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Artificial Intelligence Approaches for Information Retrieval

Artificial Intelligence Approaches for Information Retrieval Outline Artificial Intelligence (AI) AI and IR AI applied to IR Information characterisation Information seeking System Integration Support functions Conclusion References Aims of AI

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Artificial Intelligence Approaches for Information Retrieval

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  1. Artificial Intelligence Approaches for Information Retrieval Outline Artificial Intelligence (AI) AI and IR AI applied to IR Information characterisation Information seeking System Integration Support functions Conclusion References

  2. Aims of AI • Building intelligent entities as well as understanding them • Systems that reason with information in some way Ex: problem solving, classification, learning, planning • Usually use some explicit representation of information and some means of manipulating information

  3. Four goals of AI

  4. AI and IR “[AI] This is the use of computers to carry out tasks requiring reasoning on world knowledge, as exemplified by giving responses to questions in situation where one is dealing with only partial knowledge and with indirectconnectivity” Karen Sparck Jones, 1991 “We construe a system to be knowledge-based when its behavior depends largely on accessing or encoding information” Call for papers - FLAIRS conferences

  5. Areas of AI for IR • Natural language processing • Knowledge representation • Expert systems • Ex: Logical formalisms, conceptual graphs, etc • Machine learning • Short term: over a single session • Long term: over multiple searches by multiple users • Computer Vision • Ex: OCR • Reasoning under uncertainty • Ex: Dempster-Shafer, Bayesian networks, probability theory, etc • Cognitive theory • Ex: User modelling

  6. AI applied to IR • Four main roles investigated • Information characterisation • Search formulation in information seeking • System Integration • Support functions

  7. Information characterisation - Approach 1 (1) • “Strongest” AI approach • Set of documents reported as a single knowledge base • Directly manipulating the information available => Knowledge-base retrieval calcium is made of BONE is kind of is kind of is kind of HUMERUS RADIUS ULNA “…calcium Content of bones …” “… the humerus Bones…” “… radius and Ulna, bones in Arm…”

  8. Information characterisation - Approach 1 (2) • Query: “does calcium deficiency cause Smith’s disease?” • Criticism: this is a model for question/answering system (not “traditional IR”) • Replace document text (natural language) with a knowledge base in an artificial language • Much of the (textual) information is lost • What will be put in the knowledge base • Issue of information extraction • Problem with large collection • Successful in specific domain (SCISOR)

  9. Information characterisation - Approach 2 • Weaker view of AI • Keep documents and use knowledge base as access tool (query formulation) • Semantic-based access, concept-based access • Interface and presentation • Better classification of document text and better access • Criticism: problems of (automatic) linkages (documents have different style, language and level of discussion)

  10. Information characterisation - Approach 3 • Even weaker AI approach • Abandon knowledge base but use AI (syntactic level) to characterise document content • Sophisticated matching • Use NLP to derive • Noun-phrases: “The mother of Jane <=> Jane’s mother” • Sentences: “The boy ate the apple <=> The apple was eaten by the boy” • Normalisation is necessary! • Very little of evidence of success (so far)

  11. Information characterisation - Approach 4 • Weakest AI approach • Use AI to select good natural language index terms • Thesaurus construction • Compound terms • Use world knowledge and a bit of linguistics (eg noun vs verb, discourse) • Important caveat or warning: “Most criticisms are valid for cases when we have large-scale and a variety of need on user side. We may have quite a different situation on specialised contexts where an AI approach … may be both justifiable and feasible” Karen Sparck Jones, 1991

  12. Information seeking • Characterisation of the user’s information need (and not the actual matching) • User modelling • “Automating the intermediary” giving the user an intelligent front-end • Over iterative searching and dialogue, determine use’s real information need Medical doctor vs medical student Student and general topic: look for a survey document • Criticism: users have difficulty expressing their information need  difficult of manually or automatically deriving rules for systems • Possible in limited context • Expert Systems

