1 Peter Fox Data Science – CSCI/ERTH/ITWS-4350/6350 Week 12, November 26, 2013 Webs of Data and Data on the Web, the Deep Web, Data Discovery, Data Citation

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Peter Fox

Data Science – CSCI/ERTH/ITWS-4350/6350

Week 12, November 26, 2013

Webs of Data and Data on the Web, the Deep Web, Data

Discovery, Data Citation

Contents• Review of reading assignment

• Webs of data and semantic web

• Data on the web, linked data

• Deep web

• Data discovery

• Data citation

• Summary

• Next week

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Reading• Mealy

• Wickett et al.

• Data Quality European Union Presentation

• ISO Technical Standards - General Reference

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Webs of data (science)• Early Web - Web of pages

• http://www.ted.com/index.php/talks/tim_berners_lee_on_the_next_web.html

• Semantic web started as a way to facilitate “machine accessible content”– Initially was available only to those with familiarity

with the languages and tools, e.g. your parents could not use it

• Webs of data grew out of this– One specific example is W3C’s Linked Open

Data

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http://www.ted.com/index.php/talks/tim_berners_lee_on_the_next_web.html

http://www.ted.com/index.php/talks/tim_berners_lee_on_the_next_web.html

Semantic Web• http://www.w3.org/2001/sw/

• “The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries. It is a collaborative effort led by W3C with participation from a large number of researchers and industrial partners. It is based on the Resource Description Framework (RDF)...”

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http://www.w3.org/2001/sw/

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Terminology• Semantic Web

– An extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation, www.semanticweb.org

– Primer: http://www.ics.forth.gr/isl/swprimer/ • Ontology (n.d.). The Free On-line Dictionary of

Computing. http://dictionary.reference.com/browse/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.

http://www.ics.forth.gr/isl/swprimer/

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Semantic Web Layers

http://www.w3.org/2003/Talks/1023-iswc-tbl/slide26-0.html, http://flickr.com/photos/pshab/291147522/

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Application Areas for SW• Smart search• Annotation (even simple forms), smart tagging• Geospatial• Implementing logic (rules), e.g. in workflows• Data integration• Verification …. and the list goes on• Web services• Web content mining with natural language parsing• User interface development (portals)• Semantic desktop• Wikis - OntoWiki, SemanticMediaWiki• Sensor Web• Software engineering• Explanation

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Semantic Web Basics• The triple: {subject-predicate-object}

Interferometer is-a optical instrumentOptical instrument has focal length

• W3C is the primary (but not sole) governing org.– RDF– OWL 1.0 and 2.0 - Ontology Web Language

• RDF – programming environment for 14+ languages, including C, C++,

Python, Java, Javascript, Ruby, PHP,...(no Cobol or Ada yet ;-( )

• OWL programming for Java

• Closed World - where complete knowledge is known (encoded), AI relied on this

• Open World - where knowledge is incomplete/ evolving, SW promotes this

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Ontology Spectrum

Catalog/ID

SelectedLogical

Constraints(disjointness,

inverse, …)

Terms/glossary

Thesauri“narrower

term”relation

Formalis-a

Frames(properties)

Informalis-a

Formalinstance

Value Restrs.

GeneralLogical

constraints

Originally from AAAI 1999- Ontologies Panel by Gruninger, Lehmann, McGuinness, Uschold, Welty; – updated by McGuinness.Description in: www.ksl.stanford.edu/people/dlm/papers/ontologies-come-of-age-abstract.html

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Semantic Web Myths• ‘the Semantic Web is a reincarnation of Artificial Intelligence

on the Web’ (closed world versus open world)• ‘it relies on giant, centrally controlled ontologies for

"meaning" (as opposed to a democratic, bottom-up control of terms)’

• ‘one has to add metadata to all Web pages, convert all relational databases, and XML data to use the Semantic Web’

• ‘one has to learn formal logic, knowledge representation techniques, description logic, etc, to use it’

• ‘it is, essentially, an academic project, of no interest for industry’

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Integrating Multiple Data Sources

• The Semantic Web lets us merge statements from different sources

• The RDF Graph Model allows programs to use data uniformly regardless of the source

