In this paper, we analyse and compare several correction methods of knowledge resources with the purpose of improving the abilities of systems that require commonsense reasoning with the least possible human-effort.To this end, we cross-check the WordNet meronymy relation member against the knowledge encoded in a SUMO-based first-order logic ontology on the basis of the mapping between WordNet and SUMO.In particular, we focus on the knowledge in WordNet regarding the taxonomy of animals and plants.Despite being created manually, these knowledge resources -WordNet, SUMO and their mapping-are not free of errors and discrepancies.Thus, we propose three correction methods by semi-automatically improving the alignment between WordNet and SUMO, by performing some few corrections in SUMO and by combining the above two strategies.The evaluation of each method includes the required human-effort and the achieved improvement on unseen data from the WebChild project, that is tested using first-order logic automated theorem provers.

Language Models are the core for almost any Natural Language Processing system nowadays.One of their particularities is their contextualized representations, a game changer feature when a disambiguation between word senses is necessary.In this paper we aim to explore to what extent language models are capable of discerning among senses at inference time.We performed this analysis by prompting commonly used Languages Models such as BERT or RoBERTa to perform the task of Word Sense Disambiguation (WSD).We leverage the relation between word senses and domains, and cast WSD as a textual entailment problem, where the different hypothesis refer to the domains of the word senses.Our results show that this approach is indeed effective, close to supervised systems.

This paper presents the integration of Word-Net knowledge resource into ClinIDMap tool, which aims to map identifiers between clinical ontologies and lexical resources.ClinIDMap interlinks identifiers from UMLS, SMOMED-CT, ICD-10 and the corresponding Wikidata and Wikipedia articles for concepts from the UMLS Metathesaurus.The main goal of the tool is to provide semantic interoperability across the clinical concepts from various knowledge bases.As a side effect, the mapping enriches already annotated medical corpora in multiple languages with new labels.In this new release, we add WordNet 3.0 and 3.1 synsets using the available mappings through Wikidata.Thanks to cross-lingual links in MCR we also include the corresponding synsets in other languages and also, extend further ClinIDMap with different domain information.Finally, the final resource helps in the task of enriching of already annotated clinical corpora with additional semantic annotations.

In this paper we present a system that exploits different pre-trained Language Models for assigning domain labels to WordNet synsets without any kind of supervision.Furthermore, the system is not restricted to use a particular set of domain labels.We exploit the knowledge encoded within different off-theshelf pre-trained Language Models and task formulations to infer the domain label of a particular WordNet definition.The proposed zero-shot system achieves a new state-of-theart on the English dataset used in the evaluation.

We describe a detailed analysis of a sample of large benchmark of commonsense reasoning problems that has been automatically obtained from WordNet, SUMO and their mapping.The objective is to provide a better assessment of the quality of both the benchmark and the involved knowledge resources for advanced commonsense reasoning tasks.By means of this analysis, we are able to detect some knowledge misalignments, mapping errors and lack of knowledge and resources.Our final objective is the extraction of some guidelines towards a better exploitation of this commonsense knowledge framework by the improvement of the included resources.

We describe the practical application of a black-box testing methodology for the validation of the knowledge encoded in WordNet, SUMO and their mapping by using automated theorem provers.In this paper, we concentrate on the part-whole information provided by WordNet and create a large set of tests on the basis of few question patterns.From our preliminary evaluation results, we report on some of the detected inconsistencies.

This paper presents the Event and Implied Situation Ontology (ESO), a resource which formalizes the pre and post situations of events and the roles of the entities affected by an event.The ontology reuses and maps across existing resources such as WordNet, SUMO, VerbNet, Prop-Bank and FrameNet.We describe how ESO is injected into a new version of the Predicate Matrix and illustrate how these resources are used to detect information in large document collections that otherwise would have remained implicit.The model targets interpretations of situations rather than the semantics of verbs per se.The event is interpreted as a situation using RDF taking all event components into account.Hence, the ontology and the linked resources need to be considered from the perspective of this interpretation model.

