Marko Grobelnik, Janez Brank, Blaž Fortuna, Igor Mozetič

Outline  Ontology  Ontolight  Definition  Grounding  Population  Applications  Integration in OntoGen  Demo

What is ontology?  Ontology is a data model that represents a set of concepts within a domain and the relationships between those concepts.  Generally it consist of  Classes: sets, collections, or types of objects  Instances: the basic or "ground level" objects  Relations: ways that objects can be related to one another  It can be used  … as schema for knowledge management system,  … to reason about the objects within that domain,  etc.

Sample Ontology

Examples of Real-world Ontologies  AgroVoc  Multilingual thesaurus for the field of Agriculture, Forestry, Fisheries, Food Security and related stuff  Consists of  terms in different languages,  thesaurus relationships between terms  Broader, narrower, related  ASFA  Thesaurus used for annotating bibliography related to aquatic science literature  EuroVoc  Multilingual thesaurus used by European institutions  Acquis Communitarian corpus is annotated by EuroVoc  Cyc  Knowledge base, formalization of fundamental human knowledge  Dmoz – The Open Directory Project  Worlds largest directory of WWW, maintained by volunteer editors

What is Ontolight?  Simple model covering most of the well known light-weight ontologies  Stores ontology like a rich graph  Defined as:  List of languages used for lexical terms (covers multliliguality)  List of class-types (types of nodes in the graph)  List of classes (nodes in the graph)  List of relation types (types of links in the graph)  List of relations (links in the graph)  Grounding model  A function which proposes a set of classes for a given instance  Classification in machine learning

Grounding  Mutliclass classification model trained on the instances of ontology  In case of Dmoz web pages  In case of EuroVoc EU legislation  We used centroid-based classifier  Calculates a centroid vector for each class  Uses knowledge of hierarchy  Classification performed by kNN algorithm  Highly scalable – can handle 100s of thousands of classes

Population  Takes instance as an input  Output is a list of suggested classes  Example from EuroVoc  Instance: “Slovenia and Croatia are having a fishing industry”  Output:

OntoGen  Ontology construction and learning  Semi-Automatic:  Text-mining methods provide suggestions and insights into the domain  The user can interact with parameters of text-mining methods  All the final decisions are taken by the user  Data-Driven:  Most of the aid provided by the system is based on some underlying data provided by the system  Instances are described by features extracted from the data (e.g. bag-of-words vectors) Concept hierarchy List of suggested sub-concepts Ontology visualization Selected concept Concept’s details Concept’s instance management Selected concept Keywords Selected instance

Contextualized ontology generation  Ontolight is integrated with Ontogen  Helps at new ontology generation by means of existing ontologies  User loads Ontolight into Ontogen at start  Suggestion methods:  Concept suggestion  Offers concepts from loaded Ontolight as possible sub-concepts  Name suggestion  Offers names of concepts from Ontolight as possible concept names  All suggestions are integrated in semi-automatic manner

Concept suggestion  User selects concept  User selects Ontolight  OntoGen classifies each document into context – Ontolight ontology  Concepts with most documents are provided as suggestions to the user

Name suggestion  User selects concept  OntoGen classifies each document into context – loaded Ontolight ontologies  Names of concepts with most classified documents are provided as suggestions to the user

AgroVoc and EuroVoc applied to Yahoo finance data Demo