Damires Souza

A Semantic-Based Ontology Matching Process for PDMS

In Peer Data Management Systems (PDMS), ontology matching can be employed to reconcile peer ontologies and find correspondences between their elements. However, traditional approaches to ontology matching mainly rely on linguistic and/or structural techniques. In this paper, we propose a semantic-based ontology matching process which tries to overcome the limitations of traditional approaches by using semantics. To this end, we present a semantic matcher which identifies, besides the common types of correspondences (equivalence), some other ones (e.g., closeness). We also present an approach for determining a global similarity measure between two peer ontologies based on the identified similarity value of each correspondence. To clarify matters, we provide an example illustrating how the proposed approach can be used in a PDMS and some obtained experimental results.

Towards a context ontology for geospatial data integration

Recently, Geospatial data and Geographic Information Systems (GIS) have been increasingly used As a result, the integration of geospatial data has become a crucial task for decision makers Since GIS and geospatial databases are designed by different organizations using different representation models and there are diverse levels of detail for the spatial features, it is much more complex to achieve data integration in geospatial databases To help matters, context information may be employed to improve two fundamental aspects in Geospatial Data Integration: (1) schema mapping generation and (2) query answering However, a relevant issue when using context is how to better represent context information Ontologies are an interesting approach to represent context, since they enable sharing and reusability and help reasoning In this paper, we propose a context ontology to formally represent context in geospatial data integration We also present an example where this context ontology is used to improve query processing.

A semantic-based approach for data management in a P2P system

Data management in P2P Systems is a challenging problem, due to the high number of autonomous and heterogeneous peers. In some Peer Data Management Systems (PDMSs), peers are semantically clustered in the overlay network. A peer joining the system is assigned to an appropriate cluster, and a query issued by a user at a given peer is routed to semantic neighbor clusters which can provide relevant answers. To help matters, semantic knowledge in the form of ontologies and contextual information has been used successfully to support the techniques used to manage data in such systems. Ontologies can be used to solve the heterogeneities between the peers, while contextual information allows a PDMS to deal with information that is acquired dynamically during the execution of a given query. The goal of this paper is to point out how the semantics provided by ontologies and contextual information can be used to enhance the results of two important data management issues in PDMSs, namely, peer clustering and query reformulation. We present a semantic-based approach to support these processes and we report some experimental results which show how semantics can improve them.

Using information quality for the identification of relevant web data sources: a proposal

In the last decade, applications that make use of data sources available on the Web have experienced a huge growth. One of the main problems regarding that consists in finding the most relevant data sources for a given application. In a general way, a data source is considered relevant when it contributes for answering queries submitted to the application. However, it may happen that a specific data source contributes for answering an application query but the answer provided by the data source does not really meet the user requirements. This may occur because the data source has generic data and the user wants more specific data, for example. On the other hand, some data sources may have data of poor quality, i.e., the data may be outdated, incomplete or incorrect. In such cases, it is not enough just to find data sources that can answer to the application queries. It is also important to check if the available data also meet the user needs. In this paper, we discuss such problem and we propose an approach, based on Information Quality (IQ), to help the evaluation of the relevance of a Web data source for domain-specific applications. We also present an example illustrating how our proposal can be used to enhance this evaluation.

Towards an information quality approach to enhance query routing processes

Query answering has been addressed as a key issue in distributed environments such as Peer Data Management Systems (PDMS). An important step in this process regards query routing, i.e., how to find peers that are most likely to provide matching results according to the semantics of a submitted query. To help matters, we argue that semantic information obtained from Information Quality (IQ) measurements can enrich query routing processes. However, dealing with IQ entails a complex development activity because several tasks (e.g., IQ criteria assessment) must be performed. In this work, we propose a model which combines semantic information concepts with IQ ones as a way to produce semantic knowledge to be used in query routing processes.

A User Context Management Approach for Query Personalization Settings

Data-oriented applications have experienced a huge growth mainly in distributed settings. The increasing amount of available data has made it hard for users to find the information they need in the way they consider relevant. To help matters, a user-centric approach may be used to enhance query answering and, particularly, provide query personalization. In this work, we address the issue of personalizing query answers in diverse settings taking into account the user context. We propose a user context management approach which includes a representational model (as an ontology) and a context-aware service named CODI4In. CODI4In provides the persistence and recovery of the manipulated user context. It has been developed as a plug in which may be coupled to any query answering system. In this paper, we present an initial version of the developed plug in coupled with a query answering application and some promising experimental results we have accomplished with real users.

A Semantic-based Approach for Facilitating Arbovirus Data Usage.

Today’s continuous growth for healthcare information entails an increasing need for using large amounts of data. Particularly, the incidence of arboviruses has been on the rise in some countries, what causes specific needs for studies and definitions of public strategies. In this light, providing a computational platform for usage and reuse on arboviruses related data may help matters. The idea is that different applications and users can make use of that data in diverse ways. In this work, we propose a semantic-based approach for facilitating use and reuse of arboviruses related data. We present the definitions underlying our approach, examples illustrating how it works, and some promising results we have obtained.

Enhancing JSON to RDF Data Conversion with Entity Type Recognition.

Nowadays, many Web data sources and APIs make their data available on the Web in semi-structured formats such as JSON. However, JSON data cannot be directly used in the Web of data, where principles such as URIs and semantically named links are essential. Thus it is necessary to convert JSON data into RDF data. To this end, we have to consider semantics in order to provide data reference according to domain vocabularies. To help matters, we present an approach which identifies JSON metadata, aligns them with domain vocabulary terms and converts data into RDF. In addition, along with the data conversion process, we provide the identification of the semantically most appropriate entity types to the JSON objects. We present the definitions underlying our approach and results obtained with the evaluation.

Conceptual Mappings to Convert Relational into NoSQL Databases

Sometimes, data belonging to Relational databases need to be transferred to NoSQL ones. However, the data conversion process between Relational to NoSQL databases is considered as not trivial, since it is necessary to have considerable knowledge about the data models at hand. Regarding the structural heterogeneity underlying this problem, we propose an approach, named as R2NoSQL, which defines conceptual mappings to enhance the data conversion process. In this paper, we present our approach and some implementation and experimental results, which show that, by using the defined conceptual mappings, we obtain a consistent target NoSQL database with respect to a source Relational one.

CORE - A Context-based Approach for Rewriting User Queries

When users access data-oriented applications, they aim to obtain useful information. Sometimes, however, the user needs to reformulate the submitted queries several times and go through many answers until a satisfactory set of answers is achieved. In this scenario, the user may be in different contexts, and these contexts may change frequently. For instance, the place where the user submits a given query may be taken into account and thereby may change the query itself and its results. In this work, we address the issue of personalizing query answers in data-oriented applications considering the context acquired at query submission time. To this end, we propose a query rewriting approach, which makes use of context-based rules to produce new related expanded or relaxed queries. In this paper, we present our approach and some experimental results we have accomplished. These results show that, by considering the acquired user context, it really enhances the precision and recall of the obtained answers.