Transcript DB/IS Research for Semantic Web & Enterprises: History

Welcome!
Invitational Workshop on
Database and Information
Systems Research
For Semantic Web and Enterprises
Amit Sheth & Robert Meersman
Database and Information Systems Research
for Semantic Web and Enterprises:
History and Role
Amit Sheth
History
(partial, DB/IS centric)
• Semantic Data Modeling
M. Hammer and D. McLeod: "The Semantic Data Model: A Modelling Machanism for Data Base
Applications"; Proc.. ACM SIGMOD, 1978.
• Conceptual Modeling
Michael Brodie, John Mylopoulos, and Joachim W. Schmidt. On Conceptual Modeling. Springer Verlag, New
York, NY, 1984.
• So Far (Schematically) yet So Near (Semantically)
• Data Semantic: What, Where and How?
Meersman, Navathe, Rosenthal, Sheth,
• Semantic Interoperability many projects in 90s
• Domain Modeling, Metadata, Context, Ontologies, Semantic
Information Brokering, Agents, Spatio-temporal-geographicimage-video-multimodal semantics
• Most of the above before “Semantic Web” term is coined
Context/Driver for this workshop
• Series of Workshops and upcoming conferences:
Lisbon (9/00), Hong Kong (5/01), Palo Alto (7/01),
Amsterdam (12/01); upcoming: WWW2002/ISWC
– Observation: visible lack of DB/IS involvement
• “Semantic Web – The Road Ahead,”
[Decker, Hans-Georg Stork, Sheth, … SemWeb’2001 at WWW10,
Hongkong, May 1, 2001. ]
• Semantic Web: Rehash or Research Goldmine
[Fensel, Mylopoulous, Meersman, Sheth, CooPIS’01]
• At a restaurant in a castle in Italy
Modeling
• Semantic Web: Programs (agents) that
understand meaning of data; Ontologies
pivotal role to supply meaning to syntax;
programs can do intelligent computation
• But technologically is ontology different from
Semantic Models and O-O model?
• Is primary difference in social phenomena–
ontological commitment, agreements
between humans that the programs should
abide by?
Ontology is Nirvana – is it?
• Need for multiple ontologies– even in
the same domains– is well accepted
• Ontology mismatch vs Schematic
Heterogeneity; Matchmaking vs Schema
Integration
• Again, are the core “semantic” problems
candidate for social processes?
Computing
–
many types of information, decision making needs
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IR
Query processing
Inferencing
Finding Patterns/Mining
Spatial-temporal-lexical-language similarity
Semantic Proximity
Semantic Associations (lateral relationships)
Hypothesis testing/knowledge discovery
What do different research communities have to
offer?
Semantic Application - 1
Syntax Metadata
Same
entity
led by
Semantic Metadata
Humanassisted
inference
Semantic Application - 2
Automatic
3rd party
content
integration
Focused
relevant
content
organized
by topic
(semantic
categorization)
Related relevant
content not
explicitly asked for
(semantic
associations)
Competitive
research
inferred
automatically
Automatic Content
Aggregation
from multiple
content providers
and feeds
Opportunities for Smarter Apps &
Human Interactions
• Combined access/use/processing of
ontologies, knowledge base, metadata,
content
– Automatic Content Enhancement
– Intelligent Information Correlation
• Integrated “Semantic” Browsing,
Searching, Hypothesis Testing and
Analysis
Killer Apps, Synergies, etc.
• Do Industry/Business vs
Research/Academic views and
approaches match?
• Semantic Enterprise vs Semantic Web
• When do we know Semantic Web has
arrived?
Some Perceptions
(verbatim comments from some NSF reviews)
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As a constituent technology, ontology work of this sort is defensible. As the basis for
programmatic research and implementation, it is a speculative and immature
technology of uncertain promise.
The proposed research is highly speculative. The ideal of a global information space
that is structured according to dynamic ontologies is appealing, if improbable.
proposal the statement is made: '…users will be able to use programs that can
understand semantics of the data to help them answer complex questions like "Find a
correlation between earthquakes and nuclear tests."' This sort of hyperbole is
characteristic of much of the genre of semantic web conjectures, papers, and
proposals thus far. It is reminiscent of the AI hype of a decade ago and practical
systems based on these ideas are no more in evidence now than they were then.
Such research is fashionable at the moment, due in part to support from defense
agencies, in part because the Web offers the first distributed environment that makes
even the dream seem tractable.
Google has shown that huge improvements in search technology can be made
without understanding semantics. Perhaps after a certain point, semantics are needed
for further improvements, but a better argument is needed.
Process and Outcome
• Day 1: 15 minute presentations
• Day 2: Group Discussions
• Day 3: Consensus Building,
Summarization
• Post workshop: Reporting/Socialization