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Semantic modeling of temporal information in a database for biotope inventories

机译:生物群落清单数据库中时间信息的语义建模

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Database queries often include explicit or implicit temporal reference. Explicit queries ask e.g. for information in a given year or time period, and implicit queries e.g. look for information about changes over time like growth in a certain area.rnOur datacenter nature and landscape (DNL) is basically a relational database with thematic data combined with a geographic information system (GIS) holding spatial data. The DNL has been semantically enriched with a bilingual eco- ontology and corresponding algorithms (semantic search, reasoning, consistency checking). The main purpose of the ontology is to allow open and intuitive search facilities for non-expert users. However so far there is no semantic support for temporal queries included yet.rnSpatio-thematic information is stored in an OWL/DL ontology. Therefore an approach for temporal information is searched which fits seamless in the existing environment. Temporal information is available in the DNL database, however it is often given in a very coarse granularity (months or years), sometimes it is entirely missing. As a consequence the challenge of semantic support for temporal queries in the DNL, also includes handling vague and incomplete temporal information.rnThe work presented here, starts from typical temporal user queries (which were collected in use cases) and checks different approaches for their usability in our real-world eco-database DNL. It is found, that a sub-rnset of Allen's interval algebra relations, which actually corresponds to RCC-5 relations best fits our needs. It is planned to be integrated into the existing thematic ontology in order to allow spatio-temporal and thematic queries.
机译:数据库查询通常包括显式或隐式的时间引用。明确的查询例如用于给定年份或时间段内的信息,以及隐式查询,例如寻找有关随时间变化的信息,例如某个区域的增长。我们的数据中心自然和景观(DNL)本质上是一个关系数据库,其中包含主题数据和保存空间数据的地理信息系统(GIS)。 DNL在语义上已经丰富了双语生态学和相应的算法(语义搜索,推理,一致性检查)。本体的主要目的是为非专家用户提供开放和直观的搜索功能。但是,到目前为止,尚不包括对时间查询的语义支持。时空主题信息存储在OWL / DL本体中。因此,搜索一种适合于现有环境的时间信息方法。 DNL数据库中提供了时间信息,但是通常以非常粗糙的粒度(数月或数年)给出,有时会完全丢失。因此,在DNL中为时态查询提供语义支持的挑战还包括处理模糊和不完整的时态信息。此处介绍的工作从典型的时态用户查询(在用例中收集)开始,并检查其可用性的不同方法在我们实际的生态数据库DNL中。发现,实际上对应于RCC-5关系的艾伦区间代数关系的子集最适合我们的需求。计划将其集成到现有的主题本体中,以允许时空和主题查询。

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