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ELECTRONIC COMMUNICATION OF LICENCE TERMS The
Why do we need a “data dictionary”? • There’s lots of metadata already – • So we need to map from one scheme to another Data (identifiers, metadata) assigned in one context or scheme may be encountered, and may be re-used, in another place (or time or scheme) - without consulting the assigner. You can’t assume that your assumptions will be known to someone else. – – • Which should be (re-) used People use different schemes – • doi> Interoperability = the possibility of use in services outside the direct control of the issuing assigner This is a prerequisite for communication (of rights terms or anything else) Does “owner” in scheme A mean “owner” in scheme B? – – We need to map meanings A prerequisite for extensibility
What is a “data dictionary”? • A set of terms, with their definitions • • An ontology combines a data dictionary with a logical data model, providing a consistent and logical world view. An interoperable data dictionary contains terms from multiple computerized systems or metadata schemes, and shows the relationships they have with one another in a formal way. • • used in a computerized system Some data dictionaries are structured, with terms related to other terms through hierarchies and other relationships: structured data dictionaries are derived from ontologies. • • doi> The purpose of an interoperable data dictionary is to support the use together of terms from different systems. Indecs DD is structured (ontology based) and interoperable
Metadata scheme e. g. ONIX Metadata scheme e. g. SCORM Agreed term-byterm mapping or “Crosswalk”
Metadata scheme e. g. ONIX Metadata scheme e. g. SCORM
Metadata scheme e. g. ONIX Term “Author” Metadata scheme e. g. SCORM Data Dictionary Metadata Scheme Norman. Rights Term “Writer” ONIX: Author = Norman. Rights: Writer
Metadata interoperability: semantic problems But such mappings are not simple: doi> • Different names (and languages) for the same thing (journal_article vs Serial. Article. Work) • Same name for different things (title, Title) • Data elements at different levels of speciality (title vs Full. Title, Alternative. Title). • Different allowed values for elements (pii vs not pii) • Data at different levels of granularity (journal_article vs Serial. Article. Work/Serial. Article. Version). • Data in different structures (article as attribute of journal or vice versa). • Data from different sources (local codes vs ONIX codes). • Different contextual meaning (DOI of what…? ) • Different representation (1 title vs n titles). • Different mandatory requirements (ISSN mandatory vs optional) • Schemas are being updated all the time. Requires a coherent structured approach. . . etc.
So how do we make sense of this? doi> • Data dictionary uses an “ontology” • “An explicit formal specification of how to represent the objects, concepts and other entities that are assumed to exist in some area of interest and the relationships that hold among them” • Because relationships can be complex
The dictionary model doi> • The methodology is the
The dictionary model doi> Agent Time Place Resource
The dictionary model doi> Agent Norman Paskin Time Place London 2004 -12 -02 Resource 041202 BICNISO. ppt
The dictionary model doi> Agent Time Event: Norman Paskin presented 041202. ppt in London on 2 Dec 2004 Resource Place
Building views of “metadata”… • Q: “This isn’t how I think of my metadata! ”. . ”it’s just a series of “things about” something. How does this more complex approach fit what I have? • A: This is simply a deeper view for the purposes of analysis. . You don’t need to change your own approach. The “events” view builds from the simple “things about” view:
Building views of “metadata”… entity 1. attribute view – simplest, most direct: “things about…” isbn “ 0297816470” Author S Pinker (values may be strings, IDs etc) attribute
Building views of “metadata”… entity relationship 2. association or relationship view – richer, more indirect: book “ 0297816470” has. Title “Words & Rules” • treats attributes as defined entities and others e. g. book “ 0297816470” has. Author “Stephen Pinker” • allows multiple occurrences entity
Building views of “metadata”… agent time resource context 3. context view – richest, most indirect publishing. Event has. Agent. Type publisher “Weidenfeld” has. Resource. Type book “ 0297816470” has. Time. Type date. Of. Publication “ 2002” has. Place. Type place. Of. Publication “UK” • Analysis moves from attribution to attribution process (Event) • Most efficient handling of complex multiple metadata e. g. a rights catalogue (“all rights transactions are events”) • Allows analysis of complex relationships and meaning place
An ontology approach uses the deeper view of metadata Three levels of attribution, moving from simple (static) to richer (dynamic events): entity Attribute (static view) Relationship Context (dynamic view) entity relationship agent attribute entity resource context time place
Tested • i. DD has a long history and is used in several major activities. • • Built using methodology from the
Neutral as to business model • The semantic analysis underlying the i. DD is independent of any implementation model. • It was fundamental to indecs (despite “e-commerce” in its name) that it had no inherent commercial model, and it remains so for all the work that has followed it. • It is just as critical to be able to say "this is not subject to copyright" as to say the opposite; – any "non-commercial" person or organization has is to be able to state that something is freely available and under what circumstances. • A broad ontology, supporting rights expressions, must be able to support any kind of expression of any kind of right, agreement or licence or any terms (or none). • Most organizations have the need for both freedom and protection of intellectual property in different contexts. – The i. DD is not solely a tool for intellectual property as “commercial property” but is neutral as to the intellectual property regime being used.
Does not mandate one metadata scheme • The aim of the i. DD is to facilitate mapping between schemes • The more precise the input, the more precise the output – e. g. a mapping from simple DC to SCORM will of necessity be “lossy” • Some uses will set minimum standards – e. g. DOI Registration Agencies have rules that must be followed in the DOI application to ensure that the metadata can be mapped into the i. DD to declare Application Profiles • Any user is otherwise free to use their own metadata schemes for gathering, storing or disseminating metadata. i. DD facilitates input and output to others schemes = semantic interoperability
Provides authority • Every term entered into the i. DD carries information on its status as to origin and mapping agreement • If reciprocally agreed, then can be an assured mapping – which will enable users of the dictionary to interpolate mappings from their own schemes, through i. DD, to scheme A and know that this will be considered authoritative by scheme A. . • Anyone contributing terms to the i. DD can specify who is allowed to see or specify their own terms. • Some terms will be accessible to all: – e. g. ONIX, some kernel DOI terms, and the MPEG 21 RDD.
Construction • Based on DD methodology and Contextual Ontologyx Architecture tools, terms from various sources (ONIX, RDD, DOI) • …But users need not understand the underlying concepts and construction of the i. DD. – It is no more a requirement to know the details than it is for the designer of a web page to read all the underlying internet protocol RFCs. • A fundamental role of the IDF and others with the i. DD is to provide assurance to users that the work has been peerreviewed and tested, and make available tools. • Some key features are: – Extensible and granular to whatever level of detail is required. – Multiple, different, specialized views are available: these include a Rights Model, based on a set of specialized Contexts. – Local terms: local (internal) data elements and names can be added into the ontology – External terms: incorporates external and standard schemes such ISO territory, currency and language codes, and sector specific external schemes
Use • Current use of the dictionary is on a project–by-project basis using technical consultancy • An automated web based look-up system for the Dictionary is under development for IDF use (and potentially others e. g. RDD) • Access will be granular: those with authority to access the Dictionary able to view what is appropriate – private terms are kept confidential.
Data dictionaries Development of
ELECTRONIC COMMUNICATION OF LICENCE TERMS The
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