Blohm S, Umbrich J, Cimiano P, Sure Y. Iterative Learning of Relation Patterns for Market Analysis with UIMA. In:  UIMA Workshop at GLDV Frühjahrstagung. 2007

Blohm, Sebastian

Cimiano, Philipp 

Sure, York

Umbrich, Jürgen

English

Publications at Bielefeld University

Iterative Learning of Relation Patterns for Market Analysis with UIMASebastian Blohm, Ju¨rgen Umbrich, Philipp Cimiano, York SureInstitute AIFB, Knowledge Management Research GroupUniversity of KarlsruheD-76128 Karlsruhe, Germany{blohm,juub,cimiano,sure}@aifb.uni-karlsruhe.deIntroductionMonitoring news as well as corporate and community websites is a important task in market analysis. Yet, it requiresmuch manual processing and is inherently incomplete asonly a small fraction of the data produced on the Web canbe processed by human analysts. We present here our on-going efforts to employ UIMA annotations and search onUIMA-annotated documents for supporting market analysis.Information is extracted based on relation patterns that serveas queries. The results are then visualized to allow detailedmarket analysis. The current status of the reported work isthat we are already implemented most of the required infras-tructure (UIMA, Pronto, Omnind) and started some of theoutlined experiments.The Unstructured Information Management Ar-chitecture (UIMA) is a framework that enablesa component-based analysis of unstructured docu-ments(Ferrucci and Lally, 2004) providing a typed datastructure for the derived structured information as well asmeans for dening and controlling process ows.We have developed Pronto, a system for automatic learn-ing of text patterns for extracting instances of semantic re-lations(Blohm and Cimiano, 2006). The system essentiallyuses the Web as a corpus accessing it via a Web search en-gine. Patterns are learned by abstraction over the textualoccurrences of relation instances in the corpus. In the ex-periments we employ Pronto to produce UIMA annotationsand additionally feed the UIMA annotation structure intothe learning process to be able to learn structural patternsrather than plain text patterns. They will enable our systemto annotated mentions of salient relations for market anal-ysis. The output will allow retrieving facts about marketevents using structured queries.<sentence>In <year>2005</year><presented subj='Fiat' object='Punto'>Fiat presented the new Punto</presented>at the <exhibition>IAA</exhibition>in Frankfurt.</sentence>Figure 1: Architecture overview.The Pronto SystemThe Pronto system allows automatic generation of appro-priate text patterns serving as queries for Web search en-gines. Pronto uses an iterative pattern induction approachsimilar to that applied in the DIPRE (Brin, 1998) and Snow-ball (Agichtein and Gravano, 2000) systems. Pronto main-tains a set of patterns for which condence scores are com-puted using statistical models, frequency of co-occurrenceor precision estimates based on previous extractions. Theset is updated in each iteration which allows for exibilityand broader coverage. For each extracted relation, a con-dence score is computed based on the number and quality ofthe patterns that retrieved that instance. Pronto takes as in-put a small number (e.g. 10) of example instances of a givenrelation and is able to produce around 1000 new instanceper hour when querying Google as a remote search index. Ithas been applied to non-taxonomic relations from differentdomains and with different characteristics reaching between45% and 85% precision.Using index-based-search for information extraction hasthe advantages that large amounts of text become manage-able and that new patterns can be introduced and tried-outwithout having to re-process the entire corpus.System ArchitectureThe core of the architecture for our experiments consists ofthe integration of our web search based information extrac-tion system Pronto with the UIMA analysis process as wellas a search engine that allows retrieval on UIMA annota-tion structures. On top, IBM OmniFind will be used as asearch engine. Prior to indexing, OmniFind runs a UIMAprocessing chain in which we intend to plug annotators thatare based on relevant background knowledge and on anno-tation patterns produced by Pronto. The index is then usedin three ways:• To visualize the development of the market (e.g. by plot-ting extraction result counts for salient patterns over time)• As a semantic search tool to further explore market devel-opments.