Question-Answering for Agricultural Open Data - Transactions on Large-Scale Data-and Knowledge-Centered Systems - page 19

gtcprop:hasYellowSpot "possible reason for the case (wikipedia uri)";
gtcprop:hasMosaic "possible reason for the case (wikipedia uri)";

gtcprop:hasFade "possible reason for the case (wikipedia uri)";

gtcprop:hasKnot "possible reason for the case (wikipedia uri)";

gtcprop:hasMold "possible reason for the case (wikipedia uri)";

gtcprop:hasInsect "possible reason for the case (wikipedia uri)";

gtcprop:hasNoFlower "possible reason for the case (wikipedia uri)";

# For work logging
gtcprop:plantingSpace

"indoor or outdoor";

gtcprop:plantingDateTime "date and hour of planting";

gtcprop:plantingAddress "address";

gtcprop:plantingWeather "weather";

gtcprop:plantingHighTemp "highest temperature of the day";

gtcprop:plantingLowTemp "lowest temperature of the day";

gtcprop:flowerSpace "indoor or outdoor";

gtcprop:flowerDateTime "date and hour of blooming";

gtcprop:flowerAddress "address";

gtcprop:flowerWeather "weather";

gtcprop:flowerHighTemp "highest temperature of the day";

gtcprop:flowerLowTemp "lowest temperature of the day";

gtcprop:wateringSpace "indoor or outdoor";

gtcprop:wateringDateTime "date and hour of watering";

gtcprop:wateringAddress "address";

gtcprop:wateringWeather "weather";

gtcprop:wateringHighTemp "highest temperature of the day";

gtcprop:wateringLowTemp "lowest temperature of the day";

gtcprop:fertilizingSpace "indoor or outdoor";
gtcprop:fertilizingDateTime "date and hour of fertilization";
gtcprop:fertilizingAddress "address";
gtcprop:fertilizingWeather "weather";
gtcprop:fertilizingHighTemp "highest temperature of the day";
gtcprop:fertilizingLowTemp "lowest temperature of the day";

gtcprop:purchaseSpace "indoor or outdoor";

gtcprop:purchaseDateTime "date and hour of purchase";

gtcprop:purchaseAddress "address";

gtcprop:purchaseWeather "weather";

gtcprop:purchaseHighTemp "highest temperature of the day";

gtcprop:purchaseLowTemp "lowest temperature of the day".

3.2 Agricultural Information Extraction from Web

This section describes a method for extracting cultivation knowledge from the
Web, and constructing LOD. Our proposed method is inspired by [ 8 ] at AAAI10,
which proposed a semi-automatic extraction service from the Web using the exist-
ing ontologies, where several learning methods are combined to reduce extraction
errors. Although [ 8 ] focused on the world knowledge, and thus the granularity
and the number of properties for each instance are rather abstract and lim-
ited, our method retains the variety of the properties and keeps the extraction
accuracy by restricting the domain of interest.

LOD Extraction Method. We developed a semi-automatic method for grow-
ing the existing LOD to collect the necessary plant information from the Web
and correlate it to DBpedia, which includes a dependency parsing method based