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  • Evaluating Evaluation Metrics for Ontology-Based Applications: InfiniteReflection

    Diana Maynard, Wim Peters, Yaoyong Li

    Dept of Computer Science, University of Sheffield, Sheffield, UK {diana,wim,y.li}@dcs.shef.ac.ukAbstract

    In this paper, we discuss methods of measuring the performance of ontology-based information extraction systems. We focus particularlyon the Balanced Distance Metric (BDM), a new metric we have proposed which aims to take into account the more flexible nature ofontologically-based applications. We first examine why traditional Precision and Recall metrics, as used for flat information extractiontasks, are inadequate when dealing with ontologies. We then describe the Balanced Distance Metric (BDM) which takes ontologicalsimilarity into account. Finally, we discuss a range of experiments designed to test the accuracy and usefulness of the BDM whencompared with traditional metrics and with a standard distance-based metric.

    1. IntroductionTraditionally, applications such as information extraction(IE) are evaluated using Precision, Recall and F-Measure.These metrics give us a binary decision of correctness foreach entity in the text, i.e. by comparing the key (goldstandard) and system responses, they classify the result aseither right or wrong in each case. Ontology-based infor-mation extraction systems attempt to classify entities in amore scalar fashion, as there are many different categoriesto which an entity can be assigned, and the distinction be-tween these categories is much less clearcut. Traditionalentity classes do not subsume each other, whereas in an on-tology, there are subclass and superclass categories to con-sider, so the distinction between right and wrong is moreblurred. In traditional IE, an element identified as a Personis either correct or incorrect (measured by Precision), andelements which should be identified as Person are eitheridentified or not (measured by Recall). When making anontological classification, however, the distinction is morefuzzy. For example if we misclassify an instance of a Re-searcher as a Lecturer, we are clearly less wrong than miss-ing the identification (and classification) altogether, and weare also somehow less wrong than if we had misclassi-fied the instance as a Location. Credit should therefore begiven for partial correctness. Traditionally, this is some-times achieved by allocating a half weight to somethingdeemed partially correct, but this is still insufficient to givea proper distinction between degrees of correctness. Wetherefore adopt an approach based on similarity betweenKey (the gold standard) and Response (the output of thesystem), known as BDM (Maynard, 2005; Maynard et al.,2006).In this paper, we aim to evaluate how useful the BDM is asa measure of the performance of an ontology-based Infor-mation Extraction (OBIE) system. Of course, how to eval-uate an evaluation metric is not obvious, but there are somegeneral guidelines proposed by (King, 2003) for evaluationmetrics that we try to follow. A metric should:

    reach its highest value for perfect quality;

    reach its lowest value for worst possible quality;

    be monotonic;

    be clear and intuitive;

    correlate well with human judgement;

    be reliable and exhibit as little variance as possible;

    be cheap to set up and apply;

    be automatic.

    We aim in this paper to show how the BDM fulfils thesecriteria, describing some experiments we have carried outto investigate its validity.

    2. A Distance-Based Metric for EvaluationAs discussed in Section 1., a metric which classifies thecorrectness of an answer based on its semantic proximityto the real answer should give us a fairer indication of theperformance of the system. Other existing cost-based ordistance-based metrics, such as Learning Accuracy (LA)(Hahn and Schnattinger, 1998), have some flaws such asnot taking into account the density of the hierarchy, andin the case of LA, being asymmetrical. By this we meanthat comparing two concepts in the ontology gives differentresults depending on which one is the Key and which isthe Result. Given that we trying to compute the similaritybetween two concepts, it seems rather odd and unintuitivethat this should be the case.The BDM computes semantic similarity between two an-notations of the same token in a document. The metrichas been designed to replace the traditional exact matchor fail metrics with a method which yields a graded cor-rectness score by taking into account the semantic distancein the ontological hierarchy between the compared nodes(Key and Response). The final version of the BDM is animproved version of the original BDM described in (May-nard, 2005), which did not take the branching factor intoaccount (as described below).The BDM is computed on the basis of the following mea-surements:

    CP = the shortest length from root to the most spe-cific common parent, i.e. the most specific ontologicalnode subsuming both Key and Response)

  • DPK = shortest length from the most specific commonparent to the Key concept

    DPR = shortest length from the most specific commonparent to the Response concept

    n1: average chain length of all ontological chains con-taining Key and Response.

    n2: average chain length of all ontological chains con-taining Key.

    n3: average chain length of all ontological chains con-taining Response.

