Grupo de Sistemas Ingeligentes Marl Ontology

(Draft: Under Construcion!)

Marl Ontology Use Cases

07 February 2011

This version: http://purl.org/marl/use_cases/0.1/
Latest version: http://purl.org/marl/use_cases/
Editors: Adam Westerski
Authors: Adam Westerski
Contributors: See acknowledgements

Creative Commons License

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 examples of metadata annotations for a number of different use cases. For the description of ontology and instructions how to connect it with descriptions of other resources see Ontology Specification.

Table of Contents

  1. Introduction
    1. Opinions on the Web and the opinion mining process
    2. The Semantic Web
    3. What is Marl for?
  2. Use Cases
    1. Movie Opinions
    2. Movie Review Opinions
    3. Product Opinions
    4. Idea Management System Opinions

Appendixes

  1. Changelog
  2. Acknowledgements

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 disorganized 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 devided into three main research directions:

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 extact 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 interlinkable and interoperable data schemas for information published online. Those schemas are often refered 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:

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

2. Use Cases

The following use cases aim to show how Marl Ontology could be used in different environments (as in systems) and when applied to to opinions of various complexity and structure.

2.1 Movie Opinions

The examples below present different kinds of opinions about movies and ways of translating them into metadata. In the examples, we used literals to describe the topics of the opinion (describesObject, describesFeature). The ontology specification recommends usage of entity references however we attempt to be flexible in this area and show a more simple usage of literals as well (so "Avatar" could be replaced by "http://dbpedia.org/resource/Avatar_(2009_film)").

Opinion text Opinion metadata Metadata graph
"I like this movie" 
marl:extractedFrom: 
http://gi2mo.org/.../comment/054321/rdf

marl:describesObject: 
"Batman"

marl:polarityValue: 
"0.2"

marl:minPolarityValue: 
"-1"

marl:maxPolarityValue: 
"1"

marl:hasPolarity: 
http://purl.org/marl/ns#Positive

rdf:type: 
http://purl.org/marl/ns#Opinion
Get RDF
"I like this movie but it was too long" 
Opinion #1: 
gi2mo.org/.../comment/054321/opinion/1/rdf

marl:extractedFrom: 
http://gi2mo.org/.../comment/054321/rdf

marl:describesObject: 
"Batman"

marl:polarityValue: 
"0.2"

marl:minPolarityValue: 
"-1"

marl:maxPolarityValue: 
"1"

marl:hasPolarity: 
http://purl.org/marl/ns#Positive

rdf:type: 
http://purl.org/marl/ns#Opinion



Opinion #2: 
gi2mo.org/.../comment/054321/opinion/2/rdf

marl:extractedFrom: 
http://gi2mo.org/.../comment/054321/rdf

marl:describesObject: 
"Batman"

marl:describesFeature: 
"length"

marl:polarityValue: 
"-0.1"

marl:minPolarityValue: 
"-1"

marl:maxPolarityValue: 
"1"

marl:hasPolarity: 
http://purl.org/marl/ns#Negative

rdf:type: 
http://purl.org/marl/ns#Opinion
Get RDF
"Awful directing, Cammeron is stupid. He just knows how to do good special effects." 
Opinion #1: 
gi2mo.org/.../comment/054321/opinion/1/rdf

marl:extractedFrom: 
http://gi2mo.org/.../comment/054321/rdf

marl:describesObject: 
"Avatar"

marl:polarityValue: 
"-0.5"

marl:minPolarityValue: 
"-1"

marl:maxPolarityValue: 
"1"

marl:hasPolarity: 
http://purl.org/marl/ns#Negative

marl:aggregatesOpinion 
http://gi2mo.org/.../opinion/2/rdf

marl:aggregatesOpinion 
http://gi2mo.org/.../opinion/3/rdf

marl:aggregatesOpinion 
http://gi2mo.org/.../opinion/4/rdf

rdf:type: 
http://purl.org/marl/ns#AggregatedOpinion



Opinion #2: 
gi2mo.org/.../comment/054321/opinion/2/rdf

marl:extractedFrom: 
http://gi2mo.org/.../comment/054321/rdf

marl:describesObject: 
"Avatar"

marl:describesFeature: 
"directing"

marl:polarityValue: 
"-0.4"

marl:minPolarityValue: 
"-1"

marl:maxPolarityValue: 
"1"

marl:hasPolarity: 
http://purl.org/marl/ns#Negative

rdf:type: 
http://purl.org/marl/ns#Opinion



Opinion #3: 
gi2mo.org/.../comment/054321/opinion/3/rdf

marl:extractedFrom: 
http://gi2mo.org/.../comment/054321/rdf

marl:describesObject: 
"James Cammeron"

marl:describesFeature: 
"intellect"

marl:polarityValue: 
"-0.6"

marl:minPolarityValue: 
"-1"

marl:maxPolarityValue: 
"1"

marl:hasPolarity: 
http://purl.org/marl/ns#Negative

rdf:type: 
http://purl.org/marl/ns#Opinion



Opinion #4: 
gi2mo.org/.../comment/054321/opinion/4/rdf

marl:extractedFrom: 
http://gi2mo.org/.../comment/054321/rdf

marl:describesObject: 
"Avatar"

marl:describesFeature: 
"special effects"

marl:polarityValue: 
"0.2"

marl:minPolarityValue: 
"-1"

marl:maxPolarityValue: 
"1"

marl:hasPolarity: 
http://purl.org/marl/ns#Positive

rdf:type: 
http://purl.org/marl/ns#Opinion
Get RDF

2.2 Movie Reviews Opinions

TODO

2.3. Product Opinions

TODO

2.4 Idea Management System Opinions

TODO

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.

A Changelog

B Acknowledgements

The style formatting of the following document has been inspired on FOAF specification.

Special thanks for support with Marl ontology creation and research to: Prof. Carlos A. Iglesias and members of the GSI Group of DIT department of Universidad Politécnica de Madrid.