Joint Evolution of Ontologies and Semantic Annotations

Ontologies are becoming nowadays a recurrent topic in many research areas of computer science. Among other things, they are used as best model for representing knowledge of a given field of expertise. More specifically, the evolution of Semantic Web mostly relies on the use of ontologies in describing various web resources. However, the world is changing, so do resources and knowledge. Consequently, ontologies on which are based knowledge and resources are also called to evolve. One of the solutions helping to ensure this evolution is to manage the development process in order to guarantee supported services as well as some quality requirements. Many tools have been designed to ensure designing/editing of ontologies, but none of them has enough functions to ensure their evolution. Evolution of ontology considered as its adaptation to changes in the field is a key issue in the ontology engineering field. Several studies have been focused on this problem, but proposed solutions did not manage all the side effects that may derive from the evolution. This article proposes an extension of MS-ONTO [1] called POOE. POOE not only supports the evolution effects on a double plan, externally and internally (verification of integrity constraints, quality analysis of the evolved ontology) but it constitutes a true validation tool of a change log.

Proceedings of the …, 2001

LDV Forum, 2008

This paper describes development and design of an ontology of linguistic annotations, primarily word classes and morphosyntactic features, based on existing standardization approaches (e.g. EAGLES), a set of annotation schemes (e.g. for German, STTS and morphological annotations), and existing terminological resources (e.g. GOLD). The ontology is intended to be a platform for terminological integration, integrated representation and ontology-based search across existing linguistic resources with terminologically heterogeneous annotations. Further, it can be applied to augment the semantic analysis of a given text with an ontological interpretation of its morphosyntactic analysis.

Proceedings of the seventh international conference on Knowledge capture - K-CAP '13, 2013

ABSTRACT One of the most important prerequisites for achieving the Semantic Web vision is semantic annotation of data/resources. Semantic annotation enriches unstructured and/or semistructured content with a context that is further linked to the structured domain-specific knowledge. In particular, ontologybased semantic annotators enable the selection of a specific ontology to annotate content. This paper presents results of an empirical study of recent ontology-based annotators, namely Stanbol, KIM, and SDArch. Specifically, we evaluated the robustness of these annotators with respect to specific features of ontology concepts such as the length of concepts? labels and their linguistic categories (e.g., prepositions and conjunctions). Our results show that although significantly correlated according to most of the conducted evaluations, tools still exhibit their unique features that could be a topic of new research.

Proc. of the ESWC 2006 Workshop on Mastering the …, 2006

This paper presents an innovative solution for annotating documents and populating domain ontologies in a single process. In fact, this process can be viewed as a translation of a linguistic representation of a text into a more formal representation format, being RDF statements or OWL instances. Thus, we provide the OntoPop methodology for mapping linguistic extractions with concepts of ontology thanks to knowledge acquisition rules. This methodology has been implemented in a platform integrating a IE tool and a commercial ontology repository. The platform is currently under evaluation on various real world applications. Nevertheless, OntoPop already raises interesting issues such as semantic heterogeneity that are discussed at the end of this article.

18th International Conference on Database and Expert Systems Applications (DEXA 2007), 2007

In this paper we give an overview of methods to find representations of ontology defined concepts in texts. We distinguish two approaches: lexicon-based methods and approaches using lexicalized ontologies. We focus on the latter method and describe the problems and choices that have to be made if this approach is put to work. Finally we describe an open-source tool that implements the lexicalized ontology approach along with two examples of its applicability in a practical context.

Proceedings of the ACL 2003 workshop …, 2003

2015

Powerful tools could help users explore and maintain domain specific documentations, provided that documents have been semantically annotated. For that, the annotations must be sufficiently specialized and rich, relying on some explicit semantic model, usually an ontology, that repre-sents the semantics of the target domain. In this paper, we learn to annotate biomedical scientific publications with respect to a Gene Regulation Ontology. We devise a two-step approach to an-notate semantic events and relations. The first step is recast as a text segmentation and labeling problem and solved using machine translation tools and a CRF, the second as multi-class classi-fication. We evaluate the approach on the BioNLP-GRO benchmark, achieving an average 61% F-measure on the event detection by itself and 50 % F-measure on biological relation annotation. This suggests that human annotators can be supported in domain specific semantic annotation tasks. Under different experimental settings, w...

Lecture Notes in Computer Science, 2006

Both the multilingual aspects which characterize the (Semantic) Web and the demand for more easy-to-share forms of knowledge representation, being equally accessible by humans and machines, push the need for a more "linguistically aware" approach to ontology development. Ontologies should thus express knowledge by associating formal content with explicative linguistic expressions, possibly in different languages. By adopting such an approach, the intended meaning of concepts and roles becomes more clearly expressed for humans, thus facilitating (among others) reuse of existing knowledge, while automatic content mediation between autonomous information sources gets far more chances than otherwise. In past work we introduced OntoLing [7], a Protégé plug-in offering a modular and scalable framework for performing manual annotation of ontological data with information from different, heterogeneous linguistic resources. We present now an improved version of OntoLing, which supports the user with automatic suggestions for enriching ontologies with linguistic content. Different specific linguistic enrichment problems are discussed and we show how they have been tackled considering both algorithmic aspects and profiling of user interaction inside the OntoLing framework.

2010

The paper compares two semantic annotation frameworks that are designed for unstructured and ungrammatical domains. Both frameworks, namely ontoX (ontology-driven information Extraction) and BNOSA (Bayesian network and ontology based semantic annotation), extensively use ontologies during knowledge building, rule generation and data extraction phases. Both of them claim to be scalable as they allow a knowledge engineer, using either of these frameworks, to employ them for any other domain by simply plugging the corresponding ontology to the framework. They, however, differ in the ways conflicts are resolved and missing values are predicted. OntoX uses two heuristic measures, named level of evidence and level of confidence, for conflict resolution while the same task is performed by BNOSA with the aid of Bayesian networks. BNOSA also uses Bayesian networks to predict missing values. The paper compares the performance of both BNOSA and ontoX on the same data set and analyzes their strengths and weaknesses.