Reasoning Methodologies in Information Technology

1. Reasoning Methodologies in Information Technology R. Weber College of Information Science & Technology Drexel University

2. Outline • Intelligent Jurisprudence Research • Proactive Dissemination of Lessons-learned

3. 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?

4. 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

5. 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

6. 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.

7. 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?

8. Case-Based Reasoning • A reasoning methodology that mimics the similarity heuristic

9. How can we use case-based reasoning for searching jurisprudence with high levels of precision and recall?

10. Legal Cases tasks relations evidence attenuating circumstances

11. Similarity Assessment • Matching occurs when both documents have similar meaning and not simply same words or same surface features axe axe murder murder

12. Similarity Assessment • Matching occurs when both documents have similar meaning and not simply same words or same surface features X axe axe murder robbery

13. 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

14. 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

15. 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)

16. 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)

17. Questions? rosina.weber@drexel.edu

18. 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?

19. 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

20. Why is it so difficult to share knowledge? • It is difficult to learn • It is difficult to communicate • It is difficult to absorb

21. 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

22. 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

23. 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

24. 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

25. 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

26. 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

27. Examples • References • Questions