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86 Pages

4-Knowledge-Retrieval

Course: CSC 581, Fall 2009
School: Cal Poly
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Word Count: 2935

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Retrieval Franz Knowledge J. Kurfess Computer Science Department California Polytechnic State University San Luis Obispo, CA, U.S.A. Franz Kurfess: Knowledge Retrieval Knowledge Retrieval Franz J. Kurfess Computer Science Department California Polytechnic State University San Luis Obispo, CA, U.S.A. Franz Kurfess: Knowledge Retrieval Knowledge Retrieval Franz J. Kurfess Computer Science Department California...

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Retrieval Franz Knowledge J. Kurfess Computer Science Department California Polytechnic State University San Luis Obispo, CA, U.S.A. Franz Kurfess: Knowledge Retrieval Knowledge Retrieval Franz J. Kurfess Computer Science Department California Polytechnic State University San Luis Obispo, CA, U.S.A. Franz Kurfess: Knowledge Retrieval Knowledge Retrieval Franz J. Kurfess Computer Science Department California Polytechnic State University San Luis Obispo, CA, U.S.A. Franz Kurfess: Knowledge Retrieval Acknowledgements Some of the material in these slides was developed for a lecture series sponsored by the European Community under the BPD program with Vilnius University as host institution Acknowledgements This lecture series has been sponsored by the European Community under the BPD program with Vilnius University as host institution Franz Kurfess: Knowledge Retrieval Use and Distribution of these Slides These slides are primarily intended for the students in classes I teach. In some cases, I only make PDF versions publicly available. If you would like to get a copy of the originals (Apple KeyNote or Microsoft PowerPoint), please contact me via email at fkurfess@calpoly.edu. I hereby grant permission to use them in educational settings. If you do so, it would be nice to send me an email about it. If youre considering using them in a commercial environment, please contact me first. Franz Kurfess: Knowledge Retrieval 6 Overview Knowledge Retrieval Finding Out About Keywords and Queries; Documents; Indexing Access via Address, Field, Name Data Retrieval Information Retrieval Access via Content (Values); Parsing; Matching Against Indices; Retrieval Assessment Access via Structure;Meaning;Context; Usage Data Mining; Rule Extraction Franz Kurfess: Knowledge Retrieval Knowledge Retrieval Knowledge Discovery 7 Logistics Franz Kurfess: Knowledge Retrieval 8 Preliminaries Franz Kurfess: Knowledge Retrieval 9 Bridge-In How do you organize your knowledge? brain paper computer Franz Kurfess: Knowledge Retrieval 10 Finding Out About Franz Kurfess: Knowledge Retrieval [Belew 2000] 11 Motivation and Objectives Franz Kurfess: Knowledge Retrieval 12 Motivation Franz Kurfess: Knowledge Retrieval 13 Objectives Franz Kurfess: Knowledge Retrieval 14 Evaluation Criteria Franz Kurfess: Knowledge Retrieval 15 Finding Out About Franz Kurfess: Knowledge Retrieval 16 Finding Out About Keywords Queries Documents Indexing Franz Kurfess: Knowledge Retrieval [Belew 2000] 17 Keywords linguistic atoms used to characterize the subject or content of a document words pieces of words (stems) phrases provide the basis for a match between the users characterization of information need the contents of the document problems Franz Kurfess: Knowledge Retrieval ambiguity choice of keywords [Belew 2000] 18 Queries formulated in a query language natural language interaction with human information providers interaction with computers especially search engines artificial language vocabulary controlled limited set of keywords may be used any keywords may be used uncontrolled syntax Franz Kurfess: OR) often Boolean operators (AND,Knowledge Retrieval [Belew 2000] 19 Documents general interpretation any document that can be represented digitally text, image, music, video, program, etc. practical interpretation passage of text strings of characters in an alphabet written natural language length may vary longer documents may be composed of shorter ones Franz