We introduce a new problem, identifying the type of relation that holds between a pair of similar items in a digital library. Being able to provide a reason why items are similar has applications in recommendation, personalization, and search. We investigate the problem within the context of Europeana, a large digital library containing items related to cultural heritage. A range of types of similarity in this collection were identified. A set of 1,500 pairs of items from the collection were annotated using crowdsourcing. A high intertagger agreement (average 71.5 Pearson correlation) was obtained and demonstrates that the task is well defined. We also present several approaches to automatically identifying the type of similarity. The best system applies linear regression and achieves a mean Pearson correlation of 71.3, close to human performance. The problem formulation and data set described here were used in a public evaluation exercise, the *SEM shared task on Semantic Textual Similarity. The task attracted the participation of 6 teams, who submitted 14 system runs. All annotations, evaluation scripts, and system runs are freely available.

We introduce a new framework to evaluate and improve first-order (FO) ontologies using automated theorem provers (ATPs) on the basis of competency questions (CQs). Our framework includes both the adaptation of a methodology for evaluating ontologies to the framework of first-order logic and a new set of non-trivial CQs designed to evaluate FO versions of SUMO, which significantly extends the very small set of CQs proposed in the literature. Most of these new CQs have been automatically generated from a small set of patterns and the mapping of WordNet to SUMO. Applying our framework, we demonstrate that Adimen-SUMO v2.2 outperforms TPTP-SUMO. In addition, using the feedback provided by ATPs we have set an improved version of Adimen-SUMO (v2.4). This new version outperforms the previous ones in terms of competency. For instance, "Humans can reason" is automatically inferred from Adimen-SUMO v2.4, while it is neither deducible from TPTP-SUMO nor Adimen-SUMO v2.2.

We report on the results of evaluating the competency of a first-order ontology for its use with automated theorem provers (ATPs). The evaluation follows the adaptation of the methodology based on competency questions (CQs) [4] to the framework of first-order logic, which is presented in [2], and is applied to Adimen-SUMO [1]. The set of CQs used for this evaluation has been automatically generated from a small set of semantic patterns and the mapping of WordNet to SUMO. Analysing the results, we can conclude that it is feasible to use ATPs for working with Adimen-SUMO v2.4, enabling the resolution of goals by means of performing non-trivial inferences.

This paper presents the first steps towards building the Predicate Matrix, a new lexical resource resulting from the integration of multiple sources of predicate information including FrameNet (Baker et al., 1997), VerbNet (Kipper, 2005), PropBank (Palmer et al., 2005) and WordNet (Fellbaum, 1998).By using the Predicate Matrix, we expect to provide a more robust interoperable lexicon by discovering and solving inherent inconsistencies among the resources.Moreover, we plan to extend the coverage of current predicate resources (by including from WordNet morphologically related nominal and verbal concepts), to enrich WordNet with predicate information, and possibly to extend predicate information to languages other than English (by exploiting the local wordnets aligned to the English WordNet).

In this paper, the authors present Adimen-SUMO, an operational ontology to be used by first-order theorem provers in intelligent systems that require sophisticated reasoning capabilities (e.g. Natural Language Processing, Knowledge Engineering, Semantic Web infrastructure, etc.). Adimen-SUMO has been obtained by automatically translating around 88% of the original axioms of SUMO (Suggested Upper Merged Ontology). Their main interest is to present in a practical way the advantages of using first-order theorem provers during the design and development of first-order ontologies. First-order theorem provers are applied as inference engines for reengineering a large and complex ontology in order to allow for formal reasoning. In particular, the authors’ study focuses on providing first-order reasoning support to SUMO. During the process, they detect, explain and repair several important design flaws and problems of the SUMO axiomatization. As a by-product, they also provide general design decisions and good practices for creating operational first-order ontologies of any kind.