Semantics Modeling and Representation
Wendy Hui WangCS Department
Stevens Institute of [email protected]
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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Consider the following data:
011500 18.66 0 0 62 46.271020111 25.220010
011500 26.93 0 1 63 68.951521001 32.651010
020100 33.95 1 0 65 92.532041101 18.930110
020100 17.38 0 0 67 50.351111100 42.160001
– What do they really mean?
– How to model the meaning of data?
– How to represent the meaning of data?
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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Outline
• Semantics modeling
• Semantics representation
• SMSN: Semantics‐based mobile social networks
• Brief discussion of Semantics + SDR
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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In Database Context
• Semantic modeling is fundamental in database design
• Goal of semantics modeling: accurately model data relationships
• Evolution of semantic models– Early years: representation of structural aspects of (static) business data
– Recent years: incorporating the behavioral (dynamic) aspects of data
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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Philosophical Roots of Semantic Modeling
• Semantic models should– Provide a higher level of abstraction for modeling data, and
– Allow database designers to think of data in ways that correlate more directly to how data arise in the world.
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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Components of Semantic Modeling
• Explicit representation of objects, and relations between objects
• Type constructors for building complex types
• ISA relationships
• Derived schema components
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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World Traveler Example
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objects
objects
Type constructors for building complex types
ISARelation
Derived Schema Component
Semantic Models
• Entity‐relation model (ER)
• Functional data model (FDM)
• Semantic data model (SDM)
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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Entity‐relation (ER) Model
• Proposed in 1976.
• One of the first true semantic data models in the literature.
• Still commonly used in database design.
• A graph‐based representation of – abstract sets of entities,
– relationships between these entity sets, and
– attributes defined from both entity and relationship sets
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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More Discussions of ER Model
• Restriction of the use of attributes and aggregation– Attributes must be single valued
– Multi‐valued attributes require the use of a relationship.
• ISA relationships are not represented.
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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Functional Data Model (FDM)
• Proposed in 1976
• the first semantic model centered around functional relationships, that is, attributes.
• FDM connects objects directly with attributes without the use of intermediate constructs such as aggregation and grouping.
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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FDM V.S. ER
• Attributes in FDM can be – either single‐ or multivalued
– defined on domains that are Cartesian products of the atomic entity sets.
• FDM supports ISA
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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Semantic Data Model (SDM)
• Proposed in 1981
• The first model to emphasize – grouping constructor, and
– the support of derived schema components.
• Derived schema components permit data relativism, that is, multiple perspectives on the same underlying data set.
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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Uniqueness of SDM
• It provides a rich set of primitives for specifying derived attributes and subtypes.
• Subtype relationships in SDM are broken into 4 categories: – Those that are defined by attributes
– Those that are defined by set operations (e.g., intersection) on existing types
– Those that serve as the range of some attribute,
– Those that are user specifiedWendy Hui Wang, SDR Semantics Study
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Outline
• Semantics modeling
• Semantics representation
• SMSN: Semantics‐based mobile social networks
• Brief discussion of Semantics + SDR
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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Semantics Representation 1: Schema
NAME LENGTH FORMAT LABELinstudy 6 MMDDYY Date of randomization into studybmi 8 Num Body Mass Index.obesity 3 0=No 1=Yes Obesity (30.0 <= BMI)ovrwt 8 0=No 1=Yes Overweight (25 <= BMI < 30)Height 3 Num Height (inches)Wtkgs 8 Num Weight (kilograms)Weight 3 Num Weight (pounds)
• The semantics, i.e. the explanation of what data means, is called metadata or “data about data.”
• Metadata can be represented by schema.
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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Semantics Representation 2: XML
• Semantic markup – the eXtensible Markup Language (XML)
• XML supports self‐defined tags, which are used to describe the meaning of the data.
• XML represents the semantics as a layer of machine‐understandable specification built on top of common syntax.
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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<bioml><organism name="Homo sapiens (human)"><chromosome name="Chromosome 11"
number="11"><gene name="Insulin gene">
<dna name="Complete HUMINS sequence“ >agagcaccca acaccctcca ...
