Marl Ontology Specification

Marl Ontology Specification

V0.2 - 13 March 2011

This version:	http://purl.org/marl/0.2/ns (RDF/XML, HTML)
Latest version:	http://purl.org/marl/ns
Previous version:	http://purl.org/marl/0.1/ns
Editors:	Adam Westerski
Authors:	Adam Westerski
Contributors:	See acknowledgements

This work is licensed under a Creative Commons Attribution License. This copyright applies to the Marl Ontology Specification and accompanying documentation in RDF. This ontology uses W3C's RDF technology, an open Web standard that can be freely used by anyone.

Abstract

Marl is a standardised data schema (also referred as "ontology" or "vocabulary") designed to annotate and describe subjective opinions expressed on the web or in particular Information Systems. The following document contains the description of ontology and instructions how to connect it with descriptions of other resources.

Introduction
Marl ontology at a glance
Marl ontology overview
1. Example
Cross-reference for Marl classes and properties

1 Introduction

The following specification is a formal description of metadata schema proposal that can be applied to data representing subjective opinions published on the Web. The goal of the following section is to provide the basic knowledge to comprehend the technical part of the specification. As such it shall introduce both Semantic Web and general topic of opinion representation and sentiment analysis.

An important note is that Marl ontology presented here is not a complete model to address the problem of describing and linking opinions online and inside information systems. It marly defines concepts that are not described yet by the means of other ontologies and provides the data attributes that enable to connect opinions with contextual information already defined in metadata created with other ontologies. For detailed instructions and recommendations how to fully model opinions and the results of opinion mining process refer to analysis done by Gi2MO project.

1.1 Opinions on the Web and the opinion mining process

With the birth of Web 2.0 users started to provide their input and create content on mass scape about their subjective opinions related to various topics (e.g. opinions about movies). While this kind of content can be very beneficial for many different uses (e.g. market analysis or predictions) it's accurate analysis and interpretation has not been fully harnessed yet. Information left by the users is often very disorganised and many portals that enable user input leave the user added information unmoderated.

Opinion mining (often referred as sentiment analysis) is one of the attempts bring order to those vast amounts of user generated content. The domain focuses to analyse textual content using special language processing tools and as output provides a quantified judgement of the sentiments contained in the text (e.g. if the text expresses a positive or negative opinion).

Due to the complexity of the problem and attempts to provide efficient and fast tools the area can be divided into three main research directions:

document wide sentiment analysis
sentence sentiment analysis
feature-based sentiment analysis

In relation to the World Wide Web, there is a number of common uses of opinion formalisation and analysis. Firstly, it can be applied on top of search engines to find the desired content and next run it through opinion analysis software to obtain desired statistics (e.g. Swotti). Secondly, such algorithms can used within dedicated systems that use the Web to connect to particular communities and gather their opinions on very specific topics (e.g. Internet shops or review websites).

In relation to the dedicated systems (e.g. Enterprise Systems), there the community collaborative models that have proven successful in the open web are often transferred to large enterprise to enhance knowledge exchange and bring the employees together. The same opinion mining techniques can be applied in such cases to extract particular information and use it for internal statistics and to improve knowledge search across the enterprise (e.g. see use of opinion mining in Idea Management [link]).

1.2 The Semantic Web

The Semantic Web is a W3C initiative that aims to introduce rich metadata to the current Web and provide machine readable and processable data as a supplement to human-readable Web.

Semantic Web is a mature domain that has been in research phase for many years and with the increasing amount of commercial interest and emerging products is starting to gain appreciation and popularity as one of the rising trends for the future Internet.

One of the corner stones of the Semantic Web is research on inter-linkable and interoperable data schemas for information published online. Those schemas are often referred to as ontologies or vocabularies. In order to facilitate the concept of ontologies that lead to a truly interoperable Web of Data, W3C has proposed a series of technologies such as RDF and OWL. Marl uses those technologies and the research that comes within to propose an ontology for the particular goal of describing opinions and linking them with contextual information (such as opinion topic, features described in the opinion etc.).

1.3 What is Marl for?

The goals of the Marl ontology to achieve as a data schema are:

enable to publish raw data about opinions and the sentiments expressed in them
deliver schema that will allow to compare opinions coming from different systems (polarity, topics, features)
interconnect opinions by linking them to contextual information expressed with concepts from other popular ontologies or specialised domain ontologies

For more information please refer to Marl usage study done as part of the research in the Gi2MO project.

2. Marl ontology at a glance

An alphabetical index of Marl terms, by class (concepts) and by property (relationships, attributes), are given below. All the terms are hyperlinked to their detailed description for quick reference.