• Figuring out where to find such data is a motivator for Semantic Web Services

#Ionosphere #magnetic

“100”“TerrestrialIonosphere”

name

hasCoordinates

hasLowerBoundaryValue

Different line & text colors represent different data sources

hasLowerBoundaryUnit

“km”

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Drill Down /Focused Perusal• The Semantic Web uses Uniform

Resource Identifiers (URIs) to name things

• These can typically be resolved to get more information about the resource

• This essentially creates a web of data analogous to the web of text created by the World Wide Web

• Ontologies are represented using the same structure as content– We can resolve class and

property URIs to learn about the ontology

InternetInternet

…#NeutralTemperature

...#ISR

…#Norway

…#EISCAT

measuredby

type

locatedIn

...#FPI

...#MilllstoneHill

operatedby

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Statements about Statements• The Semantic Web allows us to

make statements about statements– Timestamps

– Provenance / Lineage

– Authoritativeness / Probability / Uncertainty

– Security classification

– …

• This is an unsung virtue of the Semantic Web

#Aurora

Red

#Danny’s

20031031

hascolor

hasSource

hasDateTime

Ontologies Workshop, APL May 26, 2006

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‘Collecting’ the ‘data’

• Part of the (meta)data information is present in tools ... but thrown away at output e.g., a business chart can be generated by a tool: it ‘knows’ the structure, the classification, etc. of the chart, but, usually, this information is lost storing it in web data would be easy!

• SW-aware tools are around (even if you do not know it...), though more would be good: – Photoshop CS stores metadata in RDF in, say, jpg files

(using XMP)– RSS 1.0 feeds are generated by (almost) all blogging

systems (a huge amount of RDF data!)

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‘Collecting’ the ‘data’• Scraping - different tools, services, etc, come

around every day: – get RDF data associated with images, for

example: service to get RDF from flickr images– service to get RDF from XMP

– XSLT scripts to retrieve microformat data from XHTML files

– scripts to convert spreadsheets to RDF – e.g. see csv2rdf4lod and the tools, tutorials, demos at http://logd.tw.rpi.edu

– schema.org and the datasets extension

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‘Collecting’ the ‘data’• SQL - A huge amount of data in Relational Databases

– Although tools exist, it is not feasible to convert that data into RDF

– Instead: SQL ⇋ RDF ‘bridges’ are being developed: a query to RDF data is transformed into SQL on-the-fly

– Reading for this week, article by Berners Lee and Sahoo et al.

– RDB2RDF W3 working group - http://www.w3.org/2001/sw/rdb2rdf/

– D2RQ/ D2RServer– Commercial solutions appearing

• NoSQL• Other ‘graph’ forms…

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More Collecting• RDFa extends XHTML by:

– extending the link and meta to include child elements– add metadata to any elements (a bit like the class in

microformats, but via dedicated properties)– Used in schema.org/ datasets

• It is very similar to microformats, but with more rigor: – it is a general framework (instead of an ‘agreement’ on

the meaning of, say, a class attribute value)– terminologies can be mixed more easily

• GRDDL - Gleaning Resource Descriptions from Dialects of Languages

Linked open data• http://linkeddata.org/guides-and-tutorials

• http://tomheath.com/slides/2009-02-austin-linkeddata-tutorial.pdf (we will look at some of these slides now, #1-25 and 30-37)

• And of course:– http://logd.tw.rpi.edu/

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September 2011 - http://lod-cloud.net/

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“Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/”

(Class 2) Management• Creation of logical collections

• Physical data handling

• Interoperability support

• Security support

• Data ownership

• Metadata collection, management and access.

• Persistence

• Knowledge and information discovery

• Data dissemination and publication 21

Data Management and WOD• How is the data managed?

– Found?– Curated?

• What about the metadata?

• What problems are introduced/ solved?

