Folktale similarity based on ontological abstraction (2016)

Abstract: This paper presents a method to compute similarity of folktales based on conceptual overlap at various levels of abstraction as defined in Dutch WordNet. The method is applied on a corpus of Dutch folktales and evaluated using a comparison to traditional folktale similarity analysis based on the Aarne–Thompson–Uther (ATU) classification system. Document similarity computed by the presented method is in agreement with traditional analysis for a certain amount of folktale pairs, but differs for other pairs. However, it can be argued that the current approach computes an alternative, data-driven type of similarity. Using WordNet instead of a domain-specific ontology or classification system ensures applicability of the method outside of the folktale domain.

## introduction a folktale is a specific type of narrative that is particularly suitable for analysis of semantic structure. although folktales may differ in various aspects, such as the characteristics of the main actors or the sequence of events, often similarities can be identified on a more general or more abstract level. in this paper similarity between folktales is investigated using an explicit abstraction of text according to the wordnet concept hierarchy. a comparison is provided to conventional folktale motif analysis. an example of folktale similarity on various levels of abstraction is provided by the folktales sleeping beauty and snow white, which both feature a princess as specific character and a variable number of enchanted objects at a more general level. similarities regarding events occur at various levels as well, for example the princess in snow white is asked by the seven dwarfs to perform household tasks, whereas the girl protagonist from hansel & gretel is ordered by the witch to do housework. in this case both the actors and the actions are similar at various levels, depicted in figure 1 . this notion of abstraction-based semantic similarity can be computed automatically using a machine-readable concept hierarchy such as wordnet. the paper is structured as follows: section 2 describes characteristics of folktales and provides an overview of the resources used in the analysis, section 3 discusses related work, section 4 provides details on similarity computation, section 5 contains experimental results and a comparison to existing folktale analysis approaches, and section 6 concludes. ## folktale similarity the folktale texts used in the current research are extracted from the dutch folktale database @xcite . this collection contains over 40,000 folktales (including jokes, urban legends, etc.) from written and oral sources, in dutch, frisian and several contemporary and historical dutch dialects foot_0 . the database is partially annotated using aarne-thompson-uther (atu) codes (see the remainder of this section for details). the database, maintained by the meertens institute, is available for research purposes upon request. for the current research a pilot set of 16 traditional fairy tales is used from a single original source @xcite , with a total of in the current research the dutch cornetto database is used @xcite to obtain term abstractions. cornetto is modeled after the princeton wordnet, which is a widely used ontology for english concepts @xcite containing a comprehensive set of terms and (hierarchical) relations for an extensive variety of domains. concepts are organized in sets of (approximate) synonyms, called synsets, which are connected by relations such as hypernymy and meronymy. cornetto contains over 92,000 lemmas and is available under academic license 2 . traditionally, folktales are analyzed using the thompson motif index (tmi). this index is a set of over 12,000 story elements (motifs), classified in semantic categories and subcategories @xcite . some examples are provided in ## related work folktale similarity using wordnet-based term matching has been previously investigated @xcite using the hierarchical similarity measure of @xcite . in this approach a folktale is considered sequential, with similarity computation based on alignment of the sequence of actions and actors. in contrast, the current approach considers the (non-sequential) presence of terms and term abstractions, similar to a bag-of-words approach, while preserving event or situation similarity by comparing folktales on a sentence level. abstraction based on dutch wordnet for folktale similarity has been used by @xcite , using abstractions of verbs as one of several features involved in similarity computation. the abstraction feature did not improve the results significantly, which is attributed to limited coverage of the abstraction lexicon and inaccuracy of the grammatical analysis. characterizing semantic relations between folktales using tmi motifs is discussed by @xcite , presenting the conclusion that motifbased methods suffer from the limited amount of motif overlap between folktales. a search tool for tmi motifs using wordnet based semantic abstraction is presented in @xcite . a mapping of nominal phrases to folktale actors using a domain-specific ontology for term abstraction is described by @xcite . an unsupervised exploration and visualization method for concept clustering in folktales has been proposed by @xcite , using selforganizing maps trained on word trigrams. natural computing approaches using (phylo)genetic algorithms are used to study variation within folktale types and between closely related types, using tmi motifs and other story elements as features @xcite . a vector-based method for semantic folktale clustering using latent semantic mapping is described in (vaz lobo and martins de matos, 2010). several semantic relatedness measures that use wordnet as knowledge base have been proposed, see, e.g., @xcite for an overview (although the use of wordnet as an ontological resource can be criticized @xcite ). considering hierarchy traversal, well-known approaches include the wu-palmer measure mentioned above, which