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Folktale Similarity Based on Ontological Abstraction Folktale similarity based on ontological abstraction Marijn Schraagen Digital Humanities Lab, Utrecht University, The Netherlands <[email protected]> Abstract from Hansel & Gretel is ordered by the witch to This paper presents a method to compute do housework. In this case both the actors and similarity of folktales based on conceptual the actions are similar at various levels, depicted overlap at various levels of abstraction as in Figure 1. This notion of abstraction-based se- defined in Dutch WordNet. The method mantic similarity can be computed automatically is applied on a corpus of Dutch folktales using a machine-readable concept hierarchy such and evaluated using a comparison to tradi- as WordNet. The paper is structured as follows: tional folktale similarity analysis based on Section 2 describes characteristics of folktales and the Aarne–Thompson–Uther (ATU) clas- provides an overview of the resources used in the sification system. Document similarity analysis, Section 3 discusses related work, Sec- computed by the presented method is in tion 4 provides details on similarity computation, agreement with traditional analysis for a Section 5 contains experimental results and a com- certain amount of folktale pairs, but dif- parison to existing folktale analysis approaches, fers for other pairs. However, it can be ar- and Section 6 concludes. gued that the current approach computes Hansel & Gretel idea an alternative, data-driven type of similar- Snow White person action ity. Using WordNet instead of a domain- shared specific ontology or classification system female male persuade ensures applicability of the method out- Snow White Gretel witch dwarf order ask do housework side of the folktale domain. Figure 1: Example partial hierarchy showing con- 1 Introduction cepts from Snow White and Hansel & Gretel. A folktale is a specific type of narrative that is particularly suitable for analysis of semantic struc- ture. Although folktales may differ in various as- 2 Folktale similarity pects, such as the characteristics of the main ac- The folktale texts used in the current research tors or the sequence of events, often similarities are extracted from the Dutch Folktale Database can be identified on a more general or more ab- (Meder, 2010). This collection contains over stract level. In this paper similarity between folk- 40,000 folktales (including jokes, urban legends, tales is investigated using an explicit abstraction of etc.) from written and oral sources, in Dutch, text according to the WordNet concept hierarchy. Frisian and several contemporary and historical A comparison is provided to conventional folktale Dutch dialects1. The database is partially anno- motif analysis. An example of folktale similarity tated using Aarne-Thompson-Uther (ATU) codes on various levels of abstraction is provided by the (see the remainder of this section for details). The folktales Sleeping Beauty and Snow White, which database, maintained by the Meertens Institute, is both feature a princess as specific character and available for research purposes upon request. For a variable number of enchanted objects at a more the current research a pilot set of 16 traditional general level. Similarities regarding events occur fairy tales is used from a single original source at various levels as well, for example the princess (van Dongen and Grooten, 2009), with a total of in Snow White is asked by the seven dwarfs to per- form household tasks, whereas the girl protagonist 1http://www.verhalenbank.nl, in Dutch id description category ATU F451.5.1.2 Dwarfs adopt girl Marvels ! Spirits and demons 709 Snow White as sister ! Underground Spirits Q2 Kind & Unkind Rewards and punishments 440, 480, Frog King, Kind & Unkind Girls, 513, 571, . Wonderful Helpers, Golden Goose F911.3 Animal swallows man Marvels ! Extraordinary occurrences 123, 333, 700 The Wolf & the Seven Kids, (not fatally) ! extraordinary swallowings Little Red Riding Hood, Tom Thumb F913 Victims rescued from Marvels ! Extraordinary occurrences 123, 333, . The Wolf & the Seven Kids, swallower’s belly ! extraordinary swallowings Little Red Riding Hood J144 Well-trained kid The wise and the foolish ! Wisdom 123 The Wolf & the Seven Kids does not open to wolf aqcuisition ! education K1832 Disguise by changing voice Deceptions ! Deception by disguise 123 The Wolf & the Seven Kids Table 1: TMI motif examples. 33,022 words. This set provides folktales in gram- troduce semantic relatedness differences between matically correct, modern Dutch, which increases the two systems, for example the classification applicability of natural language processing tools of ATU 123 as Animal tales–Wild animals and and methods. Several folktales in this set do not domestic animals compared to ATU 333 which appear in the ATU catalog, illustrating the appli- is classified as Fairy tale–supernatural opponent, cability of the current method on non-traditional while two out of the total of four motifs of ATU folktale sources. 