Semi-Automatic Mapping of W ord N et to Basic Formal Ontology (2016)

Abstract: We present preliminary work on the mapping of WordNet 3.0 to the Basic Formal Ontology (BFO 2.0). WordNet is a large, widely used semantic network. BFO is a domain-neutral upper-level ontology that represents the types of things that exist in the world and relations between them. BFO serves as an integration hub for more specific ontologies, such as the Ontology for Biomedical Investigations (OBI) and Ontology for Biobanking (OBIB). This work aims at creating a lexico-semantic resource that can be used in NLP tools to perform ontology-related text manipulation tasks. Our semi-automatic mapping method consists in using existing mappings between WordNet and the KYOTO Ontology. The latter allows machines to reason over texts by providing interpretations of the words in ontological terms. Our working hypothesis is that a large portion of WordNet synsets can be semi-automatically mapped to BFO using simple mapping rules from KYOTO to BFO. We evaluate the method on a randomized subset of synsets, examine preliminary results, and discuss challenges related to the method. We conclude with suggestions for future work.

## introduction ontologies are often used in combination with natural language processing (nlp) tools to carry out ontology-related text manipulation tasks such as automatic annotation of biomedical texts with ontology terms. these tasks involve categorizing relevant terms from texts under the appropriate categories. this requires coupling ontologies with lexical resources. several projects have realized these kinds of mappings with upper-level ontologies that are extended by domain-specific ontologies (?; @xcite @xcite . however, no such resource is available for the basic formal ontology (bfo), which is widely used in the biomedical domain. we describe and evaluate a semi-automatic method for mapping the large lexical network wordnet 3.0 (wn) to bfo 2.0 exploiting an existing mapping between wn and the kyoto ontology (hereafter 'kyoto'). our hypothesis is that a large portion of wn, primarily nouns and verbs, can be semi-automatically mapped to bfo 2.0 types by means of simple mapping rules exploiting the kyoto ontology. in section 2, we give a brief overview of the ontological and lexical resources involved in the task: bfo, wn, and kyoto. in section 3, we motivate and describe our methodology. in section 4, we evaluate the method and present preliminary results. in section 5, we discuss the major challenges related to this task. we conclude with suggestions for future work. ## ontological and lexical resources the mapping methodology described below, in section 3, takes as input wordnet 3.0, which is mapped to the kyoto 3 top ontology by way of kyoto 3 middle. kyoto 3 top is an extension of the descriptive ontology for linguistic and cognitive engineering (dolce-lite-plus, version 3.9.7). the kyoto 3 top ontology was extended to a middle level ontology kyoto 3 middle by manually mapping base concepts automatically generated from wn @xcite . we use those existing mappings to create mapping rules from kyoto to bfo 2.0. hereafter, we briefly describe each of these ontological and lexical resources. we briefly present the aspects of bfo, wn, and the kyoto ontology that are relevant to this work. for more details, see the cited references. the basic formal ontology (bfo) is a domain-neutral upper-level ontology @xcite @xcite . it represents the types of things that exist in the world and relations between them. bfo serves as an integration hub for mid-level and domain-specific ontologies, such as the ontology for biomedical investigations (obi) and ontology for biobanking (obib). it is widely used in biomedical and other domain-specific ontologies, foot_0 which thus become interoperable @xcite . bfo is subdivided into continuants (e.g., ob-jects and functions) and occurrents (e.g., processes and events). continuants can be either independent (e.g., physical objects like persons and hearts) or dependent (e.g., the role of a person as a physician and the function of a heart to pump blood). the most recent version, bfo 2.0, represents 35 types to which previous versions (bfo 1.0 and bfo 1.1) have been mapped in @xcite . wordnet 3.0 is a large lexical network linking over 117000 sets of synonymous english words (synsets) by means of semantic relations; it is widely used in nlp tasks @xcite . noun and verb synsets are linked via the hypernym relation. foot_1 wn 3.0 distinguishes between types and instances, meaning named entities. it also links a subset of synsets to topic domains (e.g., 'medicine') and semantic labels (e.g., 'noun.artifact'). the kyoto ontology is part of a project aimed at representing domain-specific terms in