Ian Horrocks

• Igne, Federico; Germano, Stefano & Horrocks, Ian (2023). Conjunctive query answering over unrestricted OWL 2 ontologies. Semantic Web Journal. ISSN 1570-0844. 14(6), p. 997–1050. doi: 10.3233/SW-233382.
• Liu, Shuwen; Grau, Bernardo Cuenca; Horrocks, Ian & Kostylev, Egor (2023). Revisiting Inferential Benchmarks for Knowledge Graph Completion, Proceedings of the 20th International Conference on Principles of Knowledge Representation and Reasoning, KR 2023. ACM Digital Library. ISSN 9781956792027. doi: 10.24963/KR.2023/45.
• Hu, Pan; Motik, Boris & Horrocks, Ian (2022). Modular materialisation of Datalog programs. Artificial Intelligence. ISSN 0004-3702. 308. doi: 10.1016/j.artint.2022.103726. Show summary

  Answering queries over large datasets extended with Datalog rules plays a key role in numerous data management applications, and it has been implemented in several highly optimised Datalog systems in both academic and commercial contexts. Many systems implement reasoning via materialisation, which involves precomputing all consequences of the rules and the dataset in a preprocessing step. Some systems also use incremental reasoning algorithms, which can update the materialisation efficiently when the input dataset changes. Such techniques allow queries to be processed without any reference to the rules, so they are often used in applications where the performance of query answering is critical. Existing materialisation and incremental reasoning techniques enumerate all possible ways to apply rules to the data in order to derive all relevant consequences. This, however, can be inefficient because derivations of rules commonly used in practice are redundant; for example, rules axiomatising a binary predicate as symmetric and transitive can have a cubic number of applications, yet they can derive at most a quadratic number of facts. Such redundancy can be a significant source of overhead in practice and can prevent Datalog systems from successfully processing large datasets. To address this issue, in this paper we present a novel framework for modular materialisation and incremental reasoning. Our key idea is that, for certain combinations of rules commonly used in practice, all consequences can be derived using specialised procedures that do not necessarily enumerate all possible rule applications. Thus, our framework supports materialisation and incremental reasoning via a collection of modules. Each module is responsible for deriving consequences of a subset of the program, by using either standard rule application or proprietary algorithms. We prove that such an approach is complete as long as each module satisfies certain properties. Our formalisation of a module is very general, and in fact it allows modules to keep arbitrary auxiliary information. We also show how to realise custom procedures for four types of modules: transitivity, symmetry–transitivity, chain rules, and sequencing elements of a total order. Finally, we demonstrate empirically that using our custom procedures can speed up materialisation and incremental reasoning by several orders of magnitude on several well-known benchmarks. Thus, our technique has the potential to significantly improve the scalability of Datalog reasoners.

• Ronca, Alessandro; Kaminski, Mark; Grau, Bernardo Cuenca & Horrocks, Ian (2022). The delay and window size problems in rule-based stream reasoning. Artificial Intelligence. ISSN 0004-3702. 306. doi: 10.1016/j.artint.2022.103668. Show summary

  In recent years, there has been an increasing interest in extending stream processing engines with rule-based temporal reasoning capabilities. To ensure correctness, such systems must be able to output results over the partial data received so far as if the entire (infinite) stream had been available; furthermore, these results must be streamed out as soon as the relevant data is received, thus incurring the minimum possible delay; finally, due to memory limitations, systems can only keep a limited history of previous facts in memory to perform further computations. These requirements pose significant theoretical and practical challenges since temporal rules can derive new information and propagate it both towards past and future time points; as a result, streamed answers can depend on data that has not yet been received, as well as on data that arrived far in the past. Towards developing a solid foundation for practical rule-based stream reasoning, we propose and study in this paper a suite of decision problems that can be exploited by stream reasoning algorithms to tackle the aforementioned challenges, and provide tight complexity bounds for a core temporal extension of Datalog. All of the problems we consider can be solved at design time (under reasonable assumptions), prior to the processing of any data. Solving these problems enables the use of reasoning algorithms that process the input streams incrementally using a sliding window, while at the same time supporting an expressive rule-based knowledge representation language and minimising both latency and memory consumption.

• Kaminski, Mark; Kostylev, Egor; Grau, Bernardo Cuenca; Motik, Boris & Horrocks, Ian (2022). The Complexity and Expressive Power of Limit Datalog. Journal of the ACM. ISSN 0004-5411. 69(1). doi: 10.1145/3495009.
• Tena Cucala, David; Cuenca grau, Bernardo & Horrocks, Ian (2021). Pay-as-you-go consequence-based reasoning for the description logic SROIQ. Artificial Intelligence. ISSN 0004-3702. 298. doi: 10.1016/j.artint.2021.103518.
• Chen, Jiaoyan; Jimenez-Ruiz, Ernesto; