Ontology Engineering and Knowledge Services for Agriculture Domain

This paper presents a knowledge service system for the domain of agriculture. Three key issues for providing knowledge services are how to improve the access of unstructured and scattered information for the non-specialist users, how to provide adequate information to knowledge workers and how to provide the information requiring highly focused and related information. Cyber-Brain has been designed as a platform that combines approaches based on knowledge engineering and language engineering to gather knowledge from various sources and to provide the effective knowledge service. Based on specially designed ontology for practical service scenarios, it can aggregate knowledge from Internet, digital archives, expert, and other resources for providing one-stop-shop knowledge services. The domain specific and task oriented ontology also enables advanced search and allows the system ensures that knowledge service could improve the user benefit. Users are presented with the necessary information closely related to their information need and thus of potential high interest. This paper presents several service scenarios for different end-users and reviews ontology engineering and its life cycle for supporting AOS （Agricultural Ontology Services） Vocbench which is the heart of knowledge services in agriculture domain.

Mining Causality for Explanation Knowledge from Text

Mining causality is essential to provide a diagnosis within multiple sentences or EDUs （Elementary Discourse Unit） This research aims at extracting the causality existing The research emphasizes the use of causality verbs because they make explicit in a certain way the consequent events of a cause, e.g., ＂Aphids suck the sap from rice leaves. Then leaves will shrink. Later, they will become yellow and dry.＂. A verb can also be the causal-verb link between cause and effect within EDU（s）, e.g., ＂Aphids suck the sap from rice leaves causing leaves to be shrunk＂ （＂causing＂ is equivalent, to a causal-verb link in Thai）. The research confronts two main problems： identifying the interesting causality events from documents and identifying their boundaries. Then, we propose mining on verbs by using two different machine learning techniques, Naive Bayes classifier and Support Vector Machine. The resulted mining rules will be used for the identification and the causality extraction of the multiple EDUs from text. Our multiple EDUs extraction shows 0.88 precision with 0.75 recall from Naive Bayes classifier and 0.89 precision with 0.76 recall from Support Vector Machine.