• See discussion in: Parsons and Fox (2012): http://mp-datamatters.blogspot.com/

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Data on the Web, Internet• Data behind web services

• Data files on web sites

• We have covered data as service approaches (week 11)

• Thinking you have found data when you have really only found information and metadata

• The real difference between this topic and the next one is:– Access and dissemination– Level of curation (and often description) 23

Data on the internet• http://www.dataspaceweb.org/

• Data files on other protocols– FTP– RFTP– GridFTP– SABUL– XMPP/AMQP– Others…

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Deep web• Data behind web services

• Data behind query interfaces (databases or files)

• Introduces a different curation problem

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The loose definition• Something that a crawler cannot find and/or

index– Creates the other definition of shallow web

• Has many implications for discovery, access and use

• Curation is more complex to satisfy this definition, i.e. not a matter of just putting files ‘on the web’

• 50, 100, 1000 times the ‘shallow web’? 26

Managing (in) the deep web• Sometimes, the deep web aspect of a data

source can be due to extreme obscurity, language peculiarities, NO metadata, NO documentation

• There are no known studies of how effective data management (what you are learning) could change the percentage of deep/ shallow

• Semantics are often put forward as a solution http://www.mkbergman.com/458/new-currents-in-the-deep-web/ 27

Internet impacts on management

• Management of data that is… on the Internet!

• Web –> ‘stateless’

• Curation, Preservation –> highly stateful (by definition)

• You will hear terms such as digital curation and digital preservation but what about internet curation and internet preservation (Internet Archive)? 28

Thus data frameworks are appearing

• Many – meaning they go beyond web sites, they incorporate many of the data management functions

• Initially syntactic – e.g. OPeNDAP, ADDE, ODATA, OODT

• Application oriented – e.g. virtual observatories– Semantic – e.g. Virtual Solar-Terrestrial

Observatory

• ALL of these are changing the nature of data management and role of data ‘providers’

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BOM, Melbourne, VIC 20071015 (Fox)

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Some DefinitionsDAP = Data Access Protocol

Model used to describe the data; Request syntax and semantics; and Response syntax and semantics.

OPeNDAP The software; Numerous reference implementations; Core/libraries and services (servers and clients).

OPeNDAP Inc. OPeNDAP is a 501.c(3) non-profit corporation; Formed to maintain, evolve and promote the

discipline neutral DAP that was the DODS core infrastructure.


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Considerations with regard to the development of DAP and OPeNDAP

Many data providers

Many data formats

Many different semantic representations of the data

Many different security requirements

Many different client types


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Broad Vision

A world in which a single data access protocol is used for the exchange of data between network based applications regardless of discipline.

A layer above TCP/IP providing for syntactic and semantic consistency not available in existing protocols such as FTP.


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Practical Practical Considerations

The broad vision:

Is syntactically achievable, but

Was not semantically achievable, at least not fully, but perhaps in the near term.


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The DAP has been designed to be as general as possible without being constrained to a particular discipline or world view.

The Data Access Protocol (DAP)

The DAP is a discipline neutral data access protocol; it is being used in astronomy, medicine, earth science,…

Provides data format and location, and data organization transparency

Is metadata neutral


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OPeNDAP V4 (Hyrax) Architecture

OLFS BES

OPeNDAP Lightweight Front end Server (OLFS) Receives requests and asks the BES to fill them Uses Java Servlets Does not directly ‘touch’ data Multi-protocol

Data

Back End Server (BES) Reads data files, Databases, et c., returns info May return DAP2 objects or other data Does not require web server

Client


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netCDF C

Ferret GrADS

netCDF Java

IDV VisAD ncBrowse

Matlab

MatlabClient

Access ExcelIDL

IDLClient

ArcGIS

pyDAP

OPeNDAP Clients

ArcGIS

pyDAP

NCL

NCLClient

Internet

WebBrowser OPeNDAP

DataConnector


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Data Data Data Data Data Data Data

HDF5

HDF4 JDBC

FreeFormFITS

CDF CEDAR

Data

netCDF

netCDF HDF4 HDF5

Data

DSP

DSP

Data

JGOFS

Tables SQL FITS CDFFlat

Binary CEDAR

Data

General

ESML

OPeNDAP Servers

CDM

Internet


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Data

GRIBBUFR

OPeNDAP

GDS

Data

CODAR

CODAR

Data

FDS

netCDFOPeNDAP

Data

General

pyDAP

Data

DAPPER

netCDFOPeNDAP

Data

netCDFOPeNDAP

TDS

Data

General

pyDAP

Data

netCDFOPeNDAP

TDS

OPeNDAP Servers (specialized processing)

Data

ESG

netCDFOPeNDAP

Internet


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Servers

Servers may also provide other services

Directory traversal.

