Introduction. Methodology. Data preparation phase. Information extraction and mining phase. A case study with corn diseases. The results and discussion.EFITA/WCCA 2007

MARCHI, R.

MASSRUHA, S. M. F. S.

MORANDI, M. A. B.

OLIVEIRA, L. H. M. de

OLIVEIRA, S. R. de M.

SILVA, L. M. C. da

SOUZA, K. X. S. de

English

Repository Open Access to Scientific Information from Embrapa

An approach based on text mining for knowledgeacquisition in diagnostic systemsSilvia Maria Fonseca Silveira Massruháa, Rodrigo Marchi b, LuizManoel Cunha da Silva a, Kleber Xavier Sampaio de Souza a,Leandro Henrique Mendonça de Oliveira a, Stanley Robson deMedeiros Oliveira a and Marcelo Augusto B Morandi ca Embrapa Agriculture Informatics – Campinas/SP -Brazil{silvia,luizm,kleber,leandro,stanley }@cnptia.embrapa.brb Scientific research scholarship granted by CNPq/Brazilrodrigo@cnptia.embrapa.brc Embrapa Environment – Jaguariúna/SP –Brazilmmorandi@cnpma.embrapa.brAbstractIn this paper, we show how the text mining approach can be used to extract knowledge fromunstructured data in texts. We assume that a text is a collection of unstructured documents with no specialrequirements. Initially, we use methods that process information contained in text to generate a numericalmatrix. Subsequently, data mining methods are applied to such a matrix to generate a classificationmodel. These mining methods have been proved to be efficient when applied to discovery knowledge intexts. In our approach, strictly low-level frequency information is used, such as the number of times that aword appears in a document, and then well-known machine learning methods are applied to the generatedmatrix to create inference rules. In our case study, we worked with 38 disorders that can occur in a cornplantation.Keywords: text mining, knowledge discovery, machine learning, predictions, corn diseases.1 IntroductionRecent survey of the Delphi group shows that 80% of the information available at organizations isrepresented by textual documents available in digital format as well as at the Web (Han & Kamber,2001). Such documents include emails, software logs, articles, among others. In particular, the BrazilianAgricultural Research Corporation (Embrapa) has an enormous repository of these documents in the areasof animal and vegetal diseases.To deal with animal and vegetal diseases, organizations have used diagnosis’ techniques, which haveplayed an important role in Artificial Intelligence. In particular, Diagnostic reasoning is a complexcognitive process that involves knowledge about a particular domain, heuristics about such a particulardomain, and constraints imposed by cognitive limitations of human diagnosticians. Diagnostic Systems,(e.g., Mycin) have been designed for several areas of expertise in medical diagnosis and treatment.Another example is Prospector, which was designed for “earth diagnosis” in view of mineral prospecting.Most reasoning frameworks have been based on the paradigm of inference that flows from consequencesto causes, as the ones employed in the systems mentioned previously.The reliability of a diagnostic expert system depends on the quantity and quality of knowledge that ithandles, i.e., the number of diseases it can diagnose and the appropriate knowledge representationconstructed by the domain expert. This can be achieved by the knowledge engineer with a knowledgeacquisition procedure. However, the knowledge acquisition is the bottleneck in the development of anyexpert system.Plant diseases can be classified according to: the symptoms they cause (cankers, root rots, leaf spots etc.),the plant organ they affect (root diseases, stem diseases, etc.), the type of plant they affect (vegetablediseases, fruit tree diseases etc.) and the type of pathogen or another causal factor of the disease. Diseasescan either be: infectious, as those caused by fungi, bacteria, viruses, etc, or non-infectious diseases ordisorders, as those caused by mineral toxicities, soil acidity, nutrient deficiencies, etc .In the diagnosis domain, the task performed by the expert can be thought of as a classification process, inwhich diseases are assigned to classes or categories determined by their properties. In a classificationmodel, the connection between classes and properties can either be defined by something simple, such aflow-chart, or complex such as the executable models represented by computer programs. In the last case,the classification models can be built in