[DCSB] Martina Trognitz (DAI, Berlin) "EVA: An Expert System for Vases of the Antiquity"

EVAAn Expert System for Vases of the Antiquity

Martina TrognitzDeutsches Archaologisches Institut, Berlin

22 October 2013

M. Trognitz (DAI) EVA 1 / 31

What is EVA?

EVA is an expert system for computer aided classification and dating ofceramics. It represents the application of natural language processingmethods for an archaeological problem.

It was the subject of my master thesis at the University of Heidelberg, inthe department of Computational Linguistics.


Outline

1 Motivation

2 The problem

3 What is an expert system?

How it worksProperties

4 Implementation

Before implementationSystem architectureThe knowledge baseDescription texts

5 Discussion


Motivation

Motivation

Combination of computational linguistics and archaeology

Fill the gap between the number of human experts and amount ofunclassified ceramic

EVA could provide a second opinion and be used as a learning tool


The problem

Outline

1 Motivation

2 The problem



4 Implementation


5 Discussion


The problem

The problem

Ceramic is a common find at excavations.

Form and decoration depend on various factors and change in thecourse of time:

cultural environmentsourcetaste and fashiontechnical achievements

Hence ceramic is used to date archeological find deposits. It serves asa type fossil.


The problem

Problem

An ancient Greek vase is a difficult object for the non-expert to come to

terms with. Faced with rows of apparently undifferentiated black, red and

buff pots, he or she is at a loss as to where to begin.

Tom Rasmussen & Nigel Spivey


The problem

Problem

An ancient Greek vase is a difficult object for the non-expert to come to

terms with. Faced with rows of apparently undifferentiated black, red and

buff pots, he or she is at a loss as to where to begin.

Tom Rasmussen & Nigel Spivey

Solution

Store the knowledge of an expert into an expert system to classify anddate ceramic.


What is an expert system?

Outline

1 Motivation

2 The problem



4 Implementation


5 Discussion




It is a program capable of solvingproblems similarly as human expertswould do.

It uses knowledge and inferencemethods to solve problems.

It can solve complex problemsnormally requiring enourmous humanexpertise.

Edward Feigenbaum“Father of expert systems”



Special subject of artificial intelligence

Early systems were developed in the sixties (DENDRAL)

They are used comercially since the eighties

Can be used in a wide range of subjects (MYCIN, PROSPECTOR,XCON/R1)



Special subject of artificial intelligence

Early systems were developed in the sixties (DENDRAL)

They are used comercially since the eighties

Can be used in a wide range of subjects (MYCIN, PROSPECTOR,XCON/R1)

Remark

An expert system only works well in a well-defined special field, theknowledge domain.


What is an expert system? How it works

How it works

user

expert system

knowledge base

inference engine

facts & informations

expertise

user interface


What is an expert system? How it works

Functionality of the inference engine

inference engine

deductions

facts

working memory

knowledge base

agenda


What is an expert system? Properties

Properties of expert systems

high perfomance, results compete with those of human experts

proper response time

robust

understandable

flexible


What is an expert system? Properties

Properties of expert systems

high perfomance, results compete with those of human experts

proper response time

robust

understandable

flexible

Remark

The knowledge is explicitly disconnected from the processig part of theprogram.


Implementation

Outline

1 Motivation

2 The problem



4 Implementation


5 Discussion


Implementation Before implementation

Before implementation

Size of knowledge domain?

“Ancient vases” is reduced to:attic protogeometric and geometric

Knowledge base?

sort of a rule-based systembased on a decision tree

Inference engine and knowledge base depend on each other

The system is implemented with Python


Implementation System architecture

System architecture

user

expert system

knowledge base

inference engine

questions

desriptions

processing

results

classification

& dating

AB


Implementation The knowledge base

The knowledge base

The knowledge base is the heart of the system

A restricted, well-defined knowledge domain is mapped to aknowledge base

The knowledge representation depends on:

area of applicationscopesource of knowledgefunctionality of the inference engine

A knowledge engineer transfers the knowledge from an expert to theknowledge base of the expert system.



Why a decision tree?

A specific vase can be described with a specific set of characteristics.

EG I:

figured:no; shape:amphora; handlePosition:neck; handleForm:band;body:ovoid; motifs:band of slanting lines

MG II:

figured:no; shape:amphora; handlePosition:neck; handleForm:band;body:ovoid; motifs:hatched meander, zigzag, dogtooth

IF a specific set of characteristics is given THEN you get a specificvase.