    BR: the branching factor of each relevant concept, di-vided by the average branching factor of all the nodesfrom the ontology, excluding leaf nodes.

    The complete BDM formula is as follows:

    BDM =BR(CP/n1)

    BR(CP/n1) + (DPK/n2) + (DPR/n3)(1)

    The BDM itself is not sufficient to evaluate our populatedontology, because we need to preserve the useful proper-ties of the standard Precision and Recall scoring metric.Our APR metric (Augmented Precision and Recall) com-bines the traditional Precision and Recall with a cost-basedcomponent (namely the BDM). We thus combine the BDMscores for each instance in the corpus, to produce Aug-mented Precision, Recall and F-measure scores for the an-notated corpus, calculated as follows:

    AP =BDM

    n + Spuriousand AR =

    BDM

    n + Missing(2)

    while F-measure is calculated from Augmented Precisionand Recall as:

    F measure = AP AR0.5 (AP + AR)

    (3)

    3. Evaluation of Ontology-BasedInformation Extraction Metrics

    We performed various experiments to test the validity of theBDM metric. First, we compared it with two other metrics:a flat metric and another similarity-based metric, the LA.We also looked at how it performed on two different learn-ing algorithms for IE: one which is based on a flat classifi-cation (SVN) and one which is based on a hierarchical clas-sification (Hieron). Our second experiment compared twodifferent IE systems (GATE and KIM) using Precision andRecall, and the APR, in order to see how they two systemscompared using the two different metrics. The idea behindthis was to see if using BDM revealed any differences be-tween the two systems that traditional metrics did not. Thethird experiment looked specifically at the scalability of themetric and how it performed on different densities and sizesof ontology.For the evaluations, we used the semantically annotatedOntoNews corpus (Peters et al., 2005) as a gold standard.

    This consists of 292 news articles from three news agencies(The Guardian, The Independent and The Financial Times),and covers the period of August to October, 2001. The ar-ticles belong to three general topics or domains of newsgathering: International politics, UK politics and Business.The ontology used in the generation of the ontological an-notation process was the PROTON ontology1, which hasbeen created and used in the scope of the KIM platform2

    for semantic annotation, indexing, and retrieval (Kiryakovet al., 2004). The ontology consists of around 250 classesand 100 properties (such as partOf, locatedIn, hasMemberand so on). PROTON has a number of important proper-ties: it is domain-independent, and therefore suitable forthe news domain, and it is modular (comprising both a topontology and a more specific ontology).

    3.1. Experiments with OBIEThe aim of the first set of experiments was, on the one hand,to evaluate a new learning algorithm for OBIE, and, onthe other hand, to compare the different evaluation metrics(LA, flat traditional measure, and the BDM).The OBIE algorithm learns a Perceptron classifier for eachconcept in the ontology. Perceptron (Rosenblatt, 1958) isa simple yet effective machine learning algorithm, whichforms the basis of most on-line learning algorithms. Mean-while, the algorithm tries to keep the difference betweentwo classifiers proportional to the cost of their correspond-ing concepts in the ontology. In other words, the learningalgorithm tries to classify an instance as correctly as it can.If it cannot classify the instance correctly, it then tries toclassify it with another concept with the least cost associ-ated with it relative to the correct concept. The algorithmis based on the Hieron, a large margin algorithm for hier-archical classification proposed in (Dekel et al., 2004). See(Li et al., 2006) for details about the learning algorithm andexperiments.We experimentally compared the Hieron algorithm with theSVM learning algorithm (see e.g. (Cristianini and Shawe-Taylor, 2000)) for OBIE. The SVM is a state of the art al-gorithm for classification. (Li et al., 2005) applied SVMwith uneven margins, a variant of SVM, to the traditionalinformation extraction problem and achieved state of theart results on several benchmarking corpora. In the appli-cation of SVM to OBIE, we learned one SVM classifier foreach concept in the ontology separately and did not takeinto account the structure of the ontology. In other words,the SVM-based IE learning algorithm was a flat classifica-t

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