two ways. In the former, the model is obtained by interviewingthe relevant experts of the domain. In the latter, numerous recorded classifications are examined and amodel is constructed inductively, by generalization from specific examples.The first approach was adopted in the development of a preliminary version of an expert system fordiagnosis of corn diseases on the web (available at http://diagnose.cnptia.embrapa.br/milho). Wegenerated decision trees from the interviews with domain experts and resources from the literature in thecorn diseases area. After doing so, we built an expert system whose inference flows from theconsequences to the causes (Massruhá, 1999).In this paper, we show how the second approach (text mining techniques) can be used during theacquisition process to extract knowledge from unstructured data in texts (Massruhá, 2004). The text isusually a collection of unstructured documents with no special requirements. Our investigation started byusing methods that process information contained in text to generate a numerical matrix. Subsequently,data mining methods are applied to such a matrix to generate a classification model. These miningmethods have been proved to be efficient when applied to discovery knowledge in texts. Thus in ourapproach, strictly low-level frequency information is used, such as the number of times that a wordappears in a document, and then well-known machine learning methods are applied to the generatedmatrix to create inference rules.In our case study, we worked with 38 disorders that can occur in a corn plantation. This paper isorganized as follows: Section 2 describes the methodology used in this work. Section 3 presents a casestudy with corn diseases. Finally, Section 4 brings the results obtained so far as well as the future work inour research project.2 MethodologyBefore we introduce the methodology, we first define some concepts of the Knowledge Discovery fromTexts (KDT) process that are necessary to understand the issues addressed in this paper.  Fig. 1 shows anoverview of the process KDT.Fig. 1  - Process KDT (Knowledge Discovery in Text)The KDT process can be divided into three main phases: text or data preparation, information extractionand miner and validation. In the first phase, the documents are selected.  In the second phase, we use text-mining tools, techniques and algorithms to analyse the texts. In Fayyad (1996), the main objectives tominer texts or data are classified into: predictive or descriptive. The former aims to define the value of anew attribute (dependent attribute) from the other attributes available in dataset. In the latter, clusters aregenerated from texts. There are two main approaches to extract knowledge from unstructured data intexts: semantic and statistic analysis (Weiss et al, 2005). In this work, the task of mining texts ispredictive and we use the statistic analysis approach that is based on frequency of the terms in texts.Finally, in the last phase, we validate the results of this process using a test dataset.2.1 Data preparation phaseIn the data preparation phase, we analyzed some documents regarding plants diseases (soybean, corn,etc.). We then selected a document in Portuguese about corn diseases (Fernandes et al, 1999). We dividedthis document into 38 parts where each one represents a corn disease.We investigated six open-source text-mining tools in this research (ONDEX Suite, TextToOnto, txtKIT.TextMine, TSMK, RIKTEXT). In this work, we selected the tools Text-Miner Software Kit (TSMK) andInduction Kit for Text (RIKTEXT) Kit for predictive analysing to be used in the information extractionphase, because such tools are complementary and work together as described in Weiss et al (2005). In thefuture work, we intend to test another tools found in web and compare them.2.2 Information extraction and mining phaseIn this phase, we used the Text-Miner Software Kit (TMSK) and The Rule Induction Kit for Text(RIKTEXT). The TMSK is a comprehensive software package for predictive text mining. It includesroutines for pre-processing XML-based text documents and provides implementations of all the key tasksof text mining (Fig. 1). The RIKTEXT complements TMSK by providing methods for constructing andusing rules for document classification (Weiss et al, 2005). Both RIKTEXT and TMSK share the samedata format for vectors. In our case study, we used TMSK to prepare data for RIKTEXT.The information extraction process can be divided into two