Modeling, Discovering, and Exploiting

hurriedtinkleAI and Robotics

Nov 15, 2013 (3 years and 11 months ago)

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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
relationships
-

critical on the Semantic Web

Types of Semantic relationships


1. Using predefined multi
-
ontology


relationship


2. Relevancy ranked indirect relationships


3. User
-
defined relationships

Challenges in finding semantic relationships


1. Each document might describe many


entities


2. Number of relationships in the KB is very


large.





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


-

Inter
-
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
dimensions.

Arity

Cardinality

Direct vs Transitive

Crisp vs Fuzzy

Properties vs Relations

Structural Composition


Computation and Exploitation of
Relationships

Four main computations

Identify

Discover

Validate

Evaluate


SCORE

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
system

An Example Ontology and Knowledge
Base

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


similarities
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 (
ρ)

operator

It is an approach for computing complex semantic
relations.

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
ρ
T
(a,b)[C,K]

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,
INTERSECT, CONNECT and ISO

ρ
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
(HAND)

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,
inter
-
ontological relationships and operations.

InfoQuilt uses Iscapes. Supports user defined
operations.

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
Relationships

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


answer.


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


4. Full/partial translation.





Drawbacks

Semantics of the query may change.

“loss of information”

Can estimate the “loss of information” and set a
threshold.

Conclusion

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
knowledge.