Using Linguistic Patterns to Enhance Ontology Development

In this paper we describe how linguistic patterns can contribute to ontology development by enabling an easier reuse of some ontological resources. In particular, our research focuses on the reuse of ontology design patterns and ontology statements by relying on linguistic constructs at different stages of the reuse process. With this aim, we propose the employment of lexico-syntactic patterns with two objectives: 1) the reuse of ontology design patterns, and 2) the validation of ontology statements for their subsequent reuse in the ontology development. To illustrate the proposed approaches, we will present some examples of lexico-syntactic patterns and their employment in the reuse of ontology design patterns and in the validation of ontology statements

Enriching the "Senso Comune" Platform with Automatically Acquired Data

International audienceThis paper reports on research activities on automatic methods for the enrichment of the Senso Comune platform. At this stage of development, we will report on two tasks, namely word sense alignment with MultiWordNet and automatic acquisition of Verb Shallow Frames from sense annotated data in the MultiSemCor corpus. The results obtained are satisfying. We achieved a final F-measure of 0.64 for noun sense alignment and a F-measure of 0.47 for verb sense alignment, and an accuracy of 68% on the acquisition of VerbShallow Frames

Head to head: Semantic similarity of multi-word terms

Terms are linguistic signifiers of domain–specific concepts. Semantic similarity between terms refers to the corresponding distance in the conceptual space. In this study, we use lexico–syntactic information to define a vector space representation in which cosine similarity closely approximates semantic similarity between the corresponding terms. Given a multi–word term, each word is weighed in terms of its defining properties. In this context, the head noun is given the highest weight. Other words are weighed depending on their relations to the head noun. We formalized the problem as that of determining a topological ordering of a direct acyclic graph, which is based on constituency and dependency relations within a noun phrase. To counteract the errors associated with automatically inferred constituency and dependency relations, we implemented a heuristic approach to approximating the topological ordering. Different weights are assigned to different words based on their positions. Clustering experiments performed on such a vector space representation showed considerable improvement over the conventional bag–of–word representation. Specifically, it more consistently reflected semantic similarity between the terms. This was established by analyzing the differences between automatically generated dendrograms and manually constructed taxonomies. In conclusion, our method can be used to semi–automate taxonomy construction

SemEval-2016 Task 13: Taxonomy Extraction Evaluation (TExEval-2)

This paper describes the second edition of the shared task on Taxonomy Extraction Evaluation organised as part of SemEval 2016. This task aims to extract hypernym-hyponym relations between a given list of domain-specific terms and then to construct a domain taxonomy based on them. TExEval-2 introduced a multilingual setting for this task, covering four different languages including English, Dutch, Italian and French from domains as diverse as environment, food and science. A total of 62 runs submitted by 5 different teams were evaluated using structural measures, by comparison with gold standard taxonomies and by manual quality assessment of novel relations.Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 (INSIGHT

Ontology-based Why-Question Analysis Using Lexico-Syntactic Patterns

This research focuses on developing a method to analyze why-questions.  Some previous researches on the why-question analysis usually used the morphological and the syntactical approach without considering the expected answer types. Moreover, they rarely involved domain ontology to capture the semantic or conceptualization of the content. Consequently, some semantic mismatches occurred and then resulting not appropriate answers. The proposed method considers the expected answer types and involves domain ontology. It adapts the simple, the bag-of-words like model, by using semantic entities (i.e., concepts/entities and relations) instead of words to represent a query. The proposed method expands the question by adding the additional semantic entities got by executing the constructed SPARQL query of the why-question over the domain ontology. The major contribution of this research is in developing an ontology-based why-question analysis method by considering the expected answer types. Some experiments have been conducted to evaluate each phase of the proposed method. The results show good performance for all performance measures used (i.e., precision, recall, undergeneration, and overgeneration). Furthermore, comparison against two baseline methods, the keyword-based ones (i.e., the term-based and the phrase-based method), shows that the proposed method obtained better performance results in terms of MRR and P@10 values

Event-based Access to Historical Italian War Memoirs

The progressive digitization of historical archives provides new, often domain specific, textual resources that report on facts and events which have happened in the past; among these, memoirs are a very common type of primary source. In this paper, we present an approach for extracting information from Italian historical war memoirs and turning it into structured knowledge. This is based on the semantic notions of events, participants and roles. We evaluate quantitatively each of the key-steps of our approach and provide a graph-based representation of the extracted knowledge, which allows to move between a Close and a Distant Reading of the collection.Comment: 23 pages, 6 figure