Semantics For the Semantic Web: The Implicit, the Formal and The Powerful Amit Sheth, Cartic Ramakrishnan, Christopher Thomas CS751 Spring 2005 Presenter: Yihong Ding

2 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)

3 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

4 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) …

5 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

6 Formal Semantics Well-formed syntactic structures with definite semantic interpretations Governed by definite rules of syntax

7 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

8 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)

9 Powerful Semantics Meaning Imprecise Uncertain Partially true Approximate Reality of web semantics

10 Use of Semantics for the Semantic Web Construction of human knowledge --- knowledge base Manipulation of human knowledge --- reasoning

11 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

12 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) …

13 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.

14 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.

15 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

16 Semantic Web Correlated Research Information integration Information extraction/retrieval Data mining Analytical applications

17 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

18 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

19 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

20 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

21 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”.