Welcome!Welcome!

Invitational Workshop on Invitational Workshop on Database and Information Database and Information

Systems Research Systems Research For Semantic Web and For Semantic Web and

EnterprisesEnterprises

Amit Sheth & Robert Meersman

Database and Information Systems Research Database and Information Systems Research for Semantic Web and Enterprises:for Semantic Web and Enterprises:

History and RoleHistory and Role

Amit Sheth

History (partial, DB/IS centric)

• Semantic Data ModelingM. Hammer and D. McLeod: "The Semantic Data Model: A Modelling Machanism for Data Base Applications"; Proc.. ACM SIGMOD, 1978.

• Conceptual ModelingMichael 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-geographic- image-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

• 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

Semantic Metadata

led by

Same entity

Human-assisted inference

Semantic Application - 2

Focused relevantcontent

organizedby topic

(semantic categorization)

Automatic ContentAggregationfrom multiple

content providers and feeds

Related relevant content not

explicitly asked for (semantic

associations)

Competitive research inferred

automatically

Automatic 3rd party content

integration

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)

• 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