  13. Expert systems (1) • Designed to simulate expert in narrow/specific field • Rule-based systems fact: p rule: if p then q -------------------- Then add fact: q • Ex: if (patient has red spots) then (patient has measles) patient has red spots ------------------------------------------------------------- patient has measles • Rules can be uncertain

  14. Expert systems (2) • Development of an expert intermediary system to assist with • query formulation • search strategy selection • Problem (Brooks 87) • Evaluation • Formulation of the expertise (typical problem with expert system, and not just for IR)

  15. Expert systems for searching (1) (Khoo and Poo 1993) • Expert interface to online catalog • Expert system • Use search heuristics derived from humans • Rules for selecting good heuristics • Explanation of strategy selection

  16. Expert systems for searching (2) • Relevance feedback • Collect statistics from search • Number of document retrieved, precision, … • Use rules to automatically improve content of search • Query terms used - addition/removal of terms, synonyms • Connectives used

  17. Rules • 3 types of rules • Data abstraction rules • if precision <=20% then precision level is 1 • if precision > 80% then precision level is 5 • if retrieval size is 101-200 then retrieval level is 4 (1 - very low … 5 very high) • Heuristic matching rules • if precision level is 2 or 3 and retrieval level >2 then use narrowing strategy • Refinement rules • if a narrowing strategy is needed then select strategy “use terms that have high frequency in relevant records” with weight 0.8

  18. Application of rules • Forward chaining • Analyse change in statistics • Decide what heuristic rule apply • Choose refinement rules (strategies) • Backward chaining • Analyse strategies in turn to see if conditions hold • Once chosen strategies • Rank strategies by weight • Implement in turn

  19. Expert systems • Other expert systems exist for • Specialised dialogue functions (eg building a user model) • Domain knowledge representation • Intensive to build • Good for helping with complex tasks

  20. System Integration • How to search over different collections, different types of objects, different representations • Wrappers • Mediators • Machine processable semantics of information for B2B, C2B, e-commerce • XML • RDF • Ontologies

  21. Wrapper • Wrappers have been developed as a component in information mediation architectures Integrated query access to heterogeneous and distributed information sources • Wrapper: intermediate layer that mediates between users and information sources • Mediator: make the user transparent that the information sources are distributed, i.e. translates query into sub-queries • Wrapper: makes the mediator transparent that the information sources are heterogeneous (protocol, syntax, semantics) • WWW: use HTML-based document structure for heuristic information extraction

  22. Query Agent Mediator Wrapper 1 Wrapper 2 Wrapper n Source 1 Source 2 Source n Agent-based technology

  23. XML • Different to HTML, XML tags can be written for specific applications • Tags define the semantics of the data • (HTML tags mainly used as a layout language) • Example <Person> <Name>Mounia Lalmas</Name> <Email>mounia@dcs.qmul.ac.uk</Email> </Person>

  24. XML Schema • Define a grammar and meaningful tags for documents (DTD) • They are XML documents • Provide a rich set of datatypes that can be used to define the values of elementary tags • Provide namespace mechanism to combine XML documents with heterogeneous vocabularies • Example <!ELEMENT name (title*, first name | initial, middle name? Last name+)> <!ELEMENT first name #PCDATA> <name> <title>Dr</title> <first name>Mounia</first name> <last name>Lalmas<last name> </name>

  25. RDF • XML provides semantic information as a by-product of defining the structure of document • XML prescribes a tree structure for documents and the different leaves of the tree have a well-defined tag and context the information can be understand with  structure and semantics of documents are interwoven The Resource Description Framework RDF provides a means for adding semantics to a document without making any assumptions about the structure of the documents and it provides pre-defined modelling primitives for expressing semantics of data

  26. RDF • RDF is an XML application (i.e. its syntax is defined in XML) customised for adding meta-information to web documents • Currently under development as a W3C standard for content descriptions of web data • Three types of objects: • Resources (subjects) Entity that can be referred to by an address on the web (by an URI); elements that are described by RDF statements. • Properties (predicates) define a binary relation between resources and/or atomic values provided by the primitive datatype definitions in XML • Statements (objects) specifies for a resource a value of the property; provide actual characterisation of the web documents

