On Beyond OWL: challenges for ontologies on the Web

Tetherless World Constellation
On beyond OWL
Jim Hendler
@jahendler
Tetherless World Professor of Computer, Web and Cognitive Science
Director, Rensselaer Institute for Data Exploration and Applications
Rensselaer Polytechnic Institute

Semantic Web and its contribution
Slide ca. 2001. Were we right?

Were we right?
Sort of? Mostly?

Example: Semantic Search
IEEE Computer, Jan 2010)

Contenders ca. 2010

Contenders ca. 2014
Google Sem Webbers include: R. Guha, Dan Brickley, Denny Vrandečić
Natasha Noy, Chris Welty, …

Google 2014
Google finds embedded metadata on >20% of its crawl – Guha, 2014

Other success stories
Facebook: 2011 Oracle: 2012

Some others

Watson used Semantic Web
IBM

All cool, but…
• Which of these use OWL in any
significant way?
(This part of the slide intentionally left blank)

Why not OWL?
• This is NOT to say OWL isn’t being used
– but it’s not much by comparison
– but it’s not much on the Web
• with the possible exception of the misuse of
owl:sameAs, but let’s not go there…
• Semantic markup on the Web exceeds
anything we predicted in 2001…
– … but OWL use on the Web as a proportion of
this lags behind the expectations many of us
had
• The rest of this talk is speculation as to why…

What I used to think the problem is

Ontology: the OWL DL view
• Ontology as Barad-
Dur (Sauron's
tower):
– Extremely powerful!
– Patrolled by Orcs
• Let one little hobbit
in, and the whole
thing could come
crashing down
inconsistency
Decidable Logic basis

ontology: the RDFS view
• ontology and the
tower of Babel
– We will build a tower
to reach the sky
– We only need a little
ontological
agreement
• Who cares if we all
speak different
languages?
Genesis 11:7 Let us go down, and there confound their
language, that they may not understand one another's speech.
So the Lord scattered them abroad from thence upon the face
of all the earth: and they left off to build the city.

ROI: Reasoning over
(Enterprise) data
• This "big O" Ontology finds use cases in
verticals and enterprises
– Where the vocabulary can be controlled
– Where finding things in the data is important
• Example
– Drug discovery from data
• Model the molecule (site, chemical properties, etc) as
faithfully and expressively as possible
• Use "Realization" to categorize data assets against the
ontology
– Bad or missed answers are money down the drain
BUT VERY EXPENSIVE

ROI: Web 3.0
• The "small o" ontology finds use cases in
Web Applications (at Web scales)
– A lot of data, a little semantics
– Finding anything in the mess can be a win!
• Example
– Declare simple inferable relationships and apply,
at scale, to large, heterogeneous data collections
• These are "heuristics" not every answer must be
right (qua Google)
– But remember time = money!
WHAT I USED TO BELIEVE

ROI: Web 3.0
• The "small o" ontology finds use cases in
Web Applications (at Web scales)
– A lot of data, a little semantics
– Finding anything in the mess can be a win!
• Example
– Declare simple inferable relationships and apply,
at scale, to large, heterogeneous data collections
• eg. Use InverseFunctional triangulation to find the
entities that can be inferred to be the same
– These are "heuristics" not every answer must be right
(qua Google)
– But remember time = money!
WHAT I NOW BELIEVE

BUT, not quite the same way
• That explains why the uptake of
RDFS/SPARQL is going on
– and used by things like schema.org
• But still doesn’t explain why OWL
isn’t used as much as it should be
– especially on the Web
– especially when the need for ontologies
is growing rapidly and many kinds of
“ontology like things” are being used
heavily

What I am coming to believe
• My previous view was blaming OWL’s
problems on too much expressivity
– as opposed to RDFS (or really RDFS+)
• I now believe the problem is lack of
expressivity (in an interoperable
way)
– for tasks where people really need Web
semantics

The Web is increasingly about LINKING DATA
http://linkeddata.org/ cloud is a tiny fraction of what is out there

Current linked data approaches miss a key need
• Data is converted into RDF and SPARQL’ed
– creates huge graph DBs less efficient than the
original DB
• Data is converted from DB into SPARQL
return on demand
– much better, but you must know the mapping
• owl:sameAs is (ab)used to map data to
data
– but that only lets you map equals – which is an
easy mapping to express in many ways
• defining equality right in a model theory is much
harder, and thus the abuse, but let’s leave that for
another talk

DataData
MetaData
What we need
Linked Semantic Web “metadata” documents that can be used to link very large databases in
distributed data systems. This could lead to orders of magnitude reduction in information flow
for large-scale distributed data problems.

What’s the problem
• We want a knowledge representation
that can do things like:
– help us find the right data for a problem
• The “Date” field in some DB could be lots of
different things
– consider “Database of 1957 NYC births” vs
“Database of 1957 NYC deaths”
– help us map between different
databases
• The problem isn’t primarily translating
ontologies to each other, it is tying the
ontologies to the data (for the mappings)

Example: Mereology (and meronymy)
• OWL doesn’t have a part-whole relation
– left out of design because we couldn’t reach
consensus (and there’s 2000 yrs of argument behind
that)
• but also because most require transitive closure of
parts in many cases and that had complexity
issues
– but one of the most
used relations in
the gene ontology
and many medical
ontologies is
part of

But, what? Wait!
Note in the above – we use the fact that the brain has
5 lobes as a driving example for qualified cardinality
- but to say that in OWL you need to INVENT the
“hasdirectpart” relation

Database mapping
• Many things in database schemas
also map to parts/wholes
– Who is in what organization?
– What components comprise an
assembly?
– Where did something occur (that was
part of another event)?
– When…

