The goal of "Meeting Big Data Challenges through Ontology" is
to identify challenges that will advance ontology and semantic web
technologies, increase applications, and accelerate adoption.
Current State
Ontology may tame big data, drive innovation, facilitate the
rapid exploitation of information, contribute to long-lived and
sustainable software, and improve Complicated Systems Modeling.
Ontology might help big data, but why this usually fails
- easy to create ontologies that myriad incompatible
ontologies are being created in ad hoc ways
leading to the creation of new, semantic silos
- The Semantic Web framework as currently conceived and
governed by the W3C (modeled on html) yields minimal standardization
- The more semantic technology is successful;
they more we fail to achieve our goals
* Just as it’s easier to build a new database, so it’s easier
to build a new ontology for each new project
* You will not get paid for reusing existing ontologies (Let a
million ontologies bloom)
* There are no ‘good’ ontologies, anyway (just arbitrary
choices of terms and relations …)
* Information technology (hardware) changes constantly, not
worth the effort of getting things right
Linked data to lower the costs of reusing data more than
anything. In addition, government data is used quite widely already, so
we feel there are huge opportunities in promoting this in the Federal
space.
Current Uses / Examples
Systems Engineering Modeling Languages and Ontology Languages
Drive Innovation
Federation and Integration of Systems
Driving Innovation with Open Data - Creating
a Data Ecosystem
1. Gather data
* from many places and give it freely to developers,
scientists, and citizens
2. Connect the community
* in finding solutions to allow collaboration through social
media, events, platforms
3. Provide an infrastructure
4. Encourage technology
developers
* to create apps, maps, and visualizations of data that
empower people’s choices
5. Gather more data
* and connect more people
6. Energy.Data.gov
connects works with challenges across the nation to integrate federal
data and bring government personnel to code-a-thons
7. Data Drives Decisions
* Apps transform data in understandable ways to help people
make decisions
Rapid exploitation of information
1. In this world, the benefit is derived from the rapid pace
at which new data and new data sources can be combined and exploited.
2. High level reasoning over curated information In
this world, the benefit is derived from non-trivial inferences drawn
over highly vetted data.
3. Many times people try to have both expressivity
and scale. This is very expensive
* Don’t be seduced by
expressivity
* Just because you CAN say it doesn’t mean you SHOULD say it.
Stick to things that are strictly useful to building your big data
application.
* Computationally expensive
* Expressivity is not free. It must be paid for either with
load throughput or query latency, or both.
* Not easily partitioned
* Higher expressivity often involves more than one piece of
information from the abox – meaning you have to cross server
boundaries. With lower expressivity you can replicate the ontology
everywhere on the cluster and answer questions LOCALLY.
* A little ontology goes a long way
* There can be a lot of value just getting the data federated
and semantically aligned.
4. Unfortunately it is now so easy to create ontologies that
myriad incompatible ontologies are being created in ad hoc ways
leading to the creation of new, semantic silos
5. The Semantic Web framework as currently conceived and
governed by the W3C (modeled on html) yields minimal standardization
6. The more semantic technology is successful, they
more we fail to achieve our goals
Areas of Use (both current and future) / Areas of non-use
Ontology Design Patterns for Systems Engineering
Ontology for Software Production - Instantiating the ontology
describes design of a particular system
- Decisions considered, rejected, made, changed
- Formal software artifacts
- Source and executable code; specifications;
machine-readable models
- Structured informal artifacts
- Pseudo-code, requirements, graphical models, test
plans, email addressing info, subject
- Unstructured artifacts
- Email body, notes, code comments, etc.
Cyber-Physical Social Data Cloud Infrastructure
NIST & NICT Collaboration Project R&D of a
cloud platform specialized for collecting, archiving, organizing,
manipulating, and sharing very large (big) cyber-physical social data
Use case 1 - Healthcare data publishing & sharing
Use case 2 – Location Aware -based Service (e.g., disaster)
Globally monitoring and locally fencing (safe and rapid
evacuation)
Information and Communication Technology (ict)
- Too much data
- Too much speed
- Too much complexity
Why a Materials Genome Initiative? Materials Are Complicated
Systems Modeling is a Challenge
The Materials Genome Initiative is a new, multi-stakeholder
effort to develop an infrastructure to accelerate advanced materials
discovery and deployment in the United States. Over the last several
decades there has been significant Federal investment in new
experimental processes and techniques for designing advanced materials.
