International Journal of Metadata, Semantics and Ontologies
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International Journal of Metadata, Semantics and Ontologies (9 papers in press)
Abstract: Semantic annotation, the process of identifying key phrases in texts and linking them to concepts in a knowledge base, is an important basis for semantic information retrieval and the Semantic Web uptake. Despite the emergence of semantic annotation systems, very few comparative studies have been published on their performance. In this paper, we provide an evaluation of the performance of existing systems over three tasks: full semantic annotation, named entity recognition, and keyword detection. More specifically, the spotting capability (recognition of relevant surface forms in text) is evaluated for all three tasks, whereas the disambiguation (correctly associating an entity from Wikipedia or DBpedia to the spotted surface forms) is evaluated only for the first two tasks. We use logistic regression to identify significant performance differences. Although some of the annotators are specifically targeted at some task (NE, SA, KW), our results show that they do not necessarily obtain the best performance on those tasks. In fact, systems identified as full semantic annotators beat all other systems on all datasets. We also show that there is still much room for improvement for the identification of the most relevant entities described in a text.
Keywords: semantic annotation; linked data cloud; performance evaluation.
Ontology-based modleling of extended web service secure conversation Pattern
by Ashish Kumar Dwivedi
Abstract: Securing an application based on service oriented architecture provides defence against a number of security threats arising from exposing applications and data to the internet. Various security guidelines are available to apply security in web applications. But these guidelines are sometimes difficult to understand and generate inconsistencies. In this study, an extended web service secure conversation pattern is presented in the presence of a man-in-the-middle attack. An ontology-based modelling and refinement framework is presented for semantically analysing an extended web service secure conversation pattern. A metamodel is introduced to provide rigorous modelling of security services in terms of concepts, properties, and relationships. At the end of this study, an evaluation of the proposed approach has been made by performing experiments for security requirements against security policies in the presence of proposed description logic rules.
Keywords: extended web service; secure conversation pattern; security patterns; semantic web service security; UML; OWL.
Collections revisited from the perspective of historical testimonies
by Annamaria Goy, Diego Magro
Abstract: This paper presents the results of an ontological analysis of collective entities, as an essential step towards the definition of a rich semantic model underlying ontology-driven applications in the historical and cultural heritage domains. The major contributions of our proposal are the following: (a) an explicit distinction of contingent and necessary features, that led us to formalise our ontology by means of modal logics; (b) a description of collective entities from a diachronic perspective (thus including singletons and empty collections); (c) an analysis of the inferences enabled by the characterisation of collective entities and by the inclusion relationships; (d) a representation of the inclusion relationships that does not imply existential dependence; (e) a distinction between emerging and created collective entities. In the paper, we present an indepth ontological analysis of these aspects and provide a sound formalisation for it.
Keywords: collections; collective entities; sets; ontology; ontologies; semantic model; ontological analysis; ontology-driven applications; semantic metadata; inference; modal logics; historical archives; historical testimonies; cultural heritage.
ANNETT-O: an ontology for describing artificial neural network evaluation, topology and training
by Iraklis A. Klampanos, Athanasios Davvetas, Antonis Koukourikos, Vangelis Karkaletsis
Abstract: Deep learning models, while effective and versatile, are becoming increasingly complex, often including multiple overlapping networks of arbitrary depths, multiple objectives and non-intuitive training methodologies. This makes it increasingly difficult for researchers and practitioners to design, train and understand them. In this paper, we present ANNETT-O, a much-needed, generic and computer-actionable vocabulary for researchers and practitioners to describe their deep learning configurations, training procedures and experiments. The proposed ontology focuses on topological, training and evaluation aspects of complex deep neural configurations, while keeping peripheral entities more succinct. Knowledge bases implementing ANNETT-O can support a wide variety of queries, providing relevant insights to users. In addition to a detailed description of the ontology, we demonstrate its suitability for the task via a number of hypothetical use-cases of increasing complexity.
Keywords: ontologies; deep learning; artificial intelligence; semantic web; data schemas; OWL; algorithm concepts.
Exploring logical and hierarchical information to map relational databases into ontologies
by Hegler Tissot, Cristiane Aparecida Gonçalves Huve, Leticia Mara Peres, Marcos Didonet Del Fabro
Abstract: Ontologies are formal specifications of conceptualisations. Their designs require to understand the concepts involved in the domain to be mapped. One well-known method to produce ontologies is to extract their concepts from relational databases. We conducted a practical study over a real-world scenario on applying existing rules and we identified open issues to be addressed, such as the utilisation of logical metadata as a proper vocabulary, the implementation targeted to specific domains and mappings of hierarchical and self-hierarchical structures. In this paper, we present a novel approach that overcomes these issues. Our solution uses physical and logical models to enrich the terminology produced in the target ontology. It also contains a more comprehensive set of rules, taking into account instances and (self) hierarchies. We validate our approach with two experiments from the healthcare domain as input.
