Papers Accepted at WI-IAT 2020

We are happy to announce that we got two accepted for presentation at WI-IAT 20 (International Joint Conference on Web Intelligence and Intelligent Agent Technology). WI-IAT 20 provides a premier international forum to bring together researchers and practitioners from diverse fields for presentation of original research results, as well as exchange and dissemination of innovative and practical development experiences on Web intelligence and intelligent agent technology research and applications.

Here is the pre-print of the accepted paper with its abstract:

  • Multilingual Ontology Merging Using Cross-lingual Matching
    By Shimaa Ibrahim, Said Fathalla, Jens Lehmann, and Hajira Jabeen.
    Abstract With the growing amount of multilingual data on the Semantic Web, several ontologies (in different natural languages) have been developed to model the same domain. Creating multilingual ontologies by merging such monolingual ones is important to promote semantic interoperability among different ontologies in different natural languages. This is a step towards achieving the multilingual Semantic Web. In this paper, we propose MULON, an approach for merging monolingual ontologies in different natural languages producing a multilingual ontology. MULON approach comprises three modules; Preparation Module, Merging Module, and Assessment Module. We consider both classes and properties in the merging process. We present three real-world use cases describing the usability of the MULON approach in different domains. We assess the quality of the merged ontologies using a set of predefined assessment metrics. MULON has been implemented using Scala and Apache Spark under an open-source license. We have compared our cross-lingual matching results with the results from the Ontology Alignment Evaluation Initiative (OAEI 2019). MULON has achieved relatively high precision, recall, and F-measure in comparison to three state-of-the-art approaches in the matching process and significantly higher coverage without any redundancy in the merging process.
  • OWLStats: Distributed Computation of OWL Dataset Statistics
    By Heba Mohamed, Said Fathalla, Jens Lehmann, and Hajira Jabeen.
    Abstract Nowadays, ontologies are used in various application areas, involving Artificial Intelligence, Natural Language Processing, Data Integration, and Knowledge Management. It is essential to know the internal structure, distribution, and coherence of the published datasets to make it easier for reuse, interlink, integrate, infer, or query. Therefore, there is a pressing need to obtain a clear view of OWL datasets became more prevalent. In this paper, we present OWLStats, a software component for computing statistical information about large scale OWL datasets in a distributed manner. We present the primary distributed in-memory approach for computing 32 different statistical criteria for OWL datasets utilizing Apache Spark, which can scale horizontally to a cluster of machines. OWLStats has been integrated into the SANSA framework. The preliminary results prove that OWLStats is linearly scalable in terms of data scalability.