Semantics For the Semantic Web: The Implicit, the Formal and The Powerful Amit Sheth, Cartic...
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Transcript of Semantics For the Semantic Web: The Implicit, the Formal and The Powerful Amit Sheth, Cartic...
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”.