José Luis Borbinha

Extracting and Exploring the Geo-Temporal Semantics of Textual Resources

Geo-temporal criteria are important for filtering, grouping and prioritizing information resources. This presents techniques for extracting semantic geo-temporal information from text, using simple text mining methods that leverage on a gazetteer. A prototype system, implementing the proposed methods and capable of displaying information over maps and timelines, is described. This prototype can take input in RSS, demonstrating the application to content from many different online sources. Experimental results demonstrate the efficiency and accuracy of the proposed approaches.

A metadata geoparsing system for place name recognition and resolution in metadata records

This paper describes an approach for performing recognition and resolution of place names mentioned over the descriptive metadata records of typical digital libraries. Our approach exploits evidence provided by the existing structured attributes within the metadata records to support the place name recognition and resolution, in order to achieve better results than by just using lexical evidence from the textual values of these attributes. In metadata records, lexical evidence is very often insufficient for this task, since short sentences and simple expressions are predominant. Our implementation uses a dictionary based technique for recognition of place names (with names provided by Geonames), and machine learning for reasoning on the evidences and choosing a possible resolution candidate. The evaluation of our approach was performed in data sets with a metadata schema rich in Dublin Core elements. Two evaluation methods were used. First, we used cross-validation, which showed that our solution is able to achieve a very high precision of 0,99 at 0,55 recall, or a recall of 0,79 at 0,86 precision. Second, we used a comparative evaluation with an existing commercial service, where our solution performed better on any confidence level (p<0,001).

Geographically-aware information retrieval for collections of digitized historical maps

DIGMAP is a project focused on historical digitized maps. The project will develop a set of services, to be available in the Internet, based on reusable open-source software solutions. The main service will provide discovery and access to resources related to historical cartography, based on metadata from European national libraries and other relevant third part providers. These resources will comprise both physical and digitized objects. In the case of digitized maps, available metadata will be enriched by automatic and semi-automatic processes that will try to extract relevant indexing information from the images of the digitized maps, as also from any kind of associated text. This paper presents an early overview on the project, particularly focusing on the aspects related to geographical information retrieval.

FRBRization of MARC records in multiple catalogs

This paper addresses the problem of using the FRBR model to support the presentation of results. It describes a service implementing new algorithms and techniques for transforming existing MARC records into the FRBR model for this specific purpose. This work was developed in the context of the TELPlus project and processed 100,000 bibliographic and authority records from multilingual catalogs of 12 European countries.

A Deposit for Digital Collections

We present the architecture and requirements for a novel system for managing the deposit of specific genres of digital publications in a deposit library. The system adopts a simple model for online publications and supports both harvesting and delivery models of deposit. This paper describes that system, and presents an evaluation after a trial period with the harvesting functions.

Using Ontologies for Enterprise Architecture Integration and Analysis

Enterprise architecture facilitates the alignment between different domains, such as business, applications and information technology. These domains must be described with description languages that best address the concerns of its stakeholders. However, current model-based enterprise architecture techniques are unable to integrate multiple descriptions languages either due to the lack of suitable extension mechanisms or because they lack the means to maintain the coherence, consistency and traceability between the representations of the multiple domains of the enterprise. On the other hand, enterprise architecture models are often designed and used for communication and not for automated analysis of its contents. Model analysis is a valuable tool for assessing the qualities of a model, such as conformance and completeness, and also for supporting decision making. This paper addresses these two issues found in model-based enterprise architecture: (1) the integration of domain description languages, and (2) the automated analysis of models. This proposal uses ontology engineering techniques to specify and integrate the different domains and reasoning and querying as a means to analyse the models. The utility of the proposal is shown through an evaluation scenario that involve the analysis of an enterprise architecture model that spans multiple domains.

An approach for named entity recognition in poorly structured data

This paper describes an approach for the task of named entity recognition in structured data containing free text as the values of its elements. We studied the recognition of the entity types of person, location and organization in bibliographic data sets from a concrete wide digital library initiative. Our approach is based on conditional random fields models, using features designed to perform named entity recognition in the absence of strong lexical evidence, and exploiting the semantic context given by the data structure. The evaluation results support that, with the specialized features, named entity recognition can be done in free text within structured data with an acceptable accuracy. Our approach was able to achieve a maximum precision of 0.91 at 0.55 recall and a maximum recall of 0.82 at 0.77 precision. The achieved results were always higher than those obtained with Stanford Named Entity Recognizer, which was developed for grammatically well-formed text. We believe this level of quality in named entity recognition allows the use of this approach to support a wide range of information extraction applications in structured data.

Identification of FRBR works within bibliographic databases: an experiment with UNIMARC and duplicate detection techniques

Many experiments and studies have been conducted on the application of FRBR as an implementation model for bibliographic databases, in order to improve the services of resource discovery and transmit better perception of the information spaces represented in catalogues. One of these applications is the attempt to identify the FRBR work instances shared by several bibliographic records. In our work we evaluate the applicability to this problem of techniques based on string similarity, used in duplicate detection procedures mainly by the database research community. We describe the particularities of the application of these techniques to bibliographic data, and empirically compare the results obtained with these techniques to those obtained by current techniques, which are based on exact matching. Experiments performed on the Portuguese national union catalogue show a significant improvement over currently used approaches.

Using ontologies to capture the semantics of a (business) process for digital preservation

IT-supported business processes and computationally intensive science (called e-science) have become increasingly ubiquitous in the last decades. Along with this trend comes the need to make at least the most important of these processes available for the long term, to allow later analysis of their execution, or even a re-execution. As such, the preservation of scientific experiments and their results enables others to reproduce and verify the results as well as build on the result of earlier work. All but the simplest processes require to be described by a multitude of information objects, as well as their interconnections and relations, to be successfully preserved. To enable a semantic description of these objects in a structured manner, we developed a formal meta-model that can be utilised in the digital preservation of a process. The meta-model describes classes of elements and their relations, in the form of ontologies, with a core ontology describing the generic concepts, and extension mechanisms to map supplementary ontologies describing more specific aspects. In this paper, we present the overall architecture and individual ontologies, and motivate their usefulness via the application to use cases from different domains.

Manage Risks through the Enterprise Architecture

The goal of Risk Management activities is to define prevention and control mechanisms to address the risks attached to specify activities and valuable assets. Many Risk Management efforts operate in silos with narrowly focused, functionally driven, and disjointed activities. That fact leads to a fragmented view of risks, where each activity uses its own language, customs and metrics. The lack of interconnection and holistic view of risks limits an organization-wide perception of risks, where interdependent risks are not anticipated, controlled or managed. In order to address the Risk Management interoperability and standardization issues, this paper proposes an alignment between Risk Management, Governance and Enterprise Architecture activities, providing a systematic support to map and trace identified risks to enterprise artifacts modeled within the Enterprise Architecture, supporting the overall strategy of any organization. We discuss the main relationships between Risk Management and Enterprise Architecture and propose an architecture to integrate risks concerns into the overall organization environment.