A model for information retrieval driven by conceptual spaces

A retrieval model describes the transformation of a query into a set of documents. The question is: what drives this transformation? For semantic information retrieval type of models this transformation is driven by the content and structure of the semantic models. In this case, Knowledge Organization Systems (KOSs) are the semantic models that encode the meaning employed for monolingual and cross-language retrieval. The focus of this research is the relationship between these meanings’ representations and their role and potential in augmenting existing retrieval models effectiveness. The proposed approach is unique in explicitly interpreting a semantic reference as a pointer to a concept in the semantic model that activates all its linked neighboring concepts. It is in fact the formalization of the information retrieval model and the integration of knowledge resources from the Linguistic Linked Open Data cloud that is distinctive from other approaches. The preprocessing of the semantic model using Formal Concept Analysis enables the extraction of conceptual spaces (formal contexts)that are based on sub-graphs from the original structure of the semantic model. The types of conceptual spaces built in this case are limited by the KOSs structural relations relevant to retrieval: exact match, broader, narrower, and related. They capture the definitional and relational aspects of the concepts in the semantic model. Also, each formal context is assigned an operational role in the flow of processes of the retrieval system enabling a clear path towards the implementations of monolingual and cross-lingual systems. By following this model’s theoretical description in constructing a retrieval system, evaluation results have shown statistically significant results in both monolingual and bilingual settings when no methods for query expansion were used. The test suite was run on the Cross-Language Evaluation Forum Domain Specific 2004-2006 collection with additional extensions to match the specifics of this model

Quality framework for semantic interoperability in health informatics: definition and implementation

Aligned with the increased adoption of Electronic Health Record (EHR) systems, it is recognized that semantic interoperability provides benefits for promoting patient safety and continuity of care. This thesis proposes a framework of quality metrics and recommendations for developing semantic interoperability resources specially focused on clinical information models, which are defined as formal specifications of structure and semantics for representing EHR information for a specific domain or use case. This research started with an exploratory stage that performed a systematic literature review with an international survey about the clinical information modelling best practice and barriers. The results obtained were used to define a set of quality models that were validated through Delphi study methodologies and end user survey, and also compared with related quality standards in those areas that standardization bodies had a related work programme. According to the obtained research results, the defined framework is based in the following models: Development process quality model: evaluates the alignment with the best practice in clinical information modelling and defines metrics for evaluating the tools applied as part of this process. Product quality model: evaluates the semantic interoperability capabilities of clinical information models based on the defined meta-data, data elements and terminology bindings. Quality in use model: evaluates the suitability of adopting semantic interoperability resources by end users in their local projects and organisations. Finally, the quality in use model was implemented within the European Interoperability Asset register developed by the EXPAND project with the aim of applying this quality model in a broader scope to contain any relevant material for guiding the definition, development and implementation of interoperable eHealth systems in our continent. Several European projects already expressed interest in using the register, which will now be sustained by the European Institute for Innovation through Health Data

ABSTRACT Maximal Termsets as a Query Structuring Mechanism

Search engines process queries conjunctively to restrict the size of the answer set. Further, it is not rare to observe a mismatch between the vocabulary used in the text of Web pages and the terms used to compose the Web queries. The combination of these two features might lead to irrelevant query results, particularly in the case of more specific queries composed of three or more terms. To deal with this problem we propose a new technique for automatically structuring Web queries as a set of smaller subqueries. To select representative subqueries we use information on their distributions in the document collection. This can be adequately modeled using the concept of maximal termsets derived from the formalism of association rules theory. Experimentation shows that our technique leads to improved results. For the TREC-8 test collection, for instance, our technique led to gains in average precision of roughly 28 % with regard to a BM25 ranking formula

ABSTRACT Maximal Termsets as a Query Structuring Mechanism

Search engines process queries conjunctively to restrict the size of the answer set. Further, it is not rare to observe a mismatch between the vocabulary used in the text of Web pages and the terms used to compose the Web queries. The combination of these two features might lead to irrelevant query results, particularly in the case of more specific queries composed of three or more terms. To deal with this problem we propose a new technique for automatically structuring Web queries as a set of smaller subqueries. To select representative subqueries we use information on their distributions in the document collection. This can be adequately modeled using the concept of maximal termsets derived from the formalism of association rules theory. Experimentation shows that our technique leads to improved results. For the TREC-8 test collection, for instance, our technique led to gains in average precision of roughly 28 % with regard to a BM25 ranking formula