Modeling, Discovering, and Exploiting

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15 Νοε 2013 (πριν από 4 χρόνια και 7 μήνες)

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Modeling, Discovering, and Exploiting
Complex Semantic Relationships

Amit Sheth, I.Budak Arpinar and Vipul Kashyap

Identification, discovery, validation and utilization of

critical on the Semantic Web

Types of Semantic relationships

1. Using predefined multi


2. Relevancy ranked indirect relationships

3. User
defined relationships

Challenges in finding semantic relationships

1. Each document might describe many


2. Number of relationships in the KB is very


Taxonomy of Relationships based on
Information Content

Content Independent Relationships

Content Dependent Relationships

1. Direct Content Dependent Relationships

2. Content Descriptive Relationships


Direct Semantic Relationships


Complex Transitive Relationships


domain Multi
ontology Relationships


Semantic Proximity Relationships

Representation of Relationships

A fundamental representation between two concepts is
a mathematical structure denoting it as a mapping
between the instances belonging to the two concepts.

These mappings can be characterized along following



Direct vs Transitive

Crisp vs Fuzzy

Properties vs Relations

Structural Composition

Computation and Exploitation of

Four main computations






Semantic Content Organization
and Retrieval Engine

Ontology with Definitional Component and
Assertional Component.

Using relevant ontology, domain specific
metadata can be extracted from a document,
thus enhancing its meaning.

Semantic Document Enhancement in SCORE

An Example Ontology and Knowledge

Ways to improve Efficiency of the
Semantic Association Discovery

Assigning more weights to certain entities

Specification of Relevant Context

Ranking relations

Knowledge Base (KB)

Contains “Entities”(name and a classication
type) and “Relations”(name and a vector of
classification types)

Entity Classification Hierarchy

among the entity classification

Relationship Hierarchy

similarities among
existing relationships

Types of Semantic Queries

Keyword Queries

Entity Queries

Relationships Queries

Path Queries

Path Discovery Queries

Semantic Index (SI)

Constitutes a foundation for the design
of a suitable semantic query engine.

Rho (


It is an approach for computing complex semantic

Intended to facilitate complex path navigation of
metadata as well as schema/taxonomies in KBs.

Specifically it provides the mechanism for reasoning
about semantic associations that exist in KBs.

Binary form of the operator is

where C= context given by user

K = constraints that includes user associations

to a specific region in the KB.

There are 4 types of the ρ operator: PATH,

(a,b): Given the entities a and b, looks
for directed paths from a to b and returns a
subset of possible paths.

Human Assisted Knowledge Discovery

Users are able to pose questions that involve
exploring complex hypothetical relationships amongst
concepts within and across domains, in order to gain
a better understanding of their domains of study, and
the interactions between them.

Could include complex information requests involving
user defined functions and fuzzy or approximate
match of objects thus requiring richer environment of
expressiveness and computation.

Eg: Does Nuclear Testing cause Earthquakes?

Correlation of data from different domains like Natural
Disasters, Nuclear Testing

Meaning of “cause” should be clearly understood.

Refining relations and posing other questions based
on the results presented may lead to better
understanding of the nature of interactions between
two events.

Information Scapes (Iscapes)

“A computing paradigm that allows users to query
and analyze the data available from diverse
autonomous sources, gain better understanding of
the domains and their interactions as well as discover
and study relationships.”

An Iscape is defined in terms of relevant ontologies,
ontological relationships and operations.

InfoQuilt uses Iscapes. Supports user defined

Eg: Find all earthquakes with epicenter in a 5000
miles radius area of the locations at latitude 60.7
North and longitude 97.5 East.

Evaluations Involving Semantic

A user query formulated using terms in a domain ontology is
translated by using terms of other domain ontologies.

Substitution of terms by traversing inter
ontological relationships
like synonyms, hypernyms or hyponyms.

When a query is posed :

1. The user browses the available ontologies and chooses a

user ontology that includes terms needed to express the

semantics of the query.

2. If the user is not satisfied with the answer, the system

retrieves more data from other ontologies to enrich the


3. In doing so a ‘target ontology’ is created.

4. Full/partial translation.


Semantics of the query may change.

“loss of information”

Can estimate the “loss of information” and set a


Ontologies provide the semantic underpinning, while
relationships are the backbone for semantics in the
Semantic Web.

Attention needs to shift from searching relevant
documents to an approach of exploiting data with