Julio Cesar Dos Reis

Understanding semantic mapping evolution by observing changes in biomedical ontologies

Knowledge Organization Systems (KOSs) are extensively used in the biomedical domain to support information sharing between software applications. KOSs are proposed covering different, but overlapping subjects, and mappings indicate the semantic relation between concepts from two KOSs. Over time, KOSs change as do the mappings between them. This can result from a new discovery or a revision of existing knowledge which includes corrections of concepts or mappings. Indeed, changes affecting KOS entities may force the underline mappings to be updated in order to ensure their reliability over time. To tackle this open research problem, we study how mappings are affected by KOS evolution. This article presents a detailed descriptive analysis of the impact that changes in KOS have on mappings. As a case study, we use the official mappings established between SNOMED CT and ICD-9-CM from 2009 to 2011. Results highlight factors according to which KOS changes in varying degrees influence the evolution of mappings.

Semi-automatic Adaptation of Mappings between Life Science Ontologies

The continuous evolution of life science ontologies requires the adaptation of their associated mappings. We propose two approaches for tackling this problem in a largely automatic way: (1) a composition-based adaptation relying on the principle of mapping composition and (2) a diff-based adaptation algorithm individually handling change operations to update the mapping. Both techniques reuse unaffected correspondences, and adapt only the affected mapping part. We experimentally assess and confirm the effectiveness of our approaches for evolving mappings between large life science ontologies.

Mapping adaptation actions for the automatic reconciliation of dynamic ontologies

The highly dynamic nature of domain ontologies has a direct impact on semantic mappings established between concepts from different ontologies. Mappings must therefore be maintained according to ongoing ontology changes. Since many software applications exploit mappings for managing information and knowledge, it is important to define appropriate adaptation strategies to apply to existing mappings in order to keep their validity over time. In this article, we propose a set of mapping adaptation actions and present how they are used to maintain mappings up-to-date based on ontology change operations of different nature. We conduct an experimental evaluation using life sciences ontologies and mappings. We measure the evolution of mappings based on the proposed approach to mapping adaptation. The results confirm that mappings must be individually adapted according to the different types of ontology change.

State-of-the-art on mapping maintenance and challenges towards a fully automatic approach

We define and illustrate the mapping maintenance problem and its specificities.We categorize, review and discuss the literature on mapping maintenance.We provide unaddressed research challenges to cope with mapping maintenance. In several domains, software applications have intensively used Knowledge Organization Systems (KOS) like database schemas, ontologies, taxonomies and thesauri and their associated semantic correspondences (i.e., mappings). This underlines the relevance and capabilities of KOS and mappings to manage and integrate vast amounts of data. However, the dynamic nature of domain knowledge forces knowledge engineers to constantly modify KOS, to keep them up to date and useful. In this context, the maintenance of mappings affected by KOS evolution still remains an open research issue. Although this problem appears relevant for many different computer science fields, ranging from database to artificial intelligence, literature has so far only superficially addressed it to enable more flexible, automatic and precise solutions. This article presents, discusses and compares existing approaches for maintaining mappings and describes open research challenges.

Pragmatic Aspects of Collaborative Problem Solving: Towards a Framework for Conceptualizing Dynamic Knowledge

Knowledge production in the Social Web can be understood as a dynamic socio-cultural process. Mechanisms that support users to explore this knowledge in an effective and efficient way may bring various benefits. However, the construction of these interaction mechanisms depends on further research on multidisciplinary fields. The interpretation of the content by users is influenced by meanings and intentions, as well as by the understanding of the evolution of these aspects over time. This paper analyses the evolution of meaning and intentions in collaborative problem solving. The analysis is informed by Semiotics and Speech Act theories. From this analysis, the paper proposes a conceptual framework for multidisciplinary research in three interconnected perspectives: interactive, conceptual and technical.

Identifying Change Patterns of Concept Attributes in Ontology Evolution

Ontology versions are periodically released to ensure their usefulness and reliability over time. This potentially impacts dependent artefacts such as mappings and annotations. Dealing with requires to finely characterize ontology entities’ changes between ontology versions. This article proposes to identify change patterns at attribute values when an ontology evolves, to track textual statements describing concepts. We empirically evaluate our approach by using biomedical ontologies, for which new ontology versions are frequently released. Our achieved results suggest the feasibility of the proposed techniques.

