reasoning methodologies in information technology

Reasoning Methodologies in

Information Technology

R. WeberCollege of Information Science & Technology

Drexel University

Reasoning Methodologies in IT

R Weber

Outline

1. Intelligent Jurisprudence Research

2. Proactive Dissemination of Lessons-learned


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1. Intelligent Jurisprudence Research

• Why is current technology used for jurisprudence research propagating injustice?

• What technology is available to counteract this trend?

• What are the barriers?


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Current technology used are text databases

• Search for relevant precedents• Common Law and Roman Law• Judicial professionals: attorneys, judges• Create a query using Boolean operators• Read every document to assess their

relevance


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What is wrong with text databases?

• Writing queries is difficult and imprecise• Finding a set of words that are present in

relevant documents and NOT present in non-relevant ones

• There is a limit to the number of documents that are returned to the user

• Matches are based on surface features: words, trigrams, etc.

• Natural language is ambiguous and open-textured

• Users need to go through entire documents to assess their relevance


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Literature on text databases

• 20% recall for 79% precision (Blair & Maron 1985)– 100 rel. doc: 25 retrieved, 20 rel and 5 non-rel.

• Better recall can be achieved, but then precision falls (20% more in large databases) (id.).

• “It was found that in high recall searching it was not possible to achieve as high performance in the large databases as in the small one.” (Sormunen 2001)

• Most problems originating low quality are worse in larger databases.


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What technology is available to counteract this trend?

• How do humans search?• Reasoning methodology that replicates

this task• How humans search for legal precedents?

– Target problem in mind– Search for applicable solutions– How?– By assessing similarity between problems

• Which reasoning methodology can replicate this task?


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Case-Based Reasoning

• A reasoning methodology that mimics the similarity heuristic


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How can we use case-based reasoning for searching

jurisprudence with high levels of precision and recall?


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Legal Cases

tasks

relations

evidence

attenuatingcircumstances


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Similarity Assessment

• Matching occurs when both documents have similar meaning and not simply same words or same surface features

axe

murder

axe

murder


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Similarity Assessment

• Matching occurs when both documents have similar meaning and not simply same words or same surface features

axe

robbery

axe

murderX


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Intelligent Jurisprudence Research

• The problem or situation motivating the search is represented in the method’s format (and becomes the target case) after a question-answer session

• Legal cases are retrieved based on how similar they are to the target case, i.e. legal cases from different areas of law can also be considered similar

• Users access the case representation of retrieved documents


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Barriers• Current text databases ‘seem’ to work• They represent an apparent

substantial improvement over previous method

• Smaller databases can produce better results

• No one is suing anyone………yet• Designing intelligent jurisprudence

research requires manual engineering whereas (domain independent) text databases don’t


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Work on Technological Barriers

• Several researchers are seeking ways to reduce manual engineering requirements, maintaining high quality

• Information Extraction uses NLP (1991)

• Template Mining (1998)• Machine learning (1999)• Automated conversion into Graphs

(2004)


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Further Applications in Law

• Common Law:– When legal cases are fully engineered,

case-based reasoning systems can build complete argumentation structures (Ashley 1990)

• Roman Law:– Aiming at reducing the uncertainty

between the law and its interpretation, create a judicial system based on a controlled reasoning structure that appends legal decisions to rule-based representations of law (Correa 2003)


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Questions?

[email protected]


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2. Proactive Dissemination of Lessons-Learned

• Knowledge management, experience, and learning curves

• Why is it so difficult to share knowledge?

• Why best-practices/lessons-learned repositories don’t work?

• What can be done?


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Knowledge Management• Knowledge management refers to best

allocation of intellectual assets• Organizations become mature through

experience and distribution of their knowledge, e.g. culture, doctrine

• Learning curves ascend as organization become mature but oscillate depending upon:– Changing technologies– Changing consumers, focus, etc.– When scope is broad, they will be always

learning– Large number of members and large scope

makes the worst combination for learning


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Why is it so difficult to share knowledge?

• It is difficult to learn• It is difficult to communicate• It is difficult to absorb


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It is difficult to learn

• Learning involves taking risks• If one repeats exactly the same

routine everyday one will hardly learn anything

• Learning originates from positive and negative experiences

• New experiences


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It is difficult to communicate

• Culture in some organizations motivate sharing, e.g. XEROX

• Communicate may imply admitting attempting something new, equals risk

• Communicating may decrease one’s competitive advantage

• Others may not be interested


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It is difficult to absorb

• Anthropologists explain that people only pay attention when knowledge is presented when and where it is needed

• Knowledge will be absorbed when it is needed, when it is applicable

• Knowledge will be absorbed where it is needed, in the context where it is needed, e.g. in within organizational context


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Why best-practices/lessons-learned repositories are not used?

• They are standalone– They are outside the context of where they are

needed– They are not tied to the contexts when they are

needed

• Because they require users to take the initiative to search them

• Because they may not believe they are useful

• They need to learn how to operate them• They may be poorly composed, produce poor

results


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What can be done?

• Integrate knowledge repositories to applicable tasks

• Definition of lesson-learned requires it positively impacts a process it targets

• Integration requires that tasks are identified in the body of lessons-learned

• Use an applicability oriented method to retrieve lessons-learned

• Guarantee that lessons-learned are ONLY retrieved when and where they are needed


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Requirements

• Users (or a knowledge worker) have to deliver their tasks using computerized environment where applicable tasks are identified

• Lessons-learned have to be collected in a manner that their requirements are all met, e.g. must produce positive impact


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• Examples• References• Questions

Microsoft PowerPoint Presentation

reasoning methodologies in information technology

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