Keywords: ontologies; rule-based integration; logical model; hierarchical structures.
Context-based interoperability of semantic web services
by Karima Mecheri, Mahmoud Boufaida, Djamel Meslati, Labiba Souici-Meslati
Abstract: Semantic mediation is a necessary mechanism to ensure interoperability of web services. In this paper, we present a classification and comparison of works in this field. We propose a semantic model to explicitly describe the heterogeneities of web services. This model is the basis for the mediation automation. In particular, we describe the behavioural semantic sub-model, for which we use the abstract state machine's method and the notion of behavioural context that we have defined. The mediation process we propose is implemented using web services. The core of this implementation is situated in the semantic transformations module, which is based on the mapping of ontologies and the reasoning about these mappings.
Keywords: SWS; semantic web services; interoperability; semantic model; abstract state machine; behavioural context; mediation; ontologies.
Constraint specifications for domain-specific systems: ontology-driven approach
by Shreya Banerjee, Anirban Sarkar
Abstract: Well-formed Domain Specific Modelling Languages (DSML) are devised based on well-defined sets of syntaxes and semantics. These precisions are obtained raising at abstraction levels in domain specifications. Yet, appropriate representations of constraints are also important to limit meanings of generic concepts to different abstraction levels in a DSML. To address this issue, in this paper, a constraint specification language is developed. This proposed language is capable to restrict meanings of general concepts represented in an upper level ontology - Generalised Ontology Modelling (GOM) (Banerjee and Sarkar, 2016b) to domain specific systems in a systematic way. Further, several automated methods impose distinct constraints at different levels of abstractions in domain specific modelling. These methods also validate distinct constructs and constraints in domain specific systems against general concepts of GOM. The applicability of the proposed work is proved using couple of case studies based on applications in data-intensive and service-based domains.
Keywords: domain-specific modelling language; domain-specific language; constraint specification language; grammar specification; validation methods; ontology; model hierarchy; ontology hierarchy; constraint imposition methods; grammar implementation.
Mining annotators' common knowledge for automatic text revision
by Giovanni Siragusa, Luigi Di Caro, Marco Tosalli
Abstract: Many natural language understanding tasks require clean input textual data in order to train systems with the highest precision. Such data, usually collected from surveys or the web, are manually processed in order to remove morphosyntactic variability, spelling errors and incoherence in naming entities. Since these operations are conducted by domain experts and annotators, they are usually costly and time-consuming. Furthermore, this scenario is very common in industrial tasks where annotators are hired. In this context, we propose an innovative and simple method that extracts correction patterns, i.e., <expression, replacement> pairs, where expression is a matching string and replacement indicates how to re-write the matched string. Such tool can be used both to evaluate annotators (since it provides a deep understanding of their work) and to automatically revise the texts. We extensively tested our method in a multilingual setting, obtaining outstanding results over baseline approaches.
Keywords: pattern extraction; natural language understanding; annotation learning; correction patterns.
A data exchange solution for emergency response systems based on the EDXL-RESCUER ontology
by Laís Do Nascimento Salvador, Rebeca Barros, Vaninha Vieira Dos Santos, Félix Simas De Souza Neto, Renato Lima Novais, Simone Da Silva Amorim, Marian Weber
Abstract: Handling an emergency requires the coordination and cooperation of several people and systems from various agencies and organisations, including the government and society in general. A wide range of heterogeneous data is managed by different stakeholders, thus demanding solutions to support integration issues such as interoperability and ambiguity. In a previous work, we proposed an ontology for Emergency Response Systems, called EDXL-RESCUER, in the scope of the RESCUER project. In this paper, we present the usage of this ontology in a data exchange solution (DILS: Data Integration with Legacy Systems), which aims to provide semantic interoperability between Emergency Response Systems, and the evolution of EDXL-RESCUER. To evaluate the proposed solution, EDXL-RESCUER & DILS, we performed two studies: (i) simulations using two real data sources, the Police Reports from Bahia Public Safety and Security Department, Brazil; and the Canadian Disaster Database; (ii) an emergency simulation at an Industrial Park in Bahia, Brazil. The results demonstrate the potential use of the EDXL-RESCUER as a common vocabulary to support semantic interoperability between emergency response systems. We also verified the feasibility of using the DILS solution in emergency management scenarios. Besides EDXL-RESCUER & DILS, a mapping and integration of concepts related to data exchange are presented.
Keywords: emergency response system; ontology; EDXL-RESCUER; data integration; data exchange; DILS; emergency management; RESCUER.