</dna>...
</gene></chromosome>
</organism></bioml>
An Example of XML
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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Components in XML Semantics
• XML Semantics include– The interpretation of element type names, attribute names, and, in some cases, content terms
– The processing rules (also known as business rules) for conducting transactions with valid documents
– The relationship between structured elements of one document and those of another
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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Beyond XML
• To describe the meaning of data on WWW, W3C recommend– Resource Description Framework (RDF)
– Web Ontology Language (OWL)
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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The Resource Description Framework (RDF)
• RDF is a language for representing information about resources in Web.
• RDF is represented in XML, which provides machine understandable semantics.
• It provides– better precision in resource discovery than
full text search, – assisting applications as schemas evolve, – interoperability of metadata.
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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RDF Triples• The semantics of RDF is determined by the set of triples <subject, predicate, object> that are explicitly asserted or inferred.
subject
objectpredicate
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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Web Ontology Language (OWL)• OWL is based on RDF .• OWL adds many new features to RDF:
– Functional properties– Inverse functional properties (database keys)– Local domain and range constraints– General cardinality constraints– Symmetric and transitive properties
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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Outline
• Semantics modeling
• Semantics representation
• SMSN: Semantics‐based mobile social networks
• Brief discussion of Semantics + SDR
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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Social Mobile Computing
• Integration of social networks with mobile computing
• Static case: use mobile devices to access the pre‐built social networks
• Dynamic case: construct social networks from mobile devices– community‐oriented, self‐organizing, self‐adaptive
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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Necessity for Semantics• Mobile devices store large amounts of personal data– Address book, text messages, call logs, photos…
• Personal data can be summarized to user profiles (describing interests, behavior, etc.)
• Goal: group users into social communities by their personal profiles
• Challenge: simple exact match of keywords in user profiles misses important semantics – Same concern holds for resources in networksWendy Hui Wang, SDR Semantics Study
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My Current Work
• SMSN: Semantics‐based mobile social network computing
• Three components– Ontology‐based user profile representation
– Semantics‐based user profile matching
– Semantics‐driven routing
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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Ontology‐based User Profiles (1/2)
• Ontology:– Uses RDF and OWL
– The terminological box (T‐box): defines the common understanding for all the important concepts and their relationships.
• We adapt Friend‐of‐a‐Friend (FOAF) vocabulary
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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Ontology‐based User Profiles (2/2)
• User profiles • Annotated instances of the reference T‐Box ontology
<Person rdf:ID="Person_1">
<friendOf>
<Person rdf:ID="Person_2">
<eyeColorrdf:datatype=
“http://www.w3.org/2001/XMLSchema#string">Black</eyeColor>
<weight rdf:datatype="http://www.w3.org/2001/XMLSchema#float">55.0</weight>
<friendOf rdf:resource="#Person_1"/>
…
</Person>
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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Semantics‐based User Profile Matching
• Two phases– Phase 1: match user profiles at “general concept” level (T‐box level)
– Phase 2: match user profiles at “detailed concept” level (keyword level)
• Similarity metrics– “Relative” distance of two concepts in the ontology
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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Semantics‐driven Routing
• Nodes distribute semantic summary of user/resource profiles within a certain range to their network neighborhood
• A node can make routing decisions by knowing only its immediate neighbors and limited resource information.
• A leap mechanism is used to expedite the searching process by skipping over the “barren” areas, i.e., the areas consisting of few resources/profiles.
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Discussion: SDR + Semantics
• The challenges– Identify the real meaning of semantics
• In database applications, semantics means metadata
• In Web, semantics means the definition of Web resources
• In social networks, semantics means the description of users and their social behaviors
• What does semantics mean in SDR?
Wendy Hui Wang, SDR Semantics Study April 29 , 2009
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Discussion: SDR + Semantics
• The challenges (continue)• Identify the real meanings of semantics
• Design the semantic models– Can any existing semantic model (ER, FDM, SDM, and
others) be applied to SDR?
• Represent the semantics– Can any existing semantic representation (schema,
XML, RDF, OWL, and others) be applied to SDR?
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