• As interface to the Web corpus for the Pronto algorithm toimprove its patterns. In particular by taking other annota-tions (both from the background knowledge and from thepatterns previously produced by Pronto).The UIMA API serves as the interface between the docu-ment crawler, the annotators and the indexer. The study willlargely benet from the Common Analysis Structure (CAS)which allows interaction between third party annotators wewill use (for linguistic analysis and background knowledge)and the annotator that exploits the Pronto patterns. Om-niFind's Search and Indexing API (SIAPI) will be employedas the interface to Pronto's extraction component as well asto the semantic search and trend visualization component.We will make use of OmniFind's the XML Fragments querylanguage to query for text and UIMA annotations within asingle search engine request.ExperimentsIn this section we rst describe the data sets on which weintend do do our experiments and then describe the courseof experiments in further detail.DatasetsWe are using two fragments of the Web for our experiments.• Wikipedia: which shows many characteristics of largecorporate intranets: It shows higher quality, more struc-ture and less spam as the Web average and structural in-formation on the content is available (e.g. categories).• Automotive web sites: a crawl from a number of salientweb sites providing news about the automotive industrytogether with expert knowledge on a relevant semantic re-lations from market research.Course of ExperimentsOur experiments are executed in three phases of increasingintegration of UIMA extractions into the pattern inductionprocess. We are currently in the rst phase.In an initial, naive phase, extraction patterns previouslyderived on the web will be matched on documents prior toindexing to produce relation annotations. Patterns wouldlook as follows:* released the new * atWhen integrated with the search API of OmniFind, pat-terns can be induced that use UIMA annotations to focusthe search. Note that Pronto will be able to determine therelevant tags automatically by considering occurrences ofseed instances and observing the UIMA annotations that arepresent. The relevant kinds of background knowledge willthus be determined automatically.<carMaker/> released the new <carModel/> atFinally, our aim is to increase Pronto's learning capabili-ties to learning structural patterns such as:<article category='news'><sentence><carMaker/> released the new <carModel/> at</sentence></article>Market visualization will then be possible by taking axed set of queries like:<presented subj='Fiat' object='*'/><year>2007</year>To monitor news on Fiat car releases. A substantial changein search result counts may indicate an interesting marketevent.ConclusionWe presented the key components of our architecture(UIMA, Pronto, and Omnind). Our aim is to integratestructural search on UIMA annotations into our informationextraction system for market analysis. We expect two majorbenets of UIMA to extraction quality, i.e. (i) increased pre-cision of search results due to patterns that are sensitive tobackground knowledge annotations (e.g. noun categories)and (ii) increased coverage of patterns as text surface pat-terns which require high redundancy are replaced by moreabstract structural patterns.AcknowledgementsWe are very grateful for the IBM UIMA Innovation Award 2006.This work was partially funded by the X-Media project (www.x-media-project.org) sponsored by the EC as part of the IST programunder EC grant number IST-FP6-026978. The OmniFind searchengine software is kindly provided by IBM through their AcademicInitiative Program.References[Agichtein and Gravano, 2000] Agichtein, E. and Gravano, L.(2000). Snowball: extracting relations from large plain-text col-lections. In Proceedings of the fth ACM conference on Digitallibraries (DL), pages 8594.[Blohm and Cimiano, 2006] Blohm, S. and Cimiano, P. (2006).Learning patterns from the web  evaluating the evaluation func-tions. In OTT'06 - Ontologies in Text Technology: Approaches toExtract Semantic KnowledInformation.[Brin, 1998] Brin, S. (1998). Extracting patterns and relationsfrom the world wide web. In WebDB Workshop at 6th Interna-tional Conference on Extending Database Technology, EDBT'98.[Ferrucci and Lally, 2004] Ferrucci, D. and Lally, A. (2004).UIMA: An Architectural Approach to Unstructured InformationProcessing in the Corporate Research Environment. Natural Lan-guage Engineering.

Iterative Learning of Relation Patterns for Market Analysis with UIMA

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Iterative Learning of Relation Patterns for Market Analysis with UIMA

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