Knowledge Discovery Understanding

Olena Parkhomenko,

Arun Santharam &

Debashis Haldar

Knowledge Discovery Understanding

• Last week we have got some keywords:– AIML Interpreter– Knowledge Assistant– Knowledge Mediator– Semantic WEB– %#||;*&^*&#%$#@ etc. etc.

• We started finding related materials

Knowledge Discovery Understanding

• We talked with other teams & searched in www.google.com and get to know– People use some user agent to interact with machine.

– Machine has AIML interpreter to extract the human input in a standard format, called AIML. It may use the profile, preference, context relative ontology etc. to get better result.

– Now comes knowledge assistant, to help finding out the real meaning of the input.

– Another entity, called Knowledge Mediator, is also there to find the process of retrieving data from “somewhere”.

– This “somewhere” happens to be the big BUZZWORD “Semantic WEB”.

Knowledge Discovery Understanding

• We also get to know that there are three processes in Knowledge Discovery:– Process 1 comprises simply the application of a

decision tree inducer.– Process 2 preprocesses the data by applying

discretization of numeric attributes, and then builds a naïve Bayesian classifier.

– Process 3 processes the data first by taking a random sub-sample, then apply discretization, & then build a naïve Bayesian classifier.

Knowledge Discovery Understanding

• One of the goal of Knowledge Assistant is to find the right process for the user, context and purpose. This job is done according to the constraints implied by the user inputs, the data, and/or ontology.

• Here are some more terms:– Classification, categorization.

– Discretization.

– Ranking of processes

– Dual scaling (???)

Knowledge Discovery Understanding• The next part is to deal with Semantic WEB

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– Motivation:• It has become critical in many domains to be able to retrieve on-line

services that provide useful behavior as opposed to getting just static documents w/useful information

• Existing service retrieval technology is deficient in satisfying this necessity.

• Currently used keyword-based approaches can achieve high recall (retrieve all related docs), but low precision (retrieve only the right items, no false positives).

• Problem - keywords are a poor way to capture the semantics of a query or item.

• Current service retrieval approaches have serious limitations: either perform poorly, or make unrealistic demands of those who wish to index or retrieve services (ex. frame-based approach, deductive retrieval).

Knowledge Discovery Understanding– Challenge:

• capture enough service and query semantics to increase precision without reducing recall or making it too difficult to express these semantics.

– Proposed Solution: Sofisticated use of process ontologies Indexing a service in the proposed approach comes down to placing

the associated process model and all of its components (attributes, ports, dependancies, subtasks and exceptions) in the appropriate place in the ontology.

Authors define Process Query Language (queries built of entity/relationship clauses). Allow for possibility to express query w/natual language.

Knowledge Discovery Understanding• Some tools and languages:• There is a need for languages like DAML+OIL that support logic

inferences and flexible and precise knowledge representation and retrieval.

• 3 main scenarios that involve semantically marked up web pages and text documents:

– Information Retrieval– Simple question answering (Q &A) (like "Who is the President of USA?")– Complex Question answering (like "What is the situation in Iraq?")

• Query Answering (QA) Systems– START is one of the QA systems with web interface that answers

questions on geography– MULDER claims to be first automated question-answering system that

uses the full web as knowledge base.

Knowledge Discovery Understanding• DAML Queries

– DQL - DAML + OIL Query language - used for querying DAML +OIL knowledge bases.

• References to related work mentioned in the paper like Web KB, Quest, OHTML, ELIXIR, etc.

• Design and Implementation of OWLIR– OWLIR defines technologies encoded in DAML+OIL allowing users to

specify their interests in different events.

– Use engine called HAIRCUT for information retrieval.

– Description on OWLIR process flow

Knowledge Discovery Understanding• Bibliography:

– http://www.cs.umd.edu/projects/plus/SHOE/pubs/aaai-paper.html

– http://www.ifi.unizh.ch/ddis/staff/bernstein/publ/SWWSFinal.pdf

– http://www.ifi.unizh.ch/ddis/staff/bernstein/publ/Bernstein_ESSW_pub.pdf

– http://www.ifi.unizh.ch/ddis/staff/bernstein/publ/IDEA_CeDR_0202.pdf

– http://www.ifi.unizh.ch/ddis/staff/bernstein/publ/idea.pdf