This new focused initiative will better leverage existing Federal
investments through the use of computational capabilities, data
management, and an integrated approach to materials science and
engineering.
* File repository for first principles calculations
** File repository for CALPHAD calculations
** General data repository Prototype repository for data used
in Calphad assessments
* Evaluation of data storage formats (e.g. markup language,
hierarchical data format)
Accessibility (i.e., ease of use) / Impediments
Ontology Quality for Large-Scale Systems
Ontology Tools and Training for Systems Engineers
Recommendations
Some big systems and systems engineering needs and desires of
ontology are:
- Fast integration of data
- Integrated heterogeneous data, linked data, and structured
data
- Easy exploitation of data
- Fine‐grained provenance of federated data.
- An Open, Transparent Platform for Everyone
* More opportunities for social, economic and political
participation
* Open platform for everyone, new public good
* Non-expert system
* Crowd sourcing, citizen science
* Establish new information ecosystem to create new
opportunities, services and jobs
* Benefit from cultural diversity
* Value-sensitive design
The European FuturICT (Information and
Communication Technology) Paradigm is:
- Create a Big Data Commons
- Ethical, value-sensitive, culturally fitting ICT
(responsive + responsible)
- Privacy-respecting data-mining
- Platforms for collective awareness
- Participatory platforms, new opportunities for everyone
- A new information ecosystem
- Coevolution of ICT with society
- Democratic control
- Socio-inspired ICT (socially adaptive, self-organizing,
self-regulating, etc.)
- A 'trustable web'
Big data might benefit from ontology technology but why this
usually fails
- How to do it right
- how create an incremental, evolutionary process, where
what is good survives, and what is bad fails
- create a scenario in which people will find it
profitable to reuse ontologies, terminologies and coding systems which
have been tried and tested
- silo effects will be avoided and results of investment
in Semantic Technology will cumulate effectively
- ontologies should mimic the methodology used by the GO
(following the principles of the OBO Foundry: http://obofoundry.org)
- ontologies in the same field should be developed in
coordinated fashion to ensure that there is exactly one ontology for
each subdomain
- ontologies should be developed incrementally in a way
that builds on successful user testing at every stage
- AmandaVizedom: I can envision a
Grand Challenge like this:
- Create a tool, of the sort that would work with an ontology
repository such as OOR, to support the following activities (make them
relatively easy and make them reliable/repeatable):
- (a) someone with an ontology registers it, and either
adds it to the repository or provides sufficient information for the
tool to access it remotely. The tool provides assistance identifying
key properties of the ontology that are relevant to its suitability for
various types of usage. This assistance includes some manual entry,
some automated validation and metrics generation, and some
semi-automated generation of information.
- (b) Someone looking for ontologies comes to the tool
and gets help finding ontologies that might meet their needs. The tool
assists them in specifying their need, by entering their
ontology-specific requirements to the extent that they know them, and
by describing their aspects of the intended usage. The tool makes this
process also semi-assisted. Key feature of this that makes it a Grand
Challenge: It's not just building a tool; it requires the research and
testing to establish some of the relationships between ontology
characteristics and usage characteristics. It also requires not just
implementation of known evaluation techniques, but also research to
develop others. On the other hand, it need not be complete to be
valuable. Increments of improvement could be high value advancements
over the current state.
Need a science of multi-level complex systems!
Linked Open Data (LOD)
Linked Open Data (LOD) is hard to create
- Linked Open Data is hard to query (Natural language query
systems a research goal)
- Two ongoing UMBC dissertations hope to make it easier
- Varish Mulwad: Generating linked data from tables
(Inferring the Semantics of Tables)
-
- Lushan Han: Querying linked data with a quasi-NL
interface (Intuitive Query System for Linked Data)
** Key idea: Reduce problem complexity by
having (1) User enter a simple graph, and (2) Annotate it words and
phrases
- Both need statistics on large amounts of LOD data and/or
text
- Linked Data is an emerging paradigm for sharing structured
and semi-structured data
- Backed by machine-understandable semantics
- Based on successful Web languages and protocols
- Generating and exploring Linked Data resources can be
challenging
- Schemas are large, too many URIs