a computer-tractable axiomatized formalism to allow machines to reason over texts in natural language @xcite . it links wordnets of different languages to ontology classes, on the basis of a mapping of the english wn to ky-oto. the approximately 2000 classes of ky-oto are subdivided into three layers: (1) the top-most layer is based on dolce-lite-plus. docle shares a number of relevant characteristics with bfo: domain neutrality; bi-partition into 'endurants' (continuants) and 'perdurants' (occurrents); strict hierarchical is a taxonomy; distinction between independent and dependent entities. (2) the second layer is composed of noun and verb synsets constituting a set of base concepts (bcs) as well as some adjectives or qualities. (3) the third layer contains domain-specific classes (e.g., from the environmental domain) and some corresponding synsets. ## mapping method our semi-automatic mapping method involves three main steps: the rules map to bfo 2.0 leaf types or, when bfo has no leaf-level type to represent the referent of a synset, to intermediary types (e.g., material entity, the direct parent of three leaf types). the implementation first lists all kyoto types that subsume or otherwise characterize a wn synset using the wn-kyoto mapping data files. 4 we only retained types related to the synsets through equivalence and subclass for example, the synset immunity.n.02 is linked to the following types: second, the mapping rules are applied starting from the more specific type in the types list: the program tests whether a given string on the left-hand side of the rule (e.g., '#non-agentive-social-object') matches a string in the types list; if the strings match, the program assigns to that synset the corresponding bfo 2.0 type (e.g., 'disposition'). thus, the synset immunity.n.02 is categorized as referring to a subtype of the bfo type disposition. ## evaluation and results in a first step, we evaluated the method on the 106 synsets in kyoto marked with a 'medicine' topic domain @xcite . the aim in this first step was to get a rough idea of the feasibility of the method, the results that could be expected, and the possible challenges. the medicine gold standard was created collectively by a bfo developer and a bfo expert. the experts followed an intuitive categorization criterion: assign the most specific bfo type of which the referent of the synset is a subtype. following this principle, for each synset we may obtain a statement of the form "the wn synset x refers to a subtype of the (leaf) bfo type y". for example, "the wn synset immunity.n.02 refers to a subtype of the bfo type disposition". this task revealed difficult interpretation issues related to adjectives. 71.7% of the assigned bfo types were correct (63.2% of the synsets were assigned the expected bfo type; 8.5% a superclass). as hypothesized, all the correctly categorized synsets were nominal and verbal. 27.4% of the assigned bfo types were incorrect (mostly adjectives). one synset was not matched by any rule. in a second step, we focused on nouns and verbs, and left adjectives for future work. after examining the erroneous cases in the first evaluation, we created a new ruleset, which we tested on a new randomly extracted sample of 100 nouns and 100 verbs (hereafter the 'pos-sample'). to create the corresponding gold standard, two of the authors, experts of bfo, first pre-annotated the pos-sample independently. they followed the same intuitive annotation criteria as with the medicine gold standard. the annotations were compared and the synsets separated into 'easy cases' (where both annotators agreed) and 'difficult cases' (in case of disagreement), respectively 101/200 and 99/200 synsets. second, two bfo developers (annotators a & b) independently (i) reviewed the easy cases for validation and (ii) annotated the difficult cases. they were asked to apply the same intuitive annotation criteria as in previous steps. the annotators agreed on 2/3 of the latter sample. finally, annotator b examined the cases on which they disagreed and decided on the final bfo type to assign considering the comments left by annotator a. some difficult cases were collectively discussed to reach consensus. we discuss the challenging cases in section 5. the baseline was created by a bfo developer and discussed with a bfo expert to resolve a few problematic cases (see, for example, section 5.5). the baseline mapping rules map, whenever possible, wn's top-level nouns to lowest level bfo 2.0 types and all verbs to bfo process. in the following, we review the main results of our mappings. we limit the evaluation of the medicine sample to nouns and verbs to allow for comparisons with the performance of the rules on the pos-sample. figure 1 shows the overall performance