123 are also found in ATU 333 (see Table 1). In the current research the Dutch Cornetto database is used (Vossen et al., 2013) to obtain 3 Related work term abstractions. Cornetto is modeled after the Princeton WordNet, which is a widely used ontol- Folktale similarity using WordNet-based term ogy for English concepts (Fellbaum, 1998) con- matching has been previously investigated (McIn- taining a comprehensive set of terms and (hier- tyre and Lapata, 2010; Lestari and Manurung, archical) relations for an extensive variety of do- 2015) using the hierarchical similarity measure of mains. Concepts are organized in sets of (ap- Wu and Palmer (1994). In this approach a folktale proximate) synonyms, called synsets, which are is considered sequential, with similarity compu- connected by relations such as hypernymy and tation based on alignment of the sequence of ac- meronymy. Cornetto contains over 92,000 lem- tions and actors. In contrast, the current approach mas and is available under academic license2. considers the (non-sequential) presence of terms Traditionally, folktales are analyzed using the and term abstractions, similar to a bag-of-words Thompson Motif Index (TMI). This index is a set approach, while preserving event or situation sim- of over 12,000 story elements (motifs), classified ilarity by comparing folktales on a sentence level. in semantic categories and subcategories (Thomp- Abstraction based on Dutch WordNet for folktale son, 1960). Some examples are provided in Ta- similarity has been used by Nguyen et al. (2013), ble 1. The motifs in this index are often specific using abstractions of verbs as one of several fea- to a single folktale (or folktale type, i.e., the set tures involved in similarity computation. The ab- of variants of a story that are considered the same straction feature did not improve the results sig- folktale), however more general motifs are used as nificantly, which is attributed to limited coverage well. The Aarne–Thompson–Uther (ATU) clas- of the abstraction lexicon and inaccuracy of the sification system (Uther, 2004) describes a folk- grammatical analysis. tale type as a list of motifs (typically two or three Characterizing semantic relations between folk- to about 20) from a subset of nearly 1,900 ele- tales using TMI motifs is discussed by Karsdorp ments from the TMI, divided into thematic cate- et al. (2012), presenting the conclusion that motif- gories and subcategories. The ATU classification based methods suffer from the limited amount of is centered around the type of protagonists and the motif overlap between folktales. A search tool general theme of the folktale, while the TMI is for TMI motifs using WordNet based semantic centered around events and relations. This may in- abstraction is presented in (Karsdorp et al., 2015). A mapping of nominal phrases to folktale actors 2An open source database using the structure of Cornetto and translations of the content of English WordNet is avail- using a domain-specific ontology for term abstrac- able as an alternative (Postma and Vossen, 2014a). tion is described by Declerck et al. (2012). An unsupervised exploration and visualization semantic similarity focus either on pairs of con- method for concept clustering in folktales has cepts (or synsets, words, lemmas, etc.), document been proposed by Honkela (1997), using self- clusters, or, in the folktale setting, variants of the organizing maps trained on word trigrams. Natu- same story. These tasks are generally motivated ral computing approaches using (phylo)genetic al- by the availability of evaluation resources, such as gorithms are used to study variation within folk- human concept similarity ratings, the Reuters cat- tale types and between closely related types, using egorized news corpus, or folktale corpora tagged TMI motifs and other story elements as features by story type, respectively. In contrast, the current (Ross et al., 2013; Tehrani, 2013). A vector-based approach attempts to construct a network of docu- method for semantic folktale clustering using La- ments based on semantic relatedness, by compar- tent Semantic Mapping is described in (Vaz Lobo ing document pairs on (non-sequential) sentence and Martins de Matos, 2010). level. Evaluation of this approach is arguably less Several semantic relatedness measures that use straightforward, however this task and the pro- WordNet as knowledge base have been proposed, posed WordNet-based method provide a shift in see, e.g., (Pedersen et al., 2004) for an overview focus compared to traditional approaches. (although
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