Kurfess: Knowledge Retrieval 20 Aboutness of Documents describes the suitability of a document as answer to a query assumptions all documents have equal aboutness the probability of any document in a corpus to be considered relevant is equal for all documents simplistic; not valid in reality a paragraph is the smallest unit of text with appreciable aboutness Franz Kurfess: Knowledge Retrieval [Belew 2000] 21 Structural Aspects of Documents documents may be composed of documents paragraphs, subsections, sections, chapters, parts footnotes, references documents may contain meta-data information about the document not part of the content of the document itself may be used for organization and retrieval purposes can be abused by creators usually to increase the perceived relevance Franz Kurfess: Knowledge Retrieval 22 Document Proxies surrogates for the real document abridged representations catalog, abstract bibliographical citation, URL pointers different media microfiches digital representations Franz Kurfess: Knowledge Retrieval 23 Indexing a vocabulary of keywords is assigned to all documents of a corpus an index maps each document doc to the set of i keywords {kwj} it is about Index: doci about {kwj} -1 describes Index : {kwj} doci indexing of a document / corpus manual: humans select appropriate keywords automatic: a computer program selects the keywords building the index relation between documents and sets of keywords is critical for information Franz Kurfess: Knowledge Retrieval [Belew 2000] 24 FOA Conversation Loop Franz Kurfess: Knowledge Retrieval [Belew 2000] 25 Data Retrieval access to specific data items access via address, field, name typically used in data bases user asks for items with specific features absence or presence of features values system returns data items no irrelevant items deterministic retrieval method Franz Kurfess: Knowledge Retrieval 26 Information Retrieval (IR) access to documents also referred to as document retrieval access via keywords IR aspects parsing matching against indices retrieval assessment Franz Kurfess: Knowledge Retrieval 27 Diagram Search Engine Franz Kurfess: Knowledge Retrieval [Belew 2000] 28 Parsing extraction of lexical features from documents mostly words may require some manipulation of the extracted features e.g. stemming of words used as the basis for automatic compilation of indices Franz Kurfess: Knowledge Retrieval [Belew 2000] 29 Parsing Tools Montytagger http://web.media.mit.edu/~hugo/montytagger/ python and Java fnTBL (C++) http://nlp.cs.jhu.edu/~rflorian/fntbl/ fast the original; influenced several later ones Brill Tagger (C) http://www.cs.jhu.edu/~brill/ Natural Language Toolkit: http://nltk.sourceforge.net/ good starting point for basics of NLP algorithms Franz Kurfess: Knowledge Retrieval 30 Matching Against Indices identification of documents that are relevant for a particular query keywords of the query are compared against the keywords that appear in the document either in the data or meta-data of the document in addition to queries, other features of documents may be used descriptive features provided by the author or cataloger usually meta-data derived features computed from the contents of the document Franz Kurfess: Knowledge Retrieval [Belew 2000] 31 Vector Space interpretation of the index matrix relates documents and keywords can grow extremely large binary matrix of 100,000 words * 1,000,000 documents sparsely populated: most entries will be 0 can be used to determine similarity of documents overlap in keywords proximity in the (virtual) vector space associative memories can be used as hardware implementation [Belew 2000] Franz Kurfess: Knowledge Retrieval 32 Vector Space Diagram Franz Kurfess: Knowledge Retrieval [Belew 2000] 33 Measuring Retrieval ideally, all relevant documents should be retrieved relative to the query posed by the user relative to the set of documents available (corpus) relevance can be subjective precision and recall relevant documents vs. retrieved documents Franz Kurfess: Knowledge Retrieval 34 Document Retrieval Franz Kurfess: Knowledge Retrieval [Belew 2000] 35 Precision and Recall recall |retrieved