An Interaction Design Method to Support the Expression of User Intentions in Collaborative Systems

The communication and interpretation of users’ intentions play a key role in collaborative web discussions. However, existing mechanisms fail to support the users’ expression of their intentions during collaborations. In this article, we propose an original interaction design method based on semiotics to guide the construction of interactive mechanisms, which allow users to explicitly express and share intentions. We apply the method in a case study in the context of collaborative forums for software developers. The obtained results reveal preliminary evidences regarding the effectiveness of the method for the definition of interface components, enabling more meaningful and successful communications.

DyKOSMap: A framework for mapping adaptation between biomedical knowledge organization systems

BACKGROUND Knowledge Organization Systems (KOS) and their associated mappings play a central role in several decision support systems. However, by virtue of knowledge evolution, KOS entities are modified over time, impacting mappings and potentially turning them invalid. This requires semi-automatic methods to maintain such semantic correspondences up-to-date at KOS evolution time. METHODS We define a complete and original framework based on formal heuristics that drives the adaptation of KOS mappings. Our approach takes into account the definition of established mappings, the evolution of KOS and the possible changes that can be applied to mappings. This study experimentally evaluates the proposed heuristics and the entire framework on realistic case studies borrowed from the biomedical domain, using official mappings between several biomedical KOSs. RESULTS We demonstrate the overall performance of the approach over biomedical datasets of different characteristics and sizes. Our findings reveal the effectiveness in terms of precision, recall and F-measure of the suggested heuristics and methods defining the framework to adapt mappings affected by KOS evolution. The obtained results contribute and improve the quality of mappings over time. CONCLUSIONS The proposed framework can adapt mappings largely automatically, facilitating thus the maintenance task. The implemented algorithms and tools support and minimize the work of users in charge of KOS mapping maintenance.

Recognizing lexical and semantic change patterns in evolving life science ontologies to inform mapping adaptation

BACKGROUND Mappings established between life science ontologies require significant efforts to maintain them up to date due to the size and frequent evolution of these ontologies. In consequence, automatic methods for applying modifications on mappings are highly demanded. The accuracy of such methods relies on the available description about the evolution of ontologies, especially regarding concepts involved in mappings. However, from one ontology version to another, a further understanding of ontology changes relevant for supporting mapping adaptation is typically lacking. METHODS This research work defines a set of change patterns at the level of concept attributes, and proposes original methods to automatically recognize instances of these patterns based on the similarity between attributes denoting the evolving concepts. This investigation evaluates the benefits of the proposed methods and the influence of the recognized change patterns to select the strategies for mapping adaptation. RESULTS The summary of the findings is as follows: (1) the Precision (>60%) and Recall (>35%) achieved by comparing manually identified change patterns with the automatic ones; (2) a set of potential impact of recognized change patterns on the way mappings is adapted. We found that the detected correlations cover ∼66% of the mapping adaptation actions with a positive impact; and (3) the influence of the similarity coefficient calculated between concept attributes on the performance of the recognition algorithms. CONCLUSIONS The experimental evaluations conducted with real life science ontologies showed the effectiveness of our approach to accurately characterize ontology evolution at the level of concept attributes. This investigation confirmed the relevance of the proposed change patterns to support decisions on mapping adaptation.

Addressing universal access in social networks: an inclusive search mechanism

Social network services (SNSs) have brought new possibilities and challenges to the design of software environments that respect people’s cultural differences. These systems may represent an opportunity for social and digital inclusion. However, search mechanisms in these systems impose serious barriers for people in the process of acquiring digital literacy. One of the barriers is the difficulty of using the adequate terms/keywords to perform content searches. This paper presents an approach to allow ordinary, non-technology proficient people to access the content of a network through the use of search parameters that make sense to them. The proposal is grounded on Semantic Web technologies (Web ontology) combined with Organizational Semiotics concepts and methods to identify the users’ profile and language. A case study was conducted with the search mechanism integrated into a SNS, and a preliminary evaluation reveals the advantages and drawbacks of the approach.