Transcript Wordpress Meetup Missoula 2012-01-04

WordPress MEETUP
January 4th 2012
Ruby’s Inn & Convention Center, Missoula
Rose Lockwood
[email protected]
Information/Content Architecture
…we’re all librarians now…
 Information/Content Architecture:
the art and science of organizing and labeling websites, intranets, online
communities, and software to support findability and usability
(The IA Institute, iainstitute.org)
 Hard-core semantics: Web 3.0 – where we’re going
 Ontology-based applications
 Semantic Web/Linked Open Data
 “Crowd” semantics: Web 2.0 – where we are now
 Taxonomy-based structure
 Tagged by publishers and readers
…also known as ONTOLOGIES
A simple ontology
Tier
Pflanze
animaux
végétale
ist
ist
est
Fleischfresser
isst
est
mange
Pflanzenfresser
carnivore
herbivore
ist
ist
est
est
Löwe
antilope
lion
antilope
isst
mange
An ontology captures semantic
information (“meaning”) by
defining relationships between
concepts.
The words are symbols for the
concepts; the symbols can be
expressed in any human language.
This is a “triple” – commonly
coded in the Resource Description
Framework (RDF) language
defined for the Semantic Web by
W3C
Ontologies are networks of
concepts; hierarchy not necessary
in the network. Semantic
databases are “triple stores”.
Why will we use ontologies?
 Coherent navigation
 Flexible entry points
 Connections (highlights related information, aids discovery)
 Represents any form of information (un-/semi-/structured)
 Inferencing (look for one thing, discover a related thing)
 Concept matching (as opposed to term matching)
 Integration of external content
 Aids disambiguation
 Reasoning (related to machine learning or AI, not generally
expressed in simpler, standard ontologies)
Implementing semantic applications
…using the Open Semantic Framework…
SCO Ontology (Semantic Component Ontology)
WSF Ontology (Web Service Framework Ontology)
AGGR Ontology (Aggregation Ontology)
irON Ontology (Instance Record and Object Notation Ontology)
domain ontologies, to capture the concepts and relationships for
the purposes of a given OSF installation, and
UMBEL (optional) or other upper-level concept ontologies, used for
linkages to external systems.
http://www.mkbergman.com/wp-content/themes/ai3/images/2011Posts/sco_animation.gif
LOD
a world of ontologies
Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/
DBpedia knowledge base (as-of Sept 2011)
…becoming the hub of Linked Open Data…
 1 billion RDF triples
 385 million extracted from the English edition of Wikipedia
 665 million extracted from other language editions and links to external datasets
 3.64 million “things” (concepts) have labels and abstracts in up to 97 different
languages
 1.83 million concepts are classified in ontologies
(http://mappings.dbpedia.org/server/ontology/classes)
 416,000 persons
 526,000 places
 106,000 music albums
 60,000 films
 17,500 video games
 169,000 organisations
 183,000 species
 5,400 diseases.
Taxonomies are a precursor to ontologies,
a way to prepare for the future
Classic taxonomies are hierarchical;
“social” taxonomies (“folksonomies”) are unstructured
Folksonomy
A folksonomy is a system of classification based
on collaboratively creating and managing tags to
annotate and categorize content.
Also known as collaborative tagging, social
classification, social indexing, and social tagging.
Taxonomy
Folksonomies (large scale, like Flickr) produce
consensus around shared vocabularies, even in the
absence of a central controlled vocabulary.
WordPress taxonomies
Category
The 'category' taxonomy lets you
group posts together by sorting
them into various categories.
Tag
The 'post_tag' taxonomy is similar
to categories, but more freeform.
Impromptu classification,
generally displayed near posts or
in the form of tag clouds.
WordPress version 3 allows
fully hierarchical custom
taxonomies.
Posting/Displaying Taxonomy Classifications in WordPress
LT Compass: A European project about innovation
using language technology
My subject: Language Technology Innovation
trend…
SME/Enterprise
Perspective
Professional
Translation/
Localisation
(“push”)
Unified
Multimodal
Multiplatform
Delivery
localisation
Unified
Publishing &
Service
Content
trend…
Consumer/End-User
Perspective
Speech
Processing
Applications
Multilingual
Support
Content
Processing
Applications
trend…
Multilingual Support
Unified
Communications
& Interface
auto-translation
Unified Access to
Information &
Services
Instant
On-demand
Translation
(“pull”)
Defining & classifying Language Technology
LT-ENABLED CONSUMER, SME & ENTERPRISE APPLICATIONS
Language Technology Applications
Speech Interaction
Speech Input
Speech Output
Virtual Agents
Robots
ID/Verification
Multilingual
Support
Translator Tools
Translation Memory
Advanced Leveraging
Machine Translation
Content Processing
Text Input
Content Creation
Search & Navigation
Text Mining & Analytics
Rich Media & Speech
Analytics
Language Processing Tools
Categorisers, Clustering Engines, Language Aligners, Language Analysers, Language
Data, NLU/Question Answering Engines, Semantic Technologies, Speech
Processors, Terminology Extractors
Language Technology Methods & Components
algorithms, co-reference resolution, clustering, discourse analysis, Hidden Markov
Models, meta-data tagging, morphology segmentation, named-entity recognition,
parsing, part-of-speech tagging, query expansion, relationship extraction, signal
processing, speech segmentation, stemming, taxonomies/ontologies, topic
segmentation /recognition, truecasing, word segmentation, word sense
disambiguation, etc.
“LT Market News” a component of the project portal
links to my content curation site
News aggregation & curation using Hivefire’s Curata
The Curata dashboard