The Analysis of Existing Experience for the Ethnobotanical Information System

Ethnobotanical researches reflect the conventional learning of a region. Over the previous decade, medical plants which used for healing indigenous people has become a significant notion among the people and impacted improvement of scientific and ethnobotanical knowledge and investigations of eliminating health problems. A public database has been based on data assembled from various verifiable sources, including journals, travel records, and treatises on therapeutic plants, composed by explorers, botanists, doctors, researchers who went to the nations during the most recent three centuries. In addition, ethnobotanical data depicted in chronicled natural accumulations and in Ancient and Medieval writings from the inquired district have been incorporated into the database. The databases have to be sufficiently adaptable to illustrate a valuable tool for analysts who need to store and analyze present and past ethnobotanical data from the researched location. The ethnobotanical researches are improved in Azerbaijan day by day. The database is used for informing people about some national plants which are growing in the different region of Azerbaijan. The ethnobotanical databases from different countries are analyzed in this article.There are used some special methods for comparing the differences among these databases as data mining and text mining. As a first step the suitable databases are gathered for our investigation, then are defined the best information systems that are used in many countries\u27 biologists and scientists and the end is observed advantages and disadvantages of all existing ethnobotanical databases which we researched. The features of information systems are evaluated. The results demonstrated each of databases has its very own quality, but none has turned a standard form for universal research. The reason is very basic: none of these databases enable specialists to include their own information. There is also illustrated sample structure, main tables and key components of the ethnobotanical database.The obtained results, while a few ethnobotanical databases existing, none are satisfactory answers for worldwide work, and none enable analysts to include their very own information. There is a need brought together all essential properties of existing databases, and creating a free database that encourages ethnobotanical research. Due to the rise and quick improvement in the field of data advances, it has now turned out to be conceivable to digitize, oversee and make ethnobotanical information accessible to a more extensive gathering of people

Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

Towards a Universal Wordnet by Learning from Combined Evidenc

Lexical databases are invaluable sources of knowledge about words and their meanings, with numerous applications in areas like NLP, IR, and AI. We propose a methodology for the automatic construction of a large-scale multilingual lexical database where words of many languages are hierarchically organized in terms of their meanings and their semantic relations to other words. This resource is bootstrapped from WordNet, a well-known English-language resource. Our approach extends WordNet with around 1.5 million meaning links for 800,000 words in over 200 languages, drawing on evidence extracted from a variety of resources including existing (monolingual) wordnets, (mostly bilingual) translation dictionaries, and parallel corpora. Graph-based scoring functions and statistical learning techniques are used to iteratively integrate this information and build an output graph. Experiments show that this wordnet has a high level of precision and coverage, and that it can be useful in applied tasks such as cross-lingual text classification

Review of Semantic Importance and Role of using Ontologies in Web Information Retrieval Techniques

The Web contains an enormous amount of information, which is managed to accumulate, researched, and regularly used by many users. The nature of the Web is multilingual and growing very fast with its diverse nature of data including unstructured or semi-structured data such as Websites, texts, journals, and files. Obtaining critical relevant data from such vast data with its diverse nature has been a monotonous and challenging task. Simple key phrase data gathering systems rely heavily on statistics, resulting in a word incompatibility problem related to a specific word's inescapable semantic and situation variants. As a result, there is an urgent need to arrange such colossal data systematically to find out the relevant information that can be quickly analyzed and fulfill the users' needs in the relevant context. Over the years ontologies are widely used in the semantic Web to contain unorganized information systematic and structured manner. Still, they have also significantly enhanced the efficiency of various information recovery approaches. Ontological information gathering systems recover files focused on the semantic relation of the search request and the searchable information. This paper examines contemporary ontology-based information extraction techniques for texts, interactive media, and multilingual data types. Moreover, the study tried to compare and classify the most significant developments utilized in the search and retrieval techniques and their major disadvantages and benefits

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems