction service in the KiWi system uses natural language
processing and machine learning algorithms to provide suggestions for annota-
tions [5]. There are two ways users can interact with the information extraction
services in KiWi.
3.1 ASIDE – Annotate Single Document Efficiently
Users can create and edit all kinds of annotations supported by the KiWi sys-
tem mentioned in the previous section. The information extraction component
supports the user by displaying suggestions.
Suggestions can be applied at various stages of the annotation process. Some
suggestions can be shown directly in the text, so that the user can select the
piece of text just by clicking on the suggestion.
When the user makes a selection of the piece of the text, all the suggestions
relevant to that piece of text are displayed. This may include more suggestions
than the previous step, as an additional information extraction step is taken at
this time which employs apriori information on selecting the particular piece of
text.
To support emerging knowledge, it is also crucial to support partially spec-
ified annotations (such as a link to an entity of which only type is known, but
no entity to link to exists yet), or annotations that conflict with the current
ontology (such as an object predicate linking to an entity of a wrong type). The
user interface shows the partial annotations in yellow and erroneous annotations
in red.
Some suggestions can be ambiguous, such as a suggestion for a link to a user
page based on the user names. The annotation can be directly created from these
kinds of suggestions, but it will be marked as partially specified, so the user sees
that additional action is necessary to make this annotation into a ‘green’ correct
one.
The suggestions can also be displayed in a list sorted by type. The suggestion
list includes properties which are defined for the current content item type, but
for which no suggestions have been found in the document. A user can thus see
if there are some of the required annotations missing. Then, she can annotate
just by dragging a selected piece of text and dropping it to a particular type box
Annotation Component in KiWi 3
to create an annotation of this type. List of types for the current content item
is generated from the underlying ontology.
3.2 AMUSE – Annotate Multiple Documents Simultaneously (and
Efficiently)
Especially when dealing with a new task, it is often the case that one needs to
semantically enrich many documents of the same type, e.g., a bunch of minutes
from a series of previous meetings. The use of the ASIDE tool introduced in the
previous subsection on each individual wiki page would mean a tedious work. In
these situations, it is preferable to focus on a specific type of annotations and
process all the documents in one run.
AMUSE is a kind of discovery tool intended to identify all instances of the
given type in all the documents available. This tool is also used to configure the
information extraction services and to ‘tune’ it with respect to the particular
type of annotation being extracted.
Machine learning algorithms are employed to classify potential instances.
AMUSE takes advantage of existing annotations found in the initial training
data and retrains the classifiers on the user feedback (accepting or rejecting
suggestions).
The behaviour of the tool depends on the type of entity it is used on:
– Types. Identify all the pages of the given type, based on document classifi-
cation. In addition to document features, contextual features derived from
the links to pages of the given type are used for classification.
– Tags. Same as for types, but additionally also discover all the text fragments
that should have this tag.
– Datatype properties, such as ‘foaf:birthday’. Classify all the fragments of the
particular type. A specific extractor can be assigned to each of these kinds of
extractions (such as a date extractor for recognizing date information from
text, money extractor to recognize amounts of money in a listed currencies,
etc.)
– Object properties, such as ‘foaf:currentProject’. Discovers links to entities
and their roles. It works in combination with the type classifier to recognize
roles of the potential links.
– Other entities. Discover links to this entity from other pages. This may in-
volve disambiguating titles shared by several pages.
After the initialization of this tool for a specific entity (type, tag or property),
AMUSE displays a ranked list of suggestions coming from various content items.
Users can immediately accept or reject the suggestions, thus annotating the
content items and improving the system by providing the training examples at
the same time.
4 Marek Schmidt and Pavel Smrž
Fig. 1. The annotation tool, annotating a meeting minutes document. Currently edit-
ing a link to an entity named ‘John Doe’.
4 Use-case Scenario
The scenario discussed in this subsection corresponds to an enterprise setting. A
semantic wiki is used to facilitate the knowledge formalisation process in project
management tasks. Various kinds of information need to be formally represented
in the knowledge base, such as information about projects, customers, people,
resources, meetings and tasks. This data can then appear in simple queries (‘who
attended the meetings where project Foo was discussed’), better task manage-
ment (tasks can be formally defined directly in the meeting minutes document
and automatically appear in the responsible person’s ‘todo’ lists and calendars).
This scenario assumes that an ontology describing the entities and their relations
already exists in the system.
As demonstrated by Figure 1, meeting minutes are produced in the KiWi
system. The annotation tool is opened. The system immediately offers suggestion
regarding the type of the document. Selecting the proper type leads to more
relevant suggestions. The information extraction component recognizes some of
the names of people present and correctly offers the role ‘participants’. One of
the names could not be identified, because this user was not mentioned yet in the
system. It is still recognized that the string corresponds probably to a name of a
person, though, so a suggestion to create a new entity of the type ‘foaf:Person’ is
Annotation Component in KiWi 5
displayed. Accepting the suggestion creates a new entity in the knowledge base.
It will be recognized in all further documents.
Some of the other recognized entities are irrelevant for the current context
(such as matching general terms in the ontology), so the user rejects these sugges-
tions. The provided feedback instructs the system not to offer these suggestions
in similar contexts in future steps. The user accepts one other suggestion trig-
gered by a label of one of the projects. The meeting is now formally associated
with the project.
The user can create another annotation, such as selecting a piece of text
around an action item and clicking the Nested Content Item button and selecting
the type ActionItem. The ActionItem class specifies several properties, such
as deadline and responsible persons. The user can fill the responsible person
property just by dragging-and-dropping one of the person annotations created
earlier. The task will automatically appear in the task list of the responsible
person after the automatic application of the appropriate reasoning rule.
5 Conclusions and Future Directions
The annotation component introduced in this paper has become an integral part
of the KiWi system. It enables formal semantic annotation of any kind of existing
content. Information extraction supports the annotation by providing context-
dependent suggestions which are naturally integrated into the user interface.
The discussed use-case scenario shows advantages of the implemented tools in
realistic conditions.
Our future work will focus on merging the annotation tool and the KiWi
editor and on displaying the suggestions at real time while editing the content.
We will also continue to collect real use data to quantify the actual improvement
in the annotation process given by the suggestions.
Acknowledgement The research has received funding from the EC’s Seventh
Framework Programme (FP7/2007-2013) under grant agreement No. 211932.
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