Content architecture and website design Word Press MEETUP

Content architecture and website design Word. Press MEETUP January 4 th 2012 Ruby’s Inn & Convention Center, Missoula Rose Lockwood rose@roselockwood. com

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

The Future: semantically organized content using “knowledge bases” …also known as ONTOLOGIES

A simple ontology Tier Pflanze animaux végétale ist est Fleischfresser isst est mange Pflanzenfresser carnivore herbivore ist 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 W 3 C 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) ir. ON 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/ai 3/images/2011 Posts/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.

The Present: content organized using tags and taxonomies 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.

Word. Press 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. Word. Press version 3 allows fully hierarchical custom taxonomies.

Posting/Displaying Taxonomy Classifications in Word. Press

How I use classification in “content-based marketing” 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

demo…