Decision tree in EVA

start

figured?

no

shape?

yes

LG!

amphora

handlePostion?

oinochoe

...

...

...

neck

...

shoulder

...

belly

...



tree.py

The decision tree is built in the module tree.py

Each node in the tree consists of a value, a question related to thevalue, a link to the parent node and a list of child nodes.

no

shape?

yes

LG!

amphora

handlePostion?

oinochoe

...

...

...



Knowledge base details

The knowledge base is stored in a separate text file

root - figured? : yes - LG!

root - figured? : no - shape? : amphora - handlePosition? : neck - ...

root - figured? : no - shape? : amphora - handlePosition? : shoulder -

The full question that is displayed to the user is stored indictionaries.py


Implementation Description texts

Description texts

user

expert system

knowledge base

inference engine

questions

descriptions

processing

result

classification

& dating

AB



Description texts

user

expert system

knowledge base

inference engine

questions

descriptions

processing

result

classification

& dating

AB



This is a small oinochoe. The handle isoutlined and has a wavy line. The rim isdecorated with three horizontal lines. Onthe neck a horizontal row of dots, ahorizontal panel with a lozenge chain andanother row of dots can be seen. Theshoulder is decorated with a dotted snakeand some sparse dots. On the belly arelinked dots. All ornaments are interspersedby encircling bands. The lower part of thevase is covered by a thin layer of clay.

CVA Oxford 4 (GB, 24) p.12, plate 30 1-3



This is a small oinochoe. The handle isoutlined and has a wavy line. The rim isdecorated with three horizontal lines. Onthe neck a horizontal row of dots, ahorizontal panel with a lozenge chain andanother row of dots can be seen. Theshoulder is decorated with a dotted snakeand some sparse dots. On the belly arelinked dots. All ornaments are interspersedby encircling bands. The lower part of thevase is covered by a thin layer of clay.

CVA Oxford 4 (GB, 24) p.12, plate 30 1-3



Some aspects of natural language texts

The form and structure of the texts depend on:

personal stylelanguage skillsknowledge of the subject

Some informations may be missing



Information extraction

Question

At which part of the body are the handles attached?




Question


Possible answers

This neck-handled amphora has a thick barred rim.The handles of this amphora are attached to the neck.This is a neck-handled amphora with a thick barred rim.This amphora has a thick barred rim. The handles are on the neck.




Question


Possible answers

This neck-handled amphora has a thick barred rim.The handles of this amphora are attached to the neck.This is a neck-handled amphora with a thick barred rim.This amphora has a thick barred rim. The handles are on the neck.

Possible patterns to look for

... 〈string〉-handled ...

... handles 〈verbal phrase with on/to〉 ... 〈string〉



Processing of texts

1: Parsing

Stanford Parser (Klein – Manning 2003)

2: Go through decision tree

Questions are answered automatically with the given text (information

extraction)It is done by searching for specific patterns



Demonstration of EVA

The main module ist called core.py

core.py builds the decision tree with tree.py and a knowledge basestored in a text file

In dictionaries.py the user friendly questions and answers are stored.

The patterns for information extraction are also stored indictionaries.py



Demonstration of EVA

The main module ist called core.py

core.py builds the decision tree with tree.py and a knowledge basestored in a text file

In dictionaries.py the user friendly questions and answers are stored.

The patterns for information extraction are also stored indictionaries.py

EVA is in an experimental status.


Discussion

Outline

1 Motivation

2 The problem



4 Implementation


5 Discussion


Discussion

Drawbacks

time-consuming

Knowledge base and patterns are handcraftet.

abstract

The program does not consider the actual vase (e.g. looks at images). Itonly relies on textual descriptions.


Discussion

Future work

GUI (with example images)

Use a certainty factor to weigh outcome

Expansion of knowledge base

more regions; earlier and later styles

Additional languages

Build knowledge base by means of machine learning

Include image recognition


Discussion

Discussion

Computer aided classification and dating of ceramics is possible.

What do you think?

[email protected]


[DCSB] Martina Trognitz (DAI, Berlin) "EVA: An Expert System for Vases of the Antiquity"

Education

trognitz daieva7

trognitz daieva10

trognitz daieva13

trognitz daieva16

trognitz daieva3

trognitz daieva5

trognitz daieva8

trognitz daieva14