Conceptual framework of information retrieval system in the field of gastroenterology and hepatology

Background and aims: Optimal information retrieval is one of the components of achieving evidence-based medicine. This study aimed to present the Conceptual framework of information retrieval system in the field of Gastroenterology and hepatology. Methods: Based on the previous two studies, the characteristics of the optimal information retrieval system from the perspective of gastroenterologists and hepatologists and the most important approaches and technologies used in the design of information retrieval systems in medical sciences have been used in designing the proposed conceptual framework. Results: The conceptual framework of the information retrieval system in the gastroenterology and hepatology was presented in 4 sections and the technical and content features were presented in each section. Conclusions: Considering the comparison of this research with other researches in the field of frameworks and conceptual models of retrieval systems, special needs of users and using methods of personalization of information as well as conceptual mapping of resources have been the most important differences with other researches

Probabilistic hyperspace analogue to language

Song and Bruza introduce a framework for Information Retrieval(IR) based on Gardenfor's three tiered cognitive model; Conceptual Spaces. They instantiate a conceptual space using Hyperspace Analogue to Language (HAL to generate higher order concepts which are later used for ad-hoc retrieval. In this poster, we propose an alternative implementation of the conceptual space by using a probabilistic HAL space (pHAL). To evaluate whether converting to such an implementation is beneficial we have performed an initial investigation comparing the concept combination of HAL against pHAL for the task of query expansion. Our experiments indicate that pHAL outperforms the original HAL method and that better query term selection methods can improve performance on both HAL and pHAL

Student reaction to parallel hypertext and menu‐based interfaces

It is often necessary to consider the question of what sort of interface is most useful for retrieving information from a particular land of database. A small database of text‐based but multi‐faceted items is used here in order to compare ease and speed of retrieval from two commonly used combinations of interface — HyperCard on an Apple Macintosh and dBase III+ on a PC. For the restricted range of tasks employed here, the latter combination appears to be more acceptable to students with limited computer experience. However, in more general terms, the acceptability of an interface for information retrieval depends on what particular aspect of information retrieval is being emphasized, and what conceptual frameworks students bring to their tasks

Associative conceptual space-based information retrieval systems

In this `Information Era' with the availability of large collections of books, articles, journals, CD-ROMs, video films and so on, there exists an increasing need for intelligent information retrieval systems that enable users to find the information desired easily. Many attempts have been made to construct such retrieval systems, including the electronic ones used in libraries and including the search engines for the World Wide Web. In many cases, however, the so-called `precision' and `recall' of these systems leave much to be desired. In this paper, a new AI-based retrieval system is proposed, inspired by, among other things, the WEBSOM-algorithm. However, contrary to that approach where domain knowledge is extracted from the full text of all books, we propose a system where certain specific meta-information is automatically assembled using only the index of every document. This knowledge extraction process results into a new type of concept space, the so-called Associative Conceptual Space where the `concepts' as found in all documents are clustered using a Hebbian-type of learning algorithm. Then, each document can be characterised by comparing the concepts as occurring in it to those present in the associative conceptual space. Applying these characterisations, all documents can be clustered such that semantically similar documents lie close together on a Self-Organising Map. This map can easily be inspected by its user

Factory of realities: on the emergence of virtual spatiotemporal structures

The ubiquitous nature of modern Information Retrieval and Virtual World give rise to new realities. To what extent are these "realities" real? Which "physics" should be applied to quantitatively describe them? In this essay I dwell on few examples. The first is Adaptive neural networks, which are not networks and not neural, but still provide service similar to classical ANNs in extended fashion. The second is the emergence of objects looking like Einsteinian spacetime, which describe the behavior of an Internet surfer like geodesic motion. The third is the demonstration of nonclassical and even stronger-than-quantum probabilities in Information Retrieval, their use. Immense operable datasets provide new operationalistic environments, which become to greater and greater extent "realities". In this essay, I consider the overall Information Retrieval process as an objective physical process, representing it according to Melucci metaphor in terms of physical-like experiments. Various semantic environments are treated as analogs of various realities. The readers' attention is drawn to topos approach to physical theories, which provides a natural conceptual and technical framework to cope with the new emerging realities.Comment: 21 p

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