defines similarity between two nodes as the path length from the first shared parent node to the root node of the hierarchy, and the leacock-chodorow measure, which finds the shortest path between two concepts (scaled for specificity of the hierarchy). further graph-topological information is incorporated using pagerank @xcite . evaluation of graph-based semantic relatedness measures has been performed using comparison to human word-pair similarity ratings, e.g., @xcite . recent approaches of similarity computation include path length weighting strategies @xcite and domain-specific data @xcite . using wordnet for similarity of documents has been investigated by, e.g., @xcite for the task of document clustering. these approaches represent a document as a bag-of-words, consisting of terms in the document as well as term synonyms and hypernyms from wordnet. however, it is concluded that the investigated approach of adding wordnet relations does not improve clustering results significantly. similar methods for document clustering do show improved results, e.g., @xcite , suggesting a large impact of preprocessing and sense selection procedures. further applications include information retrieval, matching a wordnet-expanded query to a set of (nonexpanded) documents @xcite . note that many approaches using wordnet for semantic similarity focus either on pairs of concepts (or synsets, words, lemmas, etc.), document clusters, or, in the folktale setting, variants of the same story. these tasks are generally motivated by the availability of evaluation resources, such as human concept similarity ratings, the reuters categorized news corpus, or folktale corpora tagged by story type, respectively. in contrast, the current approach attempts to construct a network of documents based on semantic relatedness, by comparing document pairs on (non-sequential) sentence level. evaluation of this approach is arguably less straightforward, however this task and the proposed wordnet-based method provide a shift in focus compared to traditional approaches. ## method in the current approach a document collection is compared at sentence level. first, sentence boundaries, lemmas and part-of-speech tags are obtained using the frog toolkit @xcite . lemmas tagged as noun (including proper names), adjective, or verb are selected (except for the common verbs be, have, can and will). the set of lemmas for a sentence is compared to the set of lemmas for all other sentences in all other folktales in the corpus. if a matching lemma is not found in the compared sentence then the wordnet hierarchy is consulted for a match at a higher level of abstraction (i.e., the lowest common subsumer), using the match level to adjust the similarity score. the similarity of two sentences is computed as the total of all match scores relative to the combined size of the lemma sets. formally, the score @xmath0. after computing similarity scores for all sentence pairs, for each (ordered) folktale combination (f a , f b ) the relative number of sentences in f a is counted for which the most similar sentence in the corpus originated from f b . the procedure is described formally in algorithm 1, an example is provided in figure 2 . for the mapping of sentence lemmas to wordnet the synset with the lowest wordnet sense number is selected, corresponding to some extent to the 'default' sense. incorrect senses are assumed to be related or to have a minimal effect given the document size (cf. ( hotho et al the distance measure applied in the current research uses elements from both wu-palmer and leacock-chodorow (see section 3), by measuring the distance from a source synset to the first shared parent node. as a comparison, on the correlation values. the overall values are somewhat lower than the correlation for the hierarchy traversal measures reported by @xcite on dutch wordnet, which might be caused by the lack of hierarchy depth awareness of the current method. however, the current measure is not intended as a stand-alone word pair similarity computation, instead it is part of an asymmetrical sentence matching procedure intentionally designed for matching on any level of the hierarchy. ## results and evaluation application of the current method on the folktale test corpus results in a matrix of pair-wise directed similarity scores, shown in increasing the similarity threshold to 13.0 (solid lines in figure 3 ) reveals two clusters in the graph. the left cluster contains folktales featuring civilian protagonists, who find themselves in potentially harmful circumstances. the right cluster contains royal protagonists dealing with issues of moral values. the exception is snow white, which has a royal protagonist, who is however banned from the royal court, living as a civilian house guest annex maid, and subject of murder attempts. for comparison, the same method is applied without using wordnet abstractions, i.e., counting overlap in (lemmatized) terms as found in the text. this comparison (see figure 4 ) shows that plain term overlap is less structured or partitioned in general and pair-wise relations display less topic overlap as compared to the abstraction method. to provide an evaluation of the proposed similarity measure, a comparison is performed to the traditional atu classification and associated tmi motif sets. to address the problem of limited motif overlap between folktales, the hierarchical tmi numbering system can be used for partial or abstract motif overlap using an approach similar to the term similarity computation described in section 4 (see sleeping beauty speaking horsehead snow white golden tuning fork chinese nightingale wonderful helpers gardener & fakir golden goose red shoes kind & unkind girls wolf & seven kids frog king hansel & gretel waterlilies red riding hood table, ass & stick be aggregated in different ways, e.g., the number of overlapping motifs, the sum of the highest match levels for overlapping motifs, the number of matches at any level (in this case the motifs k1832 and k1839.1 from