Providing useful perspectives onto massive digital collections

Mark GaheganTawan Banchuen, Will Smart, Brandon Whitehead

Centre for eResearchUniversity of Auckland, New Zealand

BeSTGRID

There are 2 rules for success in life:1. Never share everything you know2.

There is always missing information/knowledge…

Vannevar Bush, As We May Think (1945)

“There is a growing mountain of research. But there is increased evidence that we are being bogged down today as specialization extends. The investigator is staggered by the findings and conclusions of thousands of other workers - conclusions which he cannot find time to grasp, much less to remember, as they appear.

Professionally our methods of transmitting and reviewing the results of research are generations old and by now are totally inadequate for their purpose…”

“…A record, if it is to be useful to science, must be continuously extended, it must be stored, and above all it must be consulted.”

Sarah E. Fratesi, 2008Journal of Research PracticeVolume 4, Issue 1, Article M1,Scientific Journals as Fossil Traces of Sweeping Change in the Structure and Practice of Modern Geology

The knowledge explosion: Geological Research

The Pain

• Digital collections are growing at a geometric rate…• How much useful information is never used because

it cannot be found?• How much time & money is lost trying to interpret

(or re-interpret) acquired data?• How much effort & uncertainty is involved in trying

to understand the work of others?

Our systems compartmentalise our understanding: this is a very bad thing

Our systems:– Databases– Analysis & simulation tools– Document repositories (e.g. articles, books, theses)– Ontologies– Visualisations– Organisation charts– Calendars– Wikis / Blogs / email

Meaning resides across ALL of these places—but it is difficult to extract and connect

Questions we might ask Technologies to support these questions

Created by person X, at time T, etc. Good metadata

Described by (registered to) these semantic tags:

Ontology registration / alignment service

Was created in this way: Representation of workflows

Plays a role in these workflow(s): Search through workflow instances

Has been used to fulfill these task(s): Activity logging at the Portal

Has been used by these people/groups: Social networks, organisation charts, activity logging

Has been used by researchers with the following interests:

Semantic associations & inference

Is referenced in these publications Integration with digital libraries

Is most often used with these method(s) Data mining, recommender systems

Has received the following reviews User feedback / ranking

Is similar to, or differs from, dataset B in the following way(s):

Difference metrics for resources

All the resources in the collections housed in the GEON cyber-infrastructure

Things to bear in mind (drivers)…

1. The sheer volume of resources in digital collections.2. The dynamic nature of the collection catalogs in an e-

Infrastructure.3. The need to support multiple search strategies to

find useful resources.4. The need to help explain what resources mean, or to

contextualise them in some way, so they can be used appropriately

5. The need to capture new connections and evolving understanding.

A knowledge gateway to an eResearch community

Examples from geoscience

Conceptual Universe of GEON: now organised by themes

Navigating through the themes

GEON: Institutions, Personnel, PIs, Co-PIs, grad students

What has person A contributed?(Kai Lin: GEON researcher)

Complete conceptual neighbourhood of a resource (an article in this case)

Perspectives as filters

Perspectives filter an information space according to particular situations. Perspectives A and B preferentially select different types of resources and relations; the ability to view perspectives can show how someone else made sense of a given set of resources.

Who used a particular article? (its user community)

Multiple perspectives:What did A create that B used?

Perspectives (1) as SPARQL QueriesConsider the following SPARQL query:

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>PREFIX theme: <http://www.geovista.psu.edu/cV/themes.owl#>SELECT ?xWHERE (?x rdf:type rdfs:Class?x rdfs:subClassof theme:Geosciences)

Here “rdf” and “rdfs” are namespace prefixes for W3C’s RDF and RDFS languages, and “theme” is the namespace prefix for an ontology that defines subjects of interest within the geosciences. Executing this query on the ontology will return all the resources that are sub-classes of the resource “theme:Geosciences”.

Precise definition of a global perspective based on the query is as follows. Let T denote the original ontology (which is a set of RDF statements in the form of [subject, predicate, object]), Q denotes an RDF query, S(Q) denotes the set of subject constants in Q (empty in the above example, since all subjects are variables), P(Q) denotes the set of predicate constants in Q (including “rdf:type” and “rdfs:subClassOf” in the example), and O(Q) denotes the set of object constants in Q (including “rdf:Class” and “theme:Geosciences” in the example). If we use R to denote the set of resources returned by the query, then a global perspective is a set of RDF statement, denoted by PS, which satisfy the following conditions.For any statement [subject, predicate, object] in PS: if predicate P(Q) then subject R S(Q) and object R O(Q);∈ ∈ ∪ ∈ ∪if predicate P(Q) then subject R and object R;∉ ∈ ∈[subject, predicate, object] T∈

Perspectives (2)truncated concepts become properties

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B

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Perspectives (3): different facets of a concept are ‘externalised’

image

user A user Cuser B

PropertiesDate: ddmmyyyyScale: 1:xxxxxxxxCountry: “………..”Content: (m, n, o, p)

image

country content

PropertiesDate: ddmmyyyyUser: (A, B, C)Scale: 1:xxxxxxxx

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p

onFold in

Fold out

Which topics are closely related?

Authors folded into themes,

Themes connected together by author properties

Intersecting research interests of a science community

Conclusions

• Organising resources according to different knowledge facets holds considerable promise– We may not be able to capture what things mean directly, but we can

provide some signifiers (clues)– Usage data can provide very strong signifiers

• Intuitive navigation metaphors are needed– Strong filters are needed—perspectives—to avoid overcrowding &

confusion,– And to better match the users’ conceptual models

• We need strong identifiers for digital resources, so we can find references to them from many systems– This facilitates value-added services (semantic web-search, maps, use

cases, provenance, text descriptions, markup tools, etc)

END