Classes: AggregatedOpinion, Opinion, Polarity,

Properties: aggregatesOpinion, algorithmConfidence, describesFeature, describesObject, describesObjectPart, extractedFrom, hasOpinion, hasPolarity, maxPolarityValue, minPolarityValue, negativeOpinionCount, neutralOpinionCount, opinionCount, opinionText, polarityValue, positiveOpinionsCount,

Instances: Negative, Neutral, Positive,

3. Marl ontology overview

The Marl class diagram presented below shows connections between classes and properties used for describing opinions.

Class and Properties Diagram for the Marl Ontology (high resolution version: PNG)

3.1. Example

A very basic example below shows a single opinion annotated with Marl metadata (the second class maps the opinion structure and is shown as reference):

  <rdf:Description rdf:about="http://gi2mo.org/marl/avatar/opinion/012345/rdf">
    <marl:extractedFrom rdf:resource="http://gi2mo.org/marl/blog/avatar-review/comment/054321/rdf"/>
    <marl:describesObject rdf:resource="http://dbpedia.org/resource/Avatar_(2009_film)"/>
    <marl:describesFeature rdf:resource="http://dbpedia.org/property/runtime"/>
    <marl:polarityValue>-0.2</marl:polarityValue>
    <marl:minPolarityValue>-1</marl:minPolarityValue>
    <marl:maxPolarityValue>1</marl:maxPolarityValue>
    <marl:hasPolarity rdf:resource="http://purl.org/marl/ns#Negative"/>
    <rdf:type rdf:resource="http://purl.org/marl/ns#Opinion"/>
  </rdf:Description>
  
  <rdf:Description rdf:about="http://gi2mo.org/marl/blog/avatar-review/comment/054321/rdf">
    <dcterms:title>Re: Avatar Review</dcterms:title>;
    <sioc:has_creator rdf:resource="http://gi2mo.org/marl/blog/author/user345/"/>
    <dcterms:created>Fri, 3 Jun 2010 13:53:54 +0200</dcterms:created>;
    <sioc:reply_of rdf:resource="http://gi2mo.org/marl/blog/avatar-review/"/>
    <sioc:content>Awful movie, way to long!</sioc:content>
    <foaf:primaryTopic rdf:resource="http://gi2mo.org/marl/blog/avatar-review/comment/054321"/>
    <rdf:type rdf:resource="http://rdfs.org/sioc/ns#Post"/>
  </rdf:Description>

For more examples please see a Marl RDF export for a opinions taken from a simple idea management system instance installed for ETSIT school of Universidad Politecnica de Madrid. Furthermore, we recommend reading Marl Use Cases document for more examples and hints how to properly describe opinions with the ontology.

4. Cross-reference for Marl classes and properties

Below see a comprehensive list of all Marl classes, properties and their descriptions.

Class: marl:AggregatedOpinion

URI: http://purl.org/marl/ns#AggregatedOpinion

AggregatedOpinion - The same as Opinion class but indicates that the properties of this class aggregate all the opinions specified in the "extractedFrom" source. Optionally, if the aggregatesOpinion property is used this class could be created to aggregate only certain opinions (e.g. in a text about political scene it there could be many AggregatedOpinion classes each with opinions per different politician).

sub-class-of:: marl:Opinion
in-domain-of:: marl:aggregatesOpinion; marl:opinionCount; marl:positiveOpinionsCount; marl:negativeOpinionCount; marl:neutralOpinionCount

Marl Ontology Specification

V0.2 - 13 March 2011

Abstract

Table of Contents

Appendixes

1 Introduction

1.1 Opinions on the Web and the opinion mining process

1.2 The Semantic Web

1.3 What is Marl for?

2. Marl ontology at a glance

3. Marl ontology overview

3.1. Example

4. Cross-reference for Marl classes and properties

Class: marl:AggregatedOpinion

Class: marl:Opinion

Class: marl:Polarity

Property: marl:aggregatesOpinion

Property: marl:algorithmConfidence

Property: marl:describesFeature

Property: marl:describesObject

Property: marl:describesObjectPart

Property: marl:extractedFrom

Property: marl:hasOpinion

Property: marl:hasPolarity

Property: marl:maxPolarityValue

Property: marl:minPolarityValue

Property: marl:negativeOpinionCount

Property: marl:neutralOpinionCount

Property: marl:opinionCount

Property: marl:opinionText

Property: marl:polarityValue

Property: marl:positiveOpinionsCount

Instance: marl:Negative

Instance: marl:Neutral

Instance: marl:Positive

A Changelog

2011-03-13

2011-01-27

B Acknowledgements