When?
• Temporal reasoning is missing from
OWL
– Knowing [this talk occurred during
“OWLED 2015” AND “OWLED 2015”
occurred in October of 2015,
THEREFORE this talk occurred in
October of 2015] would be huge
• Whole books of temporal logics out there
– picking is hard
– but it is also what OWL has to do to be a standard
for this kind of mapping

Geophysical reasoning
• Add math to part-wholes
– OY!
– but GDBs are widely used for mappings
of information in big databases
• especially in science (more OWL use cases)
• Talking about “math”
– lots of discussion of adding probability
to OWL
• and someone should some day
– but what about

SIMPLE relationship reasoning
• Discovery Informatics and data mining
– Huge industries, and growing
– Web data, Internet of things, data
interoperability (the startup holy grails)
• Example: supposing some scientific theory
“implies” that if X increases then Y should
also increase
– Which databases would help confirm my
theory? Which would argue against it?
– Easy to check in many new database systems
– How do we express that aspect of the theory in
OWL?

procedural attachment
• In fact, what about procedural
attachment?
– lots of literature on preserving
completeness w/respect to procedures
• used in prolog, etc.
– Why isn’t this in OWL?
• patent issues w/respect to standard
• general concern about whether procedural
attachment was possible in OWL DL
framework

Procedural Attachment
No longer a patent issue …

33
Exploring knowledge graphs
• Agent reasons about a network of connected entities and chooses the
best next one to discuss based on a combination of factors
– Consistency, Novelty and Ontological relationships

34
Integrating
• Automate Agent Creation combining:
Information Extraction: Using anew
"living information extraction" technique, we
will be able to create a "never-ending
extractor" which will be pulling from web
documents information about entities and
events, and the relationships between
them.
The new system can work in a dynamic
node, and does not need human annotated
samples for training, but it works best if
there are a number of known relationships
between pages to build off of.
info extraction uses ontologies

35
Integrate
them.
Semantic Web: The Semantic Web
provides a number of known relationships
between pages on the Web in a number of
domains. Using general knowledge
sources, like dbpedia and Yago, and
specialized knowledge sources, like the
data from musicbrainz, the reviews from
Yelp (which have semantic annotations)
and even the Open Graph of Facebook
(which is available in a semantic web
format), provides a jumpstart for the
language extraction.
However, the Semantic Web relates pages,
but doesn't have any sort of
"understanding" of what is on the pages.
ontologies
again!

36
Our Approach
them.
Cognitive Computing : Cognitive
Computing, can allow us to have a better
way of accessing information about the
entities found on the Web and finding other
information about the same entities using
various kinds of search and language
heuristics. This allows us to have more
organized information, rapidly generated,
about the entities being explored.
However, given a large graph of entities
(even the organized linked-open data cloud
has information about billions of things),
how do we choose what to display next? If
the best we can do is provide links, all of
the above isn't much better than choosing
a page and clicking from there.
and here!

37
Our Approach
them.
Cognitive Story-telling Technology: Interactive
storytelling techniques are being explored
to take information in the kind of
"knowledge graph" resulting from the
above, and tailoring the presentation to a
user using storytelling techniques. It is
aimed at presenting the information as an
interesting and meaningful story by taking
into consideration a combination of factors
ranging from topic consistency and novelty,
to learned user interests and even a user’s
emotional reactions. The system can
essentially determine "where to go next"
and what to do there in the organized
information as processed above..
“User models” (which are a lot like ontologies)

I could go on
• There are many other such examples
– Describing how data in one set could be joined
to data in another is incredibly powerful,
timely, and important
• it’s just not really what people have typically used
traditional reasoners for
– Providing the ontological glue among different
AI technologies: Priceless
• Why aren’t we doing more to understand
these issues and bring into the OWL
“family?”
– de facto standardization leads to adoption

My challenge
• Is the problem that we cannot have
these powerful things in a decidable
(sound and complete) language?
– then maybe we have to give up
decidability?
• or even better, define new maths of
expressivity that have different kinds of
“sound and complete” behavior
– i.e. approximately sound and complete
» (is modern computational theory weakened by
“within epsilon” optimality?)
– i.e. “anytime” algorithms for reasoners
» (conjecture) sound and complete at infinity

Challenge
• We may need to rethink how we are
defining ontologies with respect to
the necessary properties for use
on the
Web

I’m not “anti-formalism”
• A sufficient formalism for Semantic Web
applications must
– Provide a model that accounts for linked data
• What is the equivalent of a DB calculus?
– Provide a means for evaluating different kinds
of completeness for reasoners
• In practice we must be able to model A-box effects
as formally as T-box technologies
– example Weaver 2012 showed what restrictions were needed
to maximize parallelism of some OWL subsets
– Think about other processors than formal
reasoners that will use the ontologies
• ontologies used in many other ways
– i.e. why does an IE system w/F1=.84 need a DL reasoner?

Summary
• The growing world of (semantic web,
linked) data needs ontologies more than
ever
– OWL has some of the important things
– But is missing many of the really important
things
• The problem isn’t (formal) expressivity
– it’s the need to express other things (esp
relationships between properties, events, etc.)
– We need more research into how to formalize
these kinds of relations

Acknowledgments
• I get funded by lots of folks – this talk may or
may not represent anything anyone there
believes:
– ARL, DARPA, Microsoft, Mitre, Optum Laboratories
• The Rensselaer Institute for Data Exploration and
Application pays my salary
– Thanks!!
• Many of my best ideas, including a lot in this talk
come from listening to smart people
– Frank van Harmelen and Peter Mika have influenced my
thinking about many of the ideas in this talk
• The students and my colleagues at the RPI
Tetherless World Constellation (tw.rpi.edu)

ANY QUESTIONS?

On Beyond OWL: challenges for ontologies on the Web

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On Beyond OWL: challenges for ontologies on the Web