  27. RDF: Simple example • Author(http://www.dcs.qmul.ac.uk/~mounia) = Mounia States that the author of the named web document is Mounia • Values can also be structured entities: Author(http://www.dcs.qmul.ac.uk/~mounia) = X Name(X) = Mounia Email(X) = mounia@dcs.qmul.ac.uk where X denotes an actual URI (Universal Resource Identifier) • Syntax of RDF is different! • RDF Schema

  28. Ontologies • Neither XML and RDF define a standard vocabulary for describing semantics of documents • Define standard vocabularies • Neither XML nor RDF define a standard structure for describing semantics of documents • Define standard structure or mappings between different structures • Ontologies • Consensual and formal specifications of conceptualisations • Provides a shared and common understanding of a domain that can be communicated across people and applications systems

  29. Ontologies • Definition of vocabulary (i.e. concepts) • Definition of structure (i.e. attributes and concept hierarchy) • Logical characterisation of concepts and attributes • Domain and range restrictions • Properties of relations (symmetry, transitivity) • General logical axioms • Examples: • Wordnet • CYC • Ontolingua • DAML, OIL (www.semanticweb.org)

  30. Support functions • Information extraction • Abstracting and summarising • Cataloguing (Ontology) • Automatically linking parts of texts (Hypertext) • Thesaurus/dictionary building(Linguistics) • Story telling (News)

  31. Information extraction • Analyse unstructured text • Extract “relevant” data from collection of documents • Application area of computation linguistics • Examples • News reading program (Jacobs and Rau, 1990) • Generation of fields from free text for database purpose • Application to the web (“semantic-based” search engine, e-commerce)

  32. Information extraction • Based on extraction patterns Concept + linguistic patterns as pre-conditions • Example: We want to extract the target of the terrorist attack from a sentence: The parliament was bombed by the guerrillas. • Triggering word: bombed (stemming) • Linguistic pattern: <subject><passive-verb> • Subject is extracted as target

  33. Summarising and abstracting • Summarising: Extracting important sentences • Abstracting: Forming a sequence sentences that summarise the content of a document. • Output of web search engines • Useful for viewing (at a glimpse) retrieved documents • Cost in downloading the documents • Help for relevance feedback user assessment

  34. Conclusions • Do not overestimate the power of AI in IR • Tasks depending on real world knowledge are hard to design, to build and perhaps to use • To be flexible sophisticated enough for wide range of users • Many IR tasks are not deep but of a more shallow linguistic nature • AI has a very valuable contribution to make • Specialised systems where domain is controlled, well-integrated and understood • Support functions • Case-based reasoning and dialogue functions • Integrated functions

  35. References • C. Stanfill and D.L. Waltz, 1992. Statistical Methods, Artificial Intelligence, and Information Retrieval. In text-based intelligent systems. Current research and practice in Information Extraction and Retrieval (eds. P.S. Jacobs) Lawrence Erlbaum Associates Intelligent • K. Sparck Jones, 1991. The role of Artificial Intelligence in Information Retrieval, JASIS 42(8) pp 558-565 • W.B. Croft, 1987. Approaches to intelligent information retrieval, IP&M 23(4) pp 249-254. • P. Jacobs and L. Rau, 1990. SCISOR, Extracting information from on-line news. Communications of the ACM 33(11), pp88-97. • A.F. Smeaton, 1992. Progress in the Application of Natural Language Processing to Information Retrieval Tasks. In The Computer Journal, 36 (3) • Srinivasan, P., 1991. Thesaurus construction. In Frakes, W., & Baeza-Yates, R. (eds), Information Retrieval: Data Structures & Algorithms, Prentice Hall. • C,S.G. Khoo and D.C.C Poo, 1993. An expert system approach to online catalog subject searching. Information Processing & Management, 30(2):223-238. • H.M. Brooks, 1987. Expert Systems and Intelligent Information Retrieval. Information Processing & Management 23(4):341-366 • K. Sparck Jones, 1999. Information retrieval and artificial intelligence. Artificial Intelligence 114:257-281.

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