of the first and the new rulesets compared to the baseline when applied to the medicine nouns and verbs sample. a total of 85% of the sample's synsets were correctly mapped with the new ruleset, which is considerably better than with the baseline rules (76%) and the first ruleset (77%). in the baseline and the first experiment, respectively 9% and 6% of the synsets were mapped to a parent bfo type (see 'partial' row). there were no occurrences of partial mapping with the new ruleset. we did indeed correct some mapping rules with lowerlevel bfo types. however, assigning lower-level bfo types cannot be done with all of wn's top level categories. this means that with the baseline mapping method there will always be synsets that are not assigned an adequate (lowest level possible) bfo type. the percentage of incorrect mappings is steady across the applied mapping rules, around 15%. the proportion of synsets for which no rule was able to output a mapping is 1% for the kyoto-based rules. as the baseline mapping rules were propagated all the way down the wn hierarchy, there are no such cases. these results show that the performance of the kyoto-based rulesets applied to the medicine nouns and verbs sample is (i) comparable to that of the baseline with the first ruleset and (ii) better with the new ruleset. while rather unsurprising, since the new ruleset was tuned on the medicine sample, this result nevertheless suggests that developing a more sophisticated mapping method, the kyoto-based method, has advantages over a simple mapping of wn's top levels to bfo types. figure 2 shows the results of our second evaluation on a randomly extracted sample of 100 noun and 100 verb synsets. the overall performance of the new ruleset on the pos-sample is lower than that of the baseline ruleset, respectively 64% and 70%. however, the baseline ruleset itself performs lower here than in the medicine sample. moreover, a closer look at the mappings reveals that while the new ruleset introduced some errors and non-matches, it also has the advantage of avoiding partial matches (when a synset is tagged with a superordinate bfo type instead of the lowest possible type). in 16 cases, the partial matches in the baseline correspond to the bfo type entity, which is the uppermost level of the ontology and not relevant for a resource mapping wn to bfo. 5 with the baseline method, these cases could only be manually resolved; with the kyoto-based method, we can test new rules to capture the incorrectly mapped cases (11/15 synsets) and the cases that were not mapped at all (3/15 synsets). moreover, the analysis of these new results reveals useful information for improving the wn-bfo mapping method. a notable example is the case of verb synsets: the baseline rules systematically mapped them to the bfo type process. this yielded only one error due to the fact that the erroneously mapped synset does actually not refer to any bfo type (see the discussion in section 5.3). this suggests that the kyoto-based rules can be improved with this verb mapping rule. to test this hypothesis, we performed a supplementary test. figure 3 shows the prospective performance of the new ruleset complemented with the verb mapping rule on the pos-sample. the overall performance rose from 64% to 70.5%, slightly higher than that of the baseline. however, the performance for verbs reached 99% (from 86% with the ruleset). the results further show that the performance for verbs was higher than that for nouns. indeed, nouns refer to a large array of entity types (to 10 bfo types in the pos-sample). although the new ruleset did include rules for all the expected bfo types in the gold standard (as checked after the experiment), it did not capture the correct types in the kyoto-types lists associated to the synsets. we suspect that the issue might be related to the ordering of the rules. finally, the results show that a total of 12 synsets (6%) were not mapped to bfo at all. this means that none of the rules was matched to the kyoto-types lists associated with these synsets. these cases covered mostly deverbal nouns (8/12 synsets) that should be mapped to process. further work is needed on the unmapped cases. to summarize, we make the following general observations. the results thus show that the kyoto-based rules can be successfully complemented with a baseline rule that consists in systematically mapping verb synsets to bfo processes. ## conclusion and future work we presented a method to semi-automatically map wordnet 3.0 synsets to bfo 2.0 types via the ky-oto ontology. our preliminary results are encouraging, but reveal a number of challenges as addressed in the discussion section. more work is thus needed to see if the method scales to the full wn. future work will include: