marzo 25, 2009
Aims of the Tutorial
- Give an overview of Ontology Learning techniques as well as a synthesis of approaches
- Provide a ‘start kit’ for Ontology Learning
- Highlight interdisciplinary aspects and opportunities for a combination of techniques
Structure of the Tutorial
- Part I Introduction – Philipp Cimiano
- Part II Ontologies in Knowledge Management & Ontology
- Life Cycle – Michael Sintek
- Part III Methods in Ontology Learning from Text –
- Paul Buitelaar & Philipp Cimiano
- Part IV Ontology Evaluation – Marko Grobelnik
- Part V Tools for Ontology Learning from Text – All
- Wrap-up Paul Buitelaar
- AI: Knowledge Acquisition. Since 60s/70s: Semantic Network Extraction and similar for Story Understanding. Systems: e.g. MARGIE (Schank et al., 1973), LUNAR (Woods, 1973)
- NLP: Lexical Knowledge Extraction. 70s/80s: Extraction of Lexical Semantic Representations from Machine Readable: Dictionaries. Systems: e.g. ACQUILEX LKB (Copestake et al.). 80s/90s: Extraction of Semantic Lexicons from Corpora for Information Extraction. Systems: e.g. AutoSlog (Riloff, 1993), CRYSTAL (Soderland et al., 1995)
- IR: Thesaurus Extraction. Since 60s: Extraction of Keywords, Thesauri and Controlled Vocabularies. Based on construction and use of thesauri in IR (Sparck-Jones, 1966/1986, 1971). Systems: e.g. Sextant (Grefenstette, 1992), DR-Link (Liddy, 1994)
Ontologies in Computer Science
- Ontology refers to an engineering artifact:
- It is constituted by a specific vocabulary used to describe a certain reality, as well as
- a set of explicit assumptions regarding the intended meaning of
- the vocabulary.
- An ontology is an explicit specification of a conceptualization. ([Gruber 93])
- An ontology is a shared understanding of some domain of interest. ([Uschold & Gruninger 96])
Why Develop an Ontology?
- To make domain assumptions explicit
- Easier to change domain assumptions
- Easier to understand and update legacy data
- To separate domain knowledge from operational knowledge
- Re-use domain and operational knowledge separately
- A community reference for applications
- To share a consistent understanding of what information means
Tools for Ontology Learning from Text.
SEKTbar: User profiling
Jožef Stefan Institute
A Web-based user profile is automatically generated while the user is browsing the Web.
It is represented in the form of a user-interest-hierarchy (UIH)
The root node holds the user’s general interest, while leaves hold more specific interests
UIH is generated by using hierarchical k-means clustering algorithm
Nodes of current interest are determined by comparing UIH node centroids to the centroid computed out of the m most recently visited pages.
The user profile is visualized on the SEKTbar (Internet Explorer Toolbar)
The user can select a node in the hierarchy to see its specific keywords and associated pages (documents)
Availability: open source (C++, .NET)
© Paul Buitelaar, Philipp Cimiano, Marko Grobelnik, Michael Sintek: Ontology Learning from Text. Tutorial at ECML/PKDD, Oct. 2005, Porto, Portugal.
ECML-OntologyLearningTutorial-20050923.pdf (application/pdf Objeto).
marzo 12, 2009
The Protégé Ontology Editor and Knowledge Acquisition System.
Protégé is a free, open source ontology editor and knowledge-base framework.
The Protégé platform supports two main ways of modeling ontologies via the Protégé-Frames and Protégé-OWL editors. Protégé ontologies can be exported into a variety of formats including RDF(S), OWL, and XML Schema. (more)
Protégé is based on Java, is extensible, and provides a plug-and-play environment that makes it a flexible base for rapid prototyping and application development. (more)
Ontology Development 101 – general ontology development guidelines, helpful hints, etc.
Protégé multi-user mode – setup and use of the multi-user client/server capabilities
Collaborative Protégé – a Protégé extension to support collaborative ontology development
WebProtégé – a lightweight web-based version of the Protégé ontology editor
Protege Ontology Library!
This page is organized into the following groupings:
If your ontology is available in multiple formats, please feel free to link to it from multiple sections.
- Education Ontology: Ontology for the Minnesota Department of Education based on the National Information Exchange Model (NIEM) structures and ISO/IEC 11179 standards. This domain includes information about K-12 students, teachers, schools, districts, enrollments, assessments, USDA food and nutrition programs, and on-line courses. Includes approximately 400 data elements. A case study will be presented at the Semantic Technology conference in March 2006. Feedback -> http://www.danmccreary.com.
- AIM@SHAPE Ontologies: Ontologies pertaining to digital shapes. Source: AIM@SHAPE NoE – Advanced and Innovative Models And Tools for the development of Semantic-based systems for Handling, Acquiring, and Processing knowledge Embedded in multidimensional digital objects.
- Basic Formal Ontology (BFO)
- Dependable Systems Ontology: Ontology about resilient and dependable systems including threats, failures, faults and errors as used in the ReSIST project.
- FOAF Ontology: An ontology describes people, the links between them and the things they create and do. Contributed by Dan Brickley and Libby Miller.
- ka.owl: Defines concepts from academic research. Contributed by Ian Horrocks
- OSM – Ontology for Support and Management: An ontology that contains all constructs required for the various versions of the Ontology for Support and Search Engine Optimization Management of pervasive services by using ontology-based policy mechanisms run by IST-Context project and the research extensions towards Onto-Context framework to demonstrate advantages when context information is used for controlling management operations.
- Videogame’s Elements Ontology: A videogame’s elements ontology that is used to model different videogame’s properties like playability. Contributed by José Luis González University of Granada, Spain.
- Dublin Core: Representation of Dublin Core metadata in Protege.
- Suggested Upper Merged Ontology (SUMO): An ontology developed within the IEEE Standard Upper Ontology Working Group with the goal of developing a standard ontology that will promote data interoperability, information search and retrieval, automated inferencing, and natural language processing.
More links here http://protegewiki.stanford.edu/index.php/Protege_Ontology_Library
The 11th International Protégé Conference will be held June 23-26, 2009 in Amsterdam, Netherlands.