Narrow Results

The advancement of the semantic web and Linked Open Data (LOD) cloud has led to the creation and integration of various knowledge bases defined by ontologies. A significant challenge within the LOD paradigm is identifying resources that refer to the same real-world object to enable large-scale data integration and sharing. In this context, instance matching has emerged as a key solution, linking co-referent instances from heterogeneous data sources using owl:sameAs links. Traditional approaches focus on schema-level matching but often fail to address property-level heterogeneity. Moreover, given the large scale of instances, examining all possible instance pairs is impractical. This paper proposes a scalable and efficient instance-matching approach using MongoDb (Humongous database) and Lucene. MongoDb stores instances at any scale and Lucene uses inverted indexes to identify matching candidates. Experiments on the instance matching track from the Ontology Alignment Evaluation Initiative (OAEI’2022) show that our approach matches the F-measure score of RE-Miner, the top performer in OAEI’2020, while surpassing all other participants in OAEI’2020, 2021 and 2022. Additionally, it operates 17 times faster than RE-Miner, four times faster than Lily and 15 times faster than LogMap, the fastest in OAEI’2020, 2021 and 2022, respectively. Moreover, we evaluate our approach on other knowledge bases from OAEI’2010. Once again, our approach gets highly competitive resuts compared to state-of-the-art approaches.

In this paper, we designed a system of scientific and technological project based on classification. Our system uses Check Weight Algorithm to extract keywords and Solr distributed index database of Lucene engine as the core to provide full text indexing and search open source enterprise platforms. This design enables our system to effectively improve the query speed and reduce computational complexity of the traditional query. Based on semantic technology project similarity calculation, it can also hasten the speed of the traditional rechecking system and improve the accuracy of rechecking the effect.