Browser-based form to build URL.

Ascii or other representations of data.

Metadata associated with the data.

Server side functions.

Summary

Tetherless World Constellation

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Data discovery• Free text search on the internet/ web

• Data portals

• What makes discovery work?– For Deep Web?– For Linked Data?

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Data discovery• What makes discovery work?

– Metadata– Logical organization– Attention to the fact that someone would want to

discover it– It turns out that file types are a key enabler or

inhibitor to discovery

• What does not work?– Result ranking using *any* conventional

algorithms43

Smart search• Semantically aware search, e.g.

http://noesis.itsc.uah.edu

• Faceted search, e.g. – mspace (http://mspace.fm )– jSpace – Exhibit (MIT)– S2S – e.g. International Open Government

Dataset Catalog (IOGDC; http://logd.tw.rpi.edu )

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NOESIS

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Search Application integration!

Deep web dashboards…

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Intl. Open Govt. Data Cat.http://logd.tw.rpi.edu

Federated search• “is the simultaneous search of multiple online

databases or web resources and is an emerging feature of automated, web-based library and information retrieval systems. It is also often referred to as a portal or a federated search engine.” wikipedia

• Libraries have been doing this for a long time (Z39.50, ISO23950)

• Key is consistent search metadata fields (keywords)• E.g. Geospatial One Stop http://www.geodata.gov

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Data Citation• “Sound, reproducible scholarship rests upon

a foundation of robust, accessible data. For this to be so in practice as well as theory, data must be accorded due importance in the practice of scholarship and in the enduring scholarly record. In other words, data should be considered legitimate, citable products of research. Data citation, like the citation of other evidence and sources, is good research practice.”

(http://www.force11.org/datacitation) 50

Landing page – a short form

http://data.rpi.edu/repository/handle/10833/24

Long formhttp://data.rpi.edu/repository/handle/10833/24?show=full

Conneg• Many examples, but what follows is ~ from:

http://www.crosscite.org/cn/

• Also see - http://labs.crossref.org/ and http://data.datacite.org/

• What is it?

– Es ce que vous parlez Français?– Do you speak html or JSON or RDF?

Conneg

Application level, e.g. as JSON

Supported content types..

Example formatting..

Other coolness…• curl -LH "Accept:

application/vnd.crossref.unixref+xml;q=1, application/rdf+xml;q=0.5" http://dx.doi.org/10.1126/science.169.3946.635

• curl http://data.datacite.org/application/x-datacite+text/10.5524/100005– Li, J; Zhang, G; Lambert, D; Wang, J; (2011):

Genomic data from the Emperor penguin (Aptenodytes forsteri); GigaScience. http://dx.doi.org/10.5524/100005

Further integration..

Additional refs.• EPIC for identifier conventions:

http://pidconsortium.eu

• Dspace and Handle - http://tw.rpi.edu/web/project/Data.rpi.edu/Architecture (install/ config notes in PDF and in Section 4.4.4, page 55 of Handle installation manual for V 1.8)

Aren’t you happy this is the last lecture?

• Otherwise – we’d go into a long discussion of the merit of data citation to achieve the business case in an earlier slide

• It would be enlightening but torture…

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Summary• Theme of data management in the chaotic

and enabling environment of the web, internet

• Emergence of frameworks that encompass some aspects of data management

• Unlocking data in a useful way is an immense challenge (discovery, citation?)

• Anything/ everything you can do by following what you have learned in this course will help

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What is next• Dec. 3 – project presentations

• Final assignment to be handed in today!

• Reading for this week: – Semantic Deep Web, James Geller, Soon Ae

Chun, and Yoo Jung An, – The Deep Web (Internet Tutorials) – Digital Image Resources on the Deep Web– Parsons and Fox: Is Data Publication the Right

Metaphor?

• Class evaluations…65

Documents

1 Peter Fox Data Science – CSCI/ERTH/ITWS-4350/6350 Week 12, November 26, 2013 Webs of Data and Data on the Web, the Deep Web, Data Discovery, Data Citation