Semantic Adaptation of Knowledge Representation Systems

Part 4: Intelligent Computational SystemsInternational audienceDue to the worldwide diversity of enterprises, a high number of ontologies representing the same segment of reality which are not semantically coincident have appeared. To solve this problem, a possible solution is to use a reference ontology to be the intermediary in the communications between the community enterprises and to outside. Since semantic mappings between enterprise’s ontologies are established, this solution allows each of the enterprises to keep internally its own ontology and semantics unchanged. However information systems are not static, thus established mappings become obsolete with time. This paper’s presents a PhD research with the objective to identify a suitable approach that combines semantic mappings with user’s feedback, providing an automatic learning to ontologies & enabling auto-adaptability and, consequently, dynamism to the information systems

Domain transfer for deep natural language generation from abstract meaning representations

Stochastic natural language generation systems that are trained from labelled datasets are often domainspecific in their annotation and in their mapping from semantic input representations to lexical-syntactic outputs. As a result, learnt models fail to generalize across domains, heavily restricting their usability beyond single applications. In this article, we focus on the problem of domain adaptation for natural language generation. We show how linguistic knowledge from a source domain, for which labelled data is available, can be adapted to a target domain by reusing training data across domains. As a key to this, we propose to employ abstract meaning representations as a common semantic representation across domains. We model natural language generation as a long short-term memory recurrent neural network encoderdecoder, in which one recurrent neural network learns a latent representation of a semantic input, and a second recurrent neural network learns to decode it to a sequence of words. We show that the learnt representations can be transferred across domains and can be leveraged effectively to improve training on new unseen domains. Experiments in three different domains and with six datasets demonstrate that the lexical-syntactic constructions learnt in one domain can be transferred to new domains and achieve up to 75-100% of the performance of in-domain training. This is based on objective metrics such as BLEU and semantic error rate and a subjective human rating study. Training a policy from prior knowledge from a different domain is consistently better than pure in-domain training by up to 10%

Criteria for the selection of a semantic repository for managing SKOS data

In the last years, institutions and organizations have started paying attention to the semantic data technologies as a key component of their information management infrastructure. A key component in traditional information systems that is expected to have a great relevance in the Semantic Web and linked open data projects are knowledge organization systems: classifications, thesauri, list of subject headings, etc. W3C technical standards for the Semantic Web include the SKOS (Simple Knowledge Organization System) specification for encoding and tagging these knowledge representation tools. The adaptation of an existing knowledge representation system to SKOS requires the conversion and tagging of the data. This is a task that can be easily done by the staff working at libraries, archives and documentation centers, but the conversion of existing materials to SKOS is not enough. Centers need to offer the capability of exploring and using these systems in different ways: e.g. direct search for end-users. To do that, entities need to select and deploy software tools for storing, managing and querying SKOS tools using the SPARQL standard. The complexity of the SPARQL language makes necessary to think on ways to make querying easier for end-users. This presentation describes the results of assessing two semantic repository tools – GraphDB and Virtuoso – to manage SKOS-based vocabularies and define query interfaces adapted to the relationships between concepts used in SKOS. The result of this research activity includes the design of two alternative query interfaces to help users interact with the SKOS tools when doing complex querie

In search of different categories of abstract concepts: a fMRI adaptation study

Concrete conceptual knowledge is supported by a distributed neural network representing different semantic features according to the neuroanatomy of sensory and motor systems. If and how this framework applies to abstract knowledge is currently debated. Here we investigated the specific brain correlates of different abstract categories. After a systematic a priori selection of brain regions involved in semantic cognition, i.e. responsible of, respectively, semantic representations and cognitive control, we used a fMRI-adaptation paradigm with a passive reading task, in order to modulate the neural response to abstract (emotions, cognitions, attitudes, human actions) and concrete (biological entities, artefacts) categories. Different portions of the left anterior temporal lobe responded selectively to abstract and concrete concepts. Emotions and attitudes adapted the left middle temporal gyrus, whereas concrete items adapted the left fusiform gyrus. Our results suggest that, similarly to concrete concepts, some categories of abstract knowledge have specific brain correlates corresponding to the prevalent semantic dimensions involved in their representation

Semi-automatic annotation process for procedural texts: An application on cooking recipes

Taaable is a case-based reasoning system that adapts cooking recipes to user constraints. Within it, the preparation part of recipes is formalised as a graph. This graph is a semantic representation of the sequence of instructions composing the cooking process and is used to compute the procedure adaptation, conjointly with the textual adaptation. It is composed of cooking actions and ingredients, among others, represented as vertices, and semantic relations between those, shown as arcs, and is built automatically thanks to natural language processing. The results of the automatic annotation process is often a disconnected graph, representing an incomplete annotation, or may contain errors. Therefore, a validating and correcting step is required. In this paper, we present an existing graphic tool named \kcatos, conceived for representing and editing decision trees, and show how it has been adapted and integrated in WikiTaaable, the semantic wiki in which the knowledge used by Taaable is stored. This interface provides the wiki users with a way to correct the case representation of the cooking process, improving at the same time the quality of the knowledge about cooking procedures stored in WikiTaaable

Collaborative Authoring of Adaptive Educational Hypermedia by Enriching a Semantic Wiki’s Output

This research is concerned with harnessing collaborative approaches for the authoring of Adaptive Educational Hypermedia (AEH) systems. It involves the enhancement of Semantic Wikis with pedagogy aware features to this end. There are many challenges in understanding how communities of interest can efficiently collaborate for learning content authoring, in introducing pedagogy to the developed knowledge models and in specifying user models for efficient delivery of AEH systems. The contribution of this work will be the development of a model of collaborative authoring which includes domain specification, content elicitation, and definition of pedagogic approach. The proposed model will be implemented in a prototype AEH authoring system that will be tested and evaluated in a formal education context

Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective

This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. Specifically, it categorises the cross-dataset recognition into seventeen problems based on a set of carefully chosen data and label attributes. Such a problem-oriented taxonomy has allowed us to examine how different transfer learning approaches tackle each problem and how well each problem has been researched to date. The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition, but also the problems (e.g. eight of the seventeen problems) that have been scarcely studied. This survey not only presents an up-to-date technical review for researchers, but also a systematic approach and a reference for a machine learning practitioner to categorise a real problem and to look up for a possible solution accordingly

Ontology-based collaborative framework for disaster recovery scenarios

This paper aims at designing of adaptive framework for supporting collaborative work of different actors in public safety and disaster recovery missions. In such scenarios, firemen and robots interact to each other to reach a common goal; firemen team is equipped with smart devices and robots team is supplied with communication technologies, and should carry on specific tasks. Here, reliable connection is mandatory to ensure the interaction between actors. But wireless access network and communication resources are vulnerable in the event of a sudden unexpected change in the environment. Also, the continuous change in the mission requirements such as inclusion/exclusion of new actor, changing the actor's priority and the limitations of smart devices need to be monitored. To perform dynamically in such case, the presented framework is based on a generic multi-level modeling approach that ensures adaptation handled by semantic modeling. Automated self-configuration is driven by rule-based reconfiguration policies through ontology