Ontology based Approach for Precision Agriculture

In this paper, we propose a framework of knowledge for an agriculture ontology which can be used for the purpose of smart agriculture systems. This ontology not only includes basic concepts in the agricultural domain but also contains geographical, IoT, business subdomains, and other knowledge extracted from various datasets. With this ontology, any users can easily understand agricultural data links between them collected from many different data resources. In our experiment, we also import country, sub-country and disease entities into this ontology as basic entities for building agricultural linked datasets later

A multilingual ontology for infectious disease surveillance: rationale, design and challenges

A lack of surveillance system infrastructure in the Asia-Pacific region is seen as hindering the global control of rapidly spreading infectious diseases such as the recent avian H5N1 epidemic. As part of improving surveillance in the region, the BioCaster project aims to develop a system based on text mining for automatically monitoring Internet news and other online sources in several regional languages. At the heart of the system is an application ontology which serves the dual purpose of enabling advanced searches on the mined facts and of allowing the system to make intelligent inferences for assessing the priority of events. However, it became clear early on in the project that existing classification schemes did not have the necessary language coverage or semantic specificity for our needs. In this article we present an overview of our needs and explore in detail the rationale and methods for developing a new conceptual structure and multilingual terminological resource that focusses on priority pathogens and the diseases they cause. The ontology is made freely available as an online database and downloadable OWL file

Agricultural Knowledge Discovery from Semi-Structured Text

This research aims to develop automatic knowledge discovery system from semi-structured Thai text for supporting plant diagnosis. Plant disease diagnosis is very important for farmers to be able to cure infected plants before infections become more severe. Prior to diagnosis, farmers need to gain knowledge retrieved primarily from text, including unstructured and semi-structured document. As this knowledge is spread throughout the text, collecting the required knowledge in its entirety is time consuming. An alternative to the manual approach is the use of automatic knowledge discovery processes to acquire concise knowledge for plant disease diagnosis. Then the knowledge discovery process consists of at least two main steps: knowledge extraction and knowledge generalization. However, there are two major problems in this research. First is the knowledge extraction problem attributed to linguistics, which can be solved by NLP technique such as zero anaphora, ellipsis, etc. And second is the generalization problem due to obtaining general knowledge that is intrinsically uncertain and incomplete. To solve these problems we propose three combination techniques: First, a template-matching rule is used to extract the knowledge from the agricultural document on website. Second, a Monte Carlo simulation technique is applied to solve the incomplete knowledge of plant disease symptoms from the texts. And the third one is the use of the fuzzy concept to determine the weighted average of the generality of the symptom from each pathogen type or insect type. The results of knowledge generalization will then be evaluated by experts, and knowledge extraction will be evaluated in term of precision, and recall. It is important to note that this is being conducted in part of ongoing research

Country Report and Activity from Thailand: Ontology Construction and Maintenance System in Agricultural Domain

Ontology is a collection of concepts and their interrelationships, which provide an essential resource to enhance the performance of Information Processing system. Creating ontology by the expert is an expensive task and it is endless task for ontology maintenance since its content relies on user requirement. It is also the fact that information in the real world has been increased. Especially in scientific documents, there are rapidly new terms and instance generation and difficult to follow up. By this reason, it is necessary to construct ontology automatically in order to update Ontology data

Information Extraction for Agricultural Information Access

For agricultural information access, some useful information such as how to analyze symptom of plant diseases and how to protect plant from diseases is in unstructured format and scattered the entire document. Moreover, information is rapidly increasing causing information to become overwhelming in size, so extraction of only significant and interesting information is necessary. Information Extraction in traditional way extracts a set of related entities in the format of slot and filler, but the representation of some information such as symptom and treatment can not be limited to set of related entities. In this paper, we present Information Extraction system for Thai documents in agricultural domain that has information about the analysis and treatment of plant disease. This type of information has to be explained in a set of continuous sentences, so the extraction of the set of related entities is not enough. In this case, we introduce “Explanation Information”, the combination of sentences that describe the topic of interest, to fill in the extraction slot. However, extracting the relevant sentence