EVA An Expert System for Vases of the Antiquity Martina Trognitz - - PowerPoint PPT Presentation

EVA

An Expert System for Vases of the Antiquity Martina Trognitz

Deutsches Arch¨ aologisches Institut, Berlin 22 October 2013

• M. Trognitz (DAI)

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What is EVA?

EVA is an expert system for computer aided classification and dating of

• ceramics. It represents the application of natural language processing

methods for an archaeological problem. It was the subject of my master thesis at the University of Heidelberg, in the department of Computational Linguistics.

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Outline

1 Motivation 2 The problem 3 What is an expert system?

How it works Properties

4 Implementation

Before implementation System architecture The knowledge base Description texts

5 Discussion

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Motivation

Motivation

Combination of computational linguistics and archaeology Fill the gap between the number of human experts and amount of unclassified ceramic EVA could provide a second opinion and be used as a learning tool

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The problem

Outline

1 Motivation 2 The problem 3 What is an expert system?

How it works Properties

4 Implementation

Before implementation System architecture The knowledge base Description texts

5 Discussion

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The problem

The problem

Ceramic is a common find at excavations. Form and decoration depend on various factors and change in the course of time:

cultural environment source taste and fashion technical achievements

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

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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

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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 and date ceramic.

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What is an expert system?

Outline

1 Motivation 2 The problem 3 What is an expert system?

How it works Properties

4 Implementation

Before implementation System architecture The knowledge base Description texts

5 Discussion

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What is an expert system?

What is an expert system?

It is a program capable of solving problems similarly as human experts would do. It uses knowledge and inference methods to solve problems. It can solve complex problems normally requiring enourmous human expertise.

Edward Feigenbaum “Father of expert systems”

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What is an expert system?

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)

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What is an expert system?

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, the knowledge domain.

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What is an expert system? How it works

How it works

user expert system knowledge base inference engine facts & informations expertise user interface

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What is an expert system? How it works

Functionality of the inference engine

inference engine deductions facts working memory knowledge base agenda

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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

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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 the program.

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Implementation

Outline

1 Motivation 2 The problem 3 What is an expert system?

How it works Properties

4 Implementation

Before implementation System architecture The knowledge base Description texts

5 Discussion

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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 system based on a decision tree

Inference engine and knowledge base depend on each other The system is implemented with Python

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Implementation System architecture

System architecture

user expert system knowledge base inference engine questions desriptions processing results classification & dating A B

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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 a knowledge base The knowledge representation depends on:

area of application scope source of knowledge functionality of the inference engine

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

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Implementation The knowledge base

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 specific vase.

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Implementation The knowledge base

Decision tree in EVA

start figured? no shape? yes LG! amphora handlePostion?

• inochoe

... ... ... neck ... shoulder ... belly ...

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Implementation The knowledge base

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 the value, a link to the parent node and a list of child nodes. no shape? yes LG! amphora handlePostion?

• inochoe

... ... ...

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Implementation The knowledge base

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 in dictionaries.py

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Implementation Description texts

Description texts

user expert system knowledge base inference engine questions descriptions processing result classification & dating A B

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Implementation Description texts

Description texts

user expert system knowledge base inference engine questions descriptions processing result classification & dating A B

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Implementation Description texts

This is a small oinochoe. The handle is

• utlined and has a wavy line. The rim is

decorated with three horizontal lines. On the neck a horizontal row of dots, a horizontal panel with a lozenge chain and another row of dots can be seen. The shoulder is decorated with a dotted snake and some sparse dots. On the belly are linked dots. All ornaments are interspersed by encircling bands. The lower part of the vase is covered by a thin layer of clay.

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

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Implementation Description texts

This is a small oinochoe. The handle is

• utlined and has a wavy line. The rim is

decorated with three horizontal lines. On the neck a horizontal row of dots, a horizontal panel with a lozenge chain and another row of dots can be seen. The shoulder is decorated with a dotted snake and some sparse dots. On the belly are linked dots. All ornaments are interspersed by encircling bands. The lower part of the vase is covered by a thin layer of clay.

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

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Implementation Description texts

Some aspects of natural language texts

The form and structure of the texts depend on:

personal style language skills knowledge of the subject

Some informations may be missing

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Implementation Description texts

Information extraction

Question At which part of the body are the handles attached?

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Implementation Description texts

Information extraction

Question At which part of the body are the handles attached? 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.

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Implementation Description texts

Information extraction

Question At which part of the body are the handles attached? 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

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Implementation Description texts

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

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Implementation Description texts

Demonstration of EVA

The main module ist called core.py core.py builds the decision tree with tree.py and a knowledge base stored in a text file In dictionaries.py the user friendly questions and answers are stored. The patterns for information extraction are also stored in dictionaries.py

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Implementation Description texts

Demonstration of EVA

The main module ist called core.py core.py builds the decision tree with tree.py and a knowledge base stored in a text file In dictionaries.py the user friendly questions and answers are stored. The patterns for information extraction are also stored in dictionaries.py EVA is in an experimental status.

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Discussion

Outline

1 Motivation 2 The problem 3 What is an expert system?

How it works Properties

4 Implementation

Before implementation System architecture The knowledge base Description texts

5 Discussion

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Discussion

Drawbacks

time-consuming Knowledge base and patterns are handcraftet. abstract The program does not consider the actual vase (e.g. looks at images). It

• nly relies on textual descriptions.
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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

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Discussion

Discussion

Computer aided classification and dating of ceramics is possible.

What do you think? martina.trognitz@dainst.de

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