Semantics For the Semantic Web:

The Implicit, the Formal and The Powerful

Amit Sheth, Cartic Ramakrishnan, Christopher Thomas

CS751 Spring 2005

Presenter: Yihong Ding

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Fact, Syntax, and Semantics Fact: being itself

Syntax: representation of beings

Semantics: meaning of beings

Apple

fruit with red or yellow or green skin and sweet to tart crisp whitish flesh (WordNet)

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Thinking Fact: the one we are interested Syntax

Arbitrary representation Human recognizable (or not?) (or do not care?)

Semantics Ideal: back to fact itself Reality

Ambiguous: a same syntax for multiple facts Uncertain: a syntax for part of (but not full of) the fact

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Implicit Semantics Description

No explicit, machine-processable syntax Loosely defined, less formal structure

Examples Clustered documents (co-occurrence

semantics) Hyperlinked documents (external relating

semantics) Paragraphs within a document (internal

relating semantics) …

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Arguments Machine processability

Possible to process Clustering, concept and rule learning, Hidden

Markov Models, neural networks, … Hard to infer

Knowledge discovery contributions Largely presented Easily and quickly to be extracted (disagree) Helpful to create or enrich formal structured

knowledge representations

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Formal Semantics

Well-formed syntactic structures with definite semantic interpretations

Governed by definite rules of syntax

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Arguments

Features Expressions in a formal language are

interpreted in models. Semantics of an expression is computed

using the semantics of its parts. Positive aspects

Truth-preserving deduction Universal usability

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Description Logics

Core ideas A simplified subset of first-order logics Automated inference for concept

subsumption and instance classification Representation for formal semantics

Basis of OWL (Web Ontology Language)

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Powerful Semantics

Meaning Imprecise Uncertain Partially true Approximate

Reality of web semantics

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Use of Semantics for the Semantic Web

Construction of human knowledge --- knowledge base

Manipulation of human knowledge --- reasoning

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Knowledge Base (on semantics)

Desired features Consistency A full and complete agreement

Real world challenges Be able to deal with inconsistency Compromise on local agreements

instead of global agreements

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Reasoning with Imprecision Real power of human reasoning Up-to-date major approaches

Possibilistic reasoning (Dubois and etc. 1994) Fuzzy reasoning (Zadeh 2002) Fuzzy description logics (Straccia 1998, 2004) Probabilistic inference on OWL within

Bayesian network (Ding and etc. 2004) …

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Logics with Uncertainties (pros)

Fuzzy theory recovers continuity back to the continuous world. Rainbow contains continuous colors, but not

discrete seven colors. Probabilistic inference gives capabilities

of answers ambiguous questions. Bayesian network provides a paradigm

of connecting probabilities to concept maps.

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Logics with Uncertainties (cons)

Requirement of assigning prior probabilities and/or fuzzy membership functions Manual: arbitrary and tedious Automatic: large and representative dataset of

annotated instances Flat vs. hierarchical structures

Machine learning favors flat ones. Prior probabilities for superclasses change when

prior probabilities for subclasses change.

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Projections of Powerful Semantics

Hierarchical composition of knowledge and statistical analysis

Reasoning on available information Formalized in a common language Utilizable by general purpose

reasoners Allowing induction, deduction, and

abduction

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Semantic Web Correlated Research

Information integration

Information extraction/retrieval

Data mining

Analytical applications

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Information Integration Semantic web relevance:

interoperate on heterogeneous sources

Semantics for the semantics web Schema integration

Implicit and/or formal semantics Agent understanding

Entity identification/disambiguation Implicit, formal, semi-formal semantics Semantic annotation

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Information Extraction/Retrieval

Semantic web relevance: gather web information

Semantics for the semantics web Search engines

Implicit, formal, powerful semantics Semantic searching

Question answering systems Implicit and powerful semantics Semantic query

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Data Mining Semantic web relevance: automatic ontology

generation Semantics for the semantics web

Clustering Implicit and/or formal semantics Entity generation

Semi-automatic taxonomy generation Formal semantics Hierarchical relationship generation

Association rule mining Implicit and formal semantics Non-taxonomic relationship generation

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Analytical Applications

Semantic web relevance: all sorts of web services

Semantics for the semantics web Complex relationship discovery

Implicit, formal, and powerful semantics Web service searching

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Conclusion

There are three types of semantics: implicit, formal, and powerful.

Currently view heavily biases on formal semantics.

We need to be aware about all three types of semantics for “semantic web applications”.