phases: training and test. We have to provideinput data to TMSK for analysis. The input data consist of: text documents to be analyzed, a list ofstopwords and a stem dictionary. Only the text documents are mandatory input; the list of stopwords andthe stem dictionary are optional. The documents are converted into a spreadsheet format where each rowcorresponds to a document, and each column corresponds to a word from a dictionary. Individual cells inthe spreadsheet are filled with the frequency counts (number of times that the word appears in thedocument). Typically, the number of words (columns) is very large and for a given document (row), whena word does not appear in the document the corresponding cell is filled with zero. Hence it is moreefficient to store only the information regarding non-zero cells (the cell number and its value). This is asparse vector form.Table 1.   presents a simple example of the sparse vector form that corresponds to a pre-processedcollection of documents.Spreadsheet Sparse Vectors Labeled Sparse Vectors0 15 0 3 (2,15) (4,3) 1 2@15 4@312 0 0 0 (1,12) 0 1@128 0 5 2 (1,8) (3,5) (4,2) 1 1@8 3@5 4@2Table 1.  An example of a sparse vector form corresponding to a pre-processed collection of documents.For training a classifier, we need labeled documents. A labeled document indicates which class such adocument belongs to. After obtaining the labeled documents, the next step is to build a model to classifythe unlabeled (new) documents. If a document is labeled, then the label appears by the side of thecorresponding vector. For instance, for the labeled sparse vector 1 2@15 4@3, the label is the number 1.Only binary labels are permitted – documents either belong to a class (label = 1) or not (label = 0). In avector file, all vectors must be of the same type (labeled or unlabeled). In the example showed inTable 1.  the second column (Sparse Vectors) corresponds to an unlabeled vector, while the third columncorresponds to a hypothetical labeled vector. The next step in this phase is to build a classification modelfrom the training dataset. In this point, we generated the bayes classifier from the labeled vector generatedby TSMK. For this, we used the routine nbayes of the TSMK tool.In the last step of this phase, we used the RIKTEXT to induce decision rules for categorizing documents.The RIKTEXT processes data in a format sparse vector. The RIKTEXT is a standalone program thatreads three files: a file containing labeled vectors for learning, another file containing the correspondingdictionary and a file containing labeled hidden test data (evaluation).The RIKTEXT is then used to determine the best set of rules for prediction and classification, where thebest is the smallest number of rules with a near minimum error. Note that RIKTEXT is always dealingdirectly with binary classification problems. It is created a positive class. The negative class is everythingelse. The output is usually an induced ruleset and is written to a standard output which can be redirectedto a file.2.3 The validation phaseAfter building a classification model, we move to validation phase. The validation is split into two steps.The former consists of taking each document in the test dataset and evaluating in which class it belongs toaccording to the classification model. In this step, we used the routine testnbayes. This routine applies anaive_bayes classifier to new documents and creates two files: one with positive predictions, the otherwith the negative predictions.  The latter consists of testing the ruleset generated by RIKTEXT against thetest dataset.3 A case study with corn diseasesIn Fig. 2 is shown an overview of the text-mining process used in our case study. Firstly, we divided thedocument about corn diseases into 38 sub-documents.  Each sub-document represents a corn disease andis divided into four parts: causal agent, symptoms, favorable conditions and control measures (Fig. 3).The corn diseases were classified into 3 groups: bacterial, fungal and viral diseases.Fig. 2 An overview of the text-mining process.Corn.TXTTransformationRIKTEXTConverterTXT  XMLCorn.XMLTMSKDictionaryStopwordsStopwordsDictionaryCorn_train.vecCorn_test.vecCorn_classifier.nbClassPosCornCorn.rulClassNegCornStem DictionaryData PreparationVectorsSparseClassifierTest N-BayesClassifierN-BayesDocumentsCornAfter that, we developed a tool to convert TXT to the XML format. In Fig. 3 is shown an example ofXML file generated from 38 files in