@article{ao_cheng_chirkova_kolaitis_2023, title={Theory and Practice of Relational-to-RDF Temporal Data Exchange and Query Answering}, volume={15}, ISSN={["1936-1955"]}, DOI={10.1145/3591359}, abstractNote={We consider the problem of answering temporal queries on RDF stores, in presence of atemporal RDFS domain ontologies, of relational data sources that include temporal information, and of rules that map the domain information in the source schemas into the target ontology. Our proposed practice-oriented solution consists of two rule-based domain-independent algorithms. The first algorithm materializes target RDF data via a version of data exchange that enriches both the data and the ontology with temporal information from the relational sources. The second algorithm accepts as inputs temporal queries expressed in terms of the domain ontology using a lightweight temporal extension of SPARQL, and ensures successful evaluation of the queries on the materialized temporally-enriched RDF data. To study the quality of the information generated by the algorithms, we develop a general framework that formalizes the relational-to-RDF temporal data-exchange problem. The framework includes a chase formalism and a formal solution for the problem of answering temporal queries in the context of relational-to-RDF temporal data exchange. In this article, we present the algorithms and the formal framework that proves correctness of the information output by the algorithms, and also report on the algorithm implementation and experimental results for two application domains.}, number={2}, journal={ACM JOURNAL OF DATA AND INFORMATION QUALITY}, author={Ao, Jing and Cheng, Zehui and Chirkova, Rada and Kolaitis, Phokion G.}, year={2023}, month={Jun} }
 @article{ao_dinakaran_yang_wright_chirkova_2021, title={Trustworthy Knowledge Graph Population From Texts for Domain Query Answering}, ISSN={["2639-1589"]}, DOI={10.1109/BigData52589.2021.9671514}, abstractNote={Obtaining answers to domain-specific questions over large-scale unstructured (text) data is an important component of data analytics in many application domains. As manual question answering does not scale to large text corpora, it is common to use information extraction (IE) to preprocess the texts of interest prior to posing the questions. This is often done by transforming text corpora into the knowledge-graph (KG) triple format that is suitable for efficient processing of the user questions in graph-oriented data-intensive systems.In a number of real-life scenarios, trustworthiness of the answers obtained from domain-specific texts is vital for downstream decision making. In this paper we focus on one critical aspect of trustworthiness, which concerns aligning with the given domain vocabularies (ontologies) those KG triples that are obtained from the source texts via IE solutions. To address this problem, we introduce a scalable domain-independent text-to-KG approach that adapts to specific domains by using domain ontologies, without having to consult external triple repositories. Our IE solution builds on the power of neural-based learning models and leverages feature engineering to distinguish ontology-aligned data from generic data in the source texts. Our experimental results indicate that the proposed approach could be more dependable than a state-of-the-art IE baseline in constructing KGs that are suitable for trustworthy domain question answering on text data.}, journal={2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)}, author={Ao, Jing and Dinakaran, Swathi and Yang, Hungjian and Wright, David and Chirkova, Rada}, year={2021}, pages={4590–4599} }