relevant| / |relevant| precision |retrieved relevant| / |retrieved| Franz Kurfess: Knowledge Retrieval [Belew 2000] 36 Specificity vs. Exhaustivity Franz Kurfess: Knowledge Retrieval [Belew 2000] 37 Retrieval Assessment subjective assessment how well do the retrieved documents satisfy the request of the user idealized omniscient expert determines the quality of the response objective assessment Franz Kurfess: Knowledge Retrieval [Belew 2000] 38 Retrieval Assessment Diagram Franz Kurfess: Knowledge Retrieval [Belew 2000] 39 Relevance Feedback subjective assessment of retrieval results often used to iteratively improve retrieval results may be collected by the retrieval system for statistical evaluation can be viewed as a variant of object recognition the object to be recognized is the prototypical document the user is looking for this document may or may not exist the difference between the retrieved document(s) and the idealized prototype indicates the quality of the retrieval results Franz Kurfess: Knowledge Retrieval [Belew 2000] 40 Relevance Feedback in Vector Space relevance feedback is used to move the query towards the cluster of positive documents moving away from bad documents does not necessarily improve the results it can also be used as a filter for a constant stream of documents as in news channels or similar situations Franz Kurfess: Knowledge Retrieval [Belew 2000] 41 Query Session Example Franz Kurfess: Knowledge Retrieval [Belew 2000] 42 Consensual Relevance relevance feedback from multiple users identifies documents that many users found useful or interesting used by some Web sites related to collaborative filtering can also be used as an evaluation method for search engines performance criteria must be carefully considered precision and recall, plus many others Franz Kurfess: Knowledge Retrieval [Belew 2000] 43 IR Diagram Index Query Corpus Doc. 5 Doc. 5 Doc. 5 Doc. 5 Doc. 5 Doc. 4 Doc. 4 Doc. 4 Doc. 4 Doc. 4 Doc. 3 Doc. 3 Doc. 3 Doc. 3 Doc. 3 Doc. 2 Doc. 2 Doc. 2 Doc. 2 Doc. 2 Doc. 1 Doc. 1 Doc. 1 Doc. 1 Doc. 1 Documents Term 1 Term 2 Term 3 Term 4 Keywords Franz Kurfess: Knowledge Retrieval 44 IR Diagram Index Query Corpus 5 Doc. Doc. 5 Doc. 5 Doc. 5 Doc. 5 Doc. 4 Doc. 4 Doc. 4 Doc. 4 Doc. 4 Doc. 3 Doc. 3 Doc. 3 Doc. 3 Doc. 3 Doc. 2 Doc. 2 Doc. 2 Doc. 2 Doc. 2 Doc. 1 Doc. 1 Doc. 1 Doc. 1 Doc. 1 Documents Term 1 Term 2 Term 3 Term 4 Keywords Franz Kurfess: Knowledge Retrieval 45 IR Diagram Index Query Corpus Doc. 5 Doc. 5 Doc. 5 Doc. 5 Doc. 5 Doc. 4 Doc. 4 Doc. 4 Doc. 4 Doc. 4 Doc. 3 Doc. 3 Doc. 3 Doc. 3 Doc. 3 Doc. 2 Doc. 2 Doc. 2 Doc. 2 Doc. 2 Doc. 1 Doc. 1 Doc. 1 Doc. 1 Doc. 1 Documents Term 1 Term 2 Term 3 Term 4 Keywords Franz Kurfess: Knowledge Retrieval 46 IR Diagram Index Query Corpus Doc. 5 Doc. 5 Doc. 5 Doc. 5 Doc. 5 Doc. 4 Doc. 4 Doc. 4 Doc. 4 Doc. 4 Doc. 3 Doc. 3 Doc. 3 Doc. 3 Doc. 3 Doc. 2 Doc. 2 Doc. 2 Doc. 2 Doc. 2 Doc. 1 Doc. 1 Doc. 1 Doc. 1 Doc. 1 Documents Term 1 Term 2 Term 3 Term 4 Keywords Franz Kurfess: Knowledge Retrieval 47 IR Diagram Index Query Corpus Doc. 5 Doc. 5 Doc. 5 Doc. 5 Doc. 5 Doc. 4 Doc. 4 Doc. 4 Doc. 4 Doc. 4 Doc. 3 Doc. 3 Doc. 3 Doc. 3 Doc. 3 Doc. 2 Doc. 2 Doc. 2 Doc. 2 Doc. 2 Doc. 1 Doc. 1 Doc. 1 Doc. 1 Doc. 1 Documents Term 1 Term 2 Term 3 Term 4 Keywords Franz Kurfess: Knowledge Retrieval 48 Knowledge Retrieval Context Usage exploratory search faceted search Franz Kurfess: Knowledge Retrieval 49 Context in Knowledge Retrieval in addition to keywords, relationships between keywords and documents are exploited explicit links hypertext thesaurus, ontology related concepts proximity spatial: place, directory temporal: creation date/time intermediate relations Franz Kurfess: Knowledge Retrieval author/creator organization 50 Inference beyond the Index determines relationships between documents citations are explicit references to relevant documents bibliographic references legal citations hypertext examples NEC CiteSeer <http://citeseer.nj.nec.com> Google Scholar http://scholar.google.com Franz Kurfess: Knowledge Retrieval 51 Additional Information Sources Franz Kurfess: Knowledge Retrieval [Belew 2000, after Kochen 1975] 52 Hypertext inter-document links provide explicit relationships between documents can be used to determine the relevance of a document for a query example: Google <http://www.google.com> intra-document links may offer additional context information for some terms footnotes, glossaries, related terms Franz Kurfess: Knowledge Retrieval 53 Adaptive Retrieval Techniques fine-tuning the matching between queries and retrieved documents learning of relationships between terms training with term pairs (thesaurus) pattern detection in past queries automatic grouping of documents according to common features clustering of similar documents pre-defined categories metadata overlap in keywords consensual relevance source Franz Kurfess: Knowledge Retrieval 54 Document Classification Franz Kurfess: Knowledge Retrieval 55 Query Model query types (templates) frequently used types of queries e.g. problem/solution, symptoms/diagnosis, problem/further checks, ... category types abstractions of query types used to determine categories or topics for the grouping of search results context information current working document/directory previous queries Franz Kurfess: Knowledge Retrieval [Pratt, Hearst, Fagan 2000] 56 Terminology Model individual terms are connected to related terms thesaurus/ontology synonyms, super-/sub-classes, related terms identifies labels for the category types Franz Kurfess: Knowledge Retrieval [Pratt, Hearst, Fagan 2000] 57 Matching categorizer determines the categories to be selected for the grouping of results assigns retrieved documents to the categories organizer arranges categories into a hierarchy should be balanced and easy to browse by the user depends on the distribution of the search results Franz Kurfess: Knowledge Retrieval [Pratt, Hearst, Fagan 2000] 58 Results retrieved documents are grouped into hierarchically arranged categories meaningful for the user the categories are related to the query the categories are related to each other all categories have similar size not always achievable due to the distribution of documents reduced search times higher user satisfaction Franz Kurfess: Knowledge Retrieval [Pratt, Hearst, Fagan 2000] 59 DynaCat knowledge-based approach to the organization of search results categorizes results into meaningful groups that correspond to the users query uses knowledge of query types and of the domain terminology to generate hierarchical categories MEDLINE is an on-line repository of medical abstracts applied to the domain of medicine 9.2 million bibliographic entries from 3800 journals PubMed is a web-based search tool returns titles as an relevance-ranked list links to related articles Franz Kurfess: Knowledge Retrieval [DynaCat, 2000] 60 DyanCat Results Franz Kurfess: Knowledge Retrieval [DynaCat, 2000] 61 DynaCat Query Types Franz Kurfess: Knowledge Retrieval [DynaCat, 2000] 62 DynaCat Search Franz Kurfess: Knowledge Retrieval [DynaCat, 2000] 63 Information vs. Knowledge Retrieval IR keywords as main components of the query KR keywords plus context information for the query index as match-making facility index plus ontology for matching query and documents statistical basis for selection of relationships between relevant documents keywords and documents influence the selection of relevant documents (ordered) list of results results are grouped into meaningful categories Franz Kurfess: Knowledge Retrieval 64 keyword input synonym expansion relation expansion KR Diagram Index Corpus Doc. 5 Doc. 5 Doc. 5 Doc. 5 Doc. 5 Doc. 4 Doc. 4 Doc. 4 Doc. 4 Doc. 4 Doc. 3 Doc. 3 Doc. 3 Doc. 3 Doc. 3 Doc. 2 Doc. 2 Doc. 2 Doc. 2 Doc. 2 Doc. 1 Doc. 1 Doc. 1 Doc. 1 Doc. 1 Documents Term 3 Term 2 Term 4 Term 1 Keywords Term A Term B Term C Query Ontology Term H Term D Term F Term I Term E Term K Term G Term M 65 FranzTerm JKnowledge Retrieval Kurfess: Term L Exploratory Search finding knowledge through association hypothesis: Human-ma...

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CS 610, Sec 101, Fall 2006, Midterm Exam Prof. David Nassimi 1. Prove thatExam #: (Time: 2 Hours)nName: First Lasti2i=1is (n3 ). GRADE 1 2 3 4 5 SUM /20 /20 /20 /20 /20 /1001CS 610, Sec 101, Fall 2006, Midterm ExamName:2. (a) Use induction to pr