TXT format.Fig. 3 An example in TXT and XML format.Afterwards, we created a list of stopwords (35 words). Thus, we generated the dictionary from the TMSKtool using such a list of stopwords and a stem dictionary in Portuguese (100 words). Subsequently, weprocessed the file in XML format to be analysed in the TMSK (Corn_28files_training.xml).We divided our dataset into two: training and test dataset. We used a set of 28 diseases to train theclassifier. We then generated the file in sparse vector format (corn_train.vec) . We used theclassifier to classify a set of 10 diseases unlabeled and the results are stored in the filecorn_test.vec.After that, we used the N-bayes classifier, whose input data was the file corn_train.vec, to generatethe file corn_classifier.nb. We then ran the routine testnbayes to apply the naïve-bayes classifier(corn_classifier.nb) to new documents and two files were created: one with positive predictions(classposcorn) and the other with negative predictions (classnegcorn). The positive cases correspond to acategory designated as “FUNGAL”. In positive predictions, the classifier included two wrong diseases: abacterial and a viral. In negative predictions, the classifier included one fungal disease. Thus, it obtained75 percent in the measures of precision and recall. Precision and recall are the most common measures ofsearch performance. Recall is the ratio of the number of relevant records retrieved to the total number ofrelevant records in the database. Precision is the ratio of the number of relevant records retrieved to thetotal number of irrelevant and relevant records retrieved. Both are usually expressed as a percentage(Weiis et al, 2005).In the last step, we used the RIKTEXT to induce decision rules for categorizing documents. TheRIKTEXT processed data in a format sparse vector (corn_train.vec and corn_test.vec).However, the results were not effective as we expected. The rules generated in the file corn.rul  weresimple as showed in Fig. 4.4 The Results and DiscussionIn this paper, we have applied the text mining technique to the acquisition process to extract knowledgefrom unstructured data in texts. In our case study, we worked with a knowledge base of 38 disorders thatcan occur in a corn plantation. This knowledge base was used in an expert system on the Web (availableat http://diagnose.cnptia.embrapa.br/milho). We compared the results of this work with those obtained inknowledge acquisition phase in the system previously constructed.Fig. 4 An example of rules (RIKTEXT).The TMSK and RIKTEXT are powerful tools for text mining designed for unstructured data in texts,although the interface of these tools is not friendly. We had to run the routines of these tools by usingcommand lines. The results obtained in the predictions were successful yielding 75% of the documents inthe test dataset.  However, the results to induce decision rules for categorizing documents were limited,i.e., at most 6 rules were generated for the dataset of 38 diseases. In the first version of the expert system,from the interviews with domain experts, were generated 44 rules for this knowledge base. We still needto test other text-mining tools to have a fair comparison.5 ReferencesHAN, J.; KAMBER, M. Data mining concepts and techniques . San Diego, CA: Academic, 2001. 550p.FAYYAD, U.; PIATESTSKY-SHAPIRO,G.;SMYTH,P. From data mining to knowledge discovery indatabases. AI Magazine,v.17, n.3, p. 37-54,1996.MASSRUHA, S. M. F. S.  Remote Diagnosis: An expert system for diagnosis of the corn diseases byweb. In: The inaugural Australian Workshop on the Application of Artificial Intelligence to Plantand Animal Production AWAPAP 99, 1999, Sidney. The inaugural Australian Workshop on theApplication of Artificial Intelligence to Plant and Animal Production AWAPAP 99, 1999.MASSRUHÁ, S.M.F.S. Incorporação de ferramentas inteligentes na Agência de InformaçãoEmbrapa. [Campinas: Embrapa Informática Agropecuária, 2004], 30p (Embrapa. Macro programa 3 –Desenvolvimento Tecnológico Incremental. Projeto) (In portuguese).Weiss S., Indurkhya N., Zhang T., Damerau F. Text mining: Predictive Methods for AnalyzingUnstructured Information.  New York: Springer (2005).

An approach based on text mining for knowledge acquisition in diagnostic systems.

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An approach based on text mining for knowledge acquisition in diagnostic systems.

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