A model driven framework for geographic knowledge discovery

Geographic knowledge discovery (GKD) is the process of extracting information and knowledge from massive georeferenced databases. Usually the process is accomplished by two different systems, the Geographic Information Systems (GIS) and the data mining engines. However, the development of those systems is a complex task due to it does not follow a systematic, integrated and standard methodology. To overcome these pitfalls, in this paper, we propose a modeling framework that addresses the development of the different parts of a multilayer GKD process. The main advantages of our framework are that: (i) it reduces the design effort, (ii) it improves quality systems obtained, (iii) it is independent of platforms, (iv) it facilitates the use of data mining techniques on geo-referenced data, and finally, (v) it ameliorates the communication between different users.

Traditional knowledge of medicinal plants in the Serra de Mariola Natural Park, South-eastern Spain

The present study aims to inventory and analyse the ethnobotanical knowledge about medicinal plants in the Serra de Mariola Natural Park. In respect to traditional uses, 93 species reported by local informants were therapeutic, 27 food, 4 natural dyes and 13 handcrafts. We developed a methodology that allowed the location of individuals or vegetation communities with a specific popular use. We prepared a geographic information system (GIS) that included gender, family, scientific nomenclature and common names in Spanish and Catalan for each species. We also made a classification of 39 medicinal uses from ATC (Anatomical, Therapeutic, Chemical classification system). Labiatae (n=19), Compositae (n=9) and Leguminosae (n=6) were the families most represented among the plants used to different purposes in humans. Species with the most elevated cultural importance index (CI) values were Thymus vulgaris (CI=1.431), Rosmarinus officinalis (CI=1.415), Eryngium campestre (CI=1.325), Verbascum sinuatum (CI=1.106) and Sideritis angustifolia (CI=1.041). Thus, the collected plants with more therapeutic uses were: Lippia triphylla (12), Thymus vulgaris and Allium roseum (9) and Erygium campestre (8). The most repeated ATC uses were: G04 (urological use), D03 (treatment of wounds and ulcers) and R02 (throat diseases). These results were in a geographic map where each point represented an individual of any species. A database was created with the corresponding therapeutic uses. This application is useful for the identification of individuals and the selection of species for specific medicinal properties. In the end, knowledge of these useful plants may be interesting to revive the local economy and in some cases promote their cultivation.

Linguistic Knowledge of Reality: A Metaphysical Impossibility?

Reality contains information (significant) that becomes significances in the mind of the observer. Language is the human instrument to understand reality. But is it possible to attain this reality? Is there an absolute reality, as certain philosophical schools tell us? The reality that we perceive, is it just a fragmented reality of which we are part? The work that the authors present is an attempt to address this question from an epistemological, linguistic and logical-mathematical point of view.

An overview of the Applications of Natural Language to Information Systems

This introduction provides an overview of the state-of-the-art technology in Applications of Natural Language to Information Systems. Specifically, we analyze the need for such technologies to successfully address the new challenges of modern information systems, in which the exploitation of the Web as a main data source on business systems becomes a key requirement. It will also discuss the reasons why Human Language Technologies themselves have shifted their focus onto new areas of interest very directly linked to the development of technology for the treatment and understanding of Web 2.0. These new technologies are expected to be future interfaces for the new information systems to come. Moreover, we will review current topics of interest to this research community, and will present the selection of manuscripts that have been chosen by the program committee of the NLDB 2011 conference as representative cornerstone research works, especially highlighting their contribution to the advancement of such technologies.

Applying semantic knowledge to the automatic processing of temporal expressions and events in natural language

This paper addresses the problem of the automatic recognition and classification of temporal expressions and events in human language. Efficacy in these tasks is crucial if the broader task of temporal information processing is to be successfully performed. We analyze whether the application of semantic knowledge to these tasks improves the performance of current approaches. We therefore present and evaluate a data-driven approach as part of a system: TIPSem. Our approach uses lexical semantics and semantic roles as additional information to extend classical approaches which are principally based on morphosyntax. The results obtained for English show that semantic knowledge aids in temporal expression and event recognition, achieving an error reduction of 59% and 21%, while in classification the contribution is limited. From the analysis of the results it may be concluded that the application of semantic knowledge leads to more general models and aids in the recognition of temporal entities that are ambiguous at shallower language analysis levels. We also discovered that lexical semantics and semantic roles have complementary advantages, and that it is useful to combine them. Finally, we carried out the same analysis for Spanish. The results obtained show comparable advantages. This supports the hypothesis that applying the proposed semantic knowledge may be useful for different languages.

Noise-tolerance feasibility for restricted-domain Information Retrieval systems

Information Retrieval systems normally have to work with rather heterogeneous sources, such as Web sites or documents from Optical Character Recognition tools. The correct conversion of these sources into flat text files is not a trivial task since noise may easily be introduced as a result of spelling or typeset errors. Interestingly, this is not a great drawback when the size of the corpus is sufficiently large, since redundancy helps to overcome noise problems. However, noise becomes a serious problem in restricted-domain Information Retrieval specially when the corpus is small and has little or no redundancy. This paper devises an approach which adds noise-tolerance to Information Retrieval systems. A set of experiments carried out in the agricultural domain proves the effectiveness of the approach presented.

Information Search Habits of First Year College Students

New technologies have transformed teaching processes and enabled new ways of study and learning. In these activities, it is suspected that the students don't make good use of new available technologies or, in the best case, they are underused. The analysis of this issue with the design of strategies to correct any defects found is the motivation that supports the development of this work and the main purpose of it. Evaluate information search habits used by the student and analyse their deduct synthesis and processing capabilities of the results found. The researchers of this study are university teachers of first year subjects, which allows them to know the information search performances by students.

An authoring tool for decision support systems in context questions of ecological knowledge

Decision support systems (DSS) support business or organizational decision-making activities, which require the access to information that is internally stored in databases or data warehouses, and externally in the Web accessed by Information Retrieval (IR) or Question Answering (QA) systems. Graphical interfaces to query these sources of information ease to constrain dynamically query formulation based on user selections, but they present a lack of flexibility in query formulation, since the expressivity power is reduced to the user interface design. Natural language interfaces (NLI) are expected as the optimal solution. However, especially for non-expert users, a real natural communication is the most difficult to realize effectively. In this paper, we propose an NLI that improves the interaction between the user and the DSS by means of referencing previous questions or their answers (i.e. anaphora such as the pronoun reference in “What traits are affected by them?”), or by eliding parts of the question (i.e. ellipsis such as “And to glume colour?” after the question “Tell me the QTLs related to awn colour in wheat”). Moreover, in order to overcome one of the main problems of NLIs about the difficulty to adapt an NLI to a new domain, our proposal is based on ontologies that are obtained semi-automatically from a framework that allows the integration of internal and external, structured and unstructured information. Therefore, our proposal can interface with databases, data warehouses, QA and IR systems. Because of the high NL ambiguity of the resolution process, our proposal is presented as an authoring tool that helps the user to query efficiently in natural language. Finally, our proposal is tested on a DSS case scenario about Biotechnology and Agriculture, whose knowledge base is the CEREALAB database as internal structured data, and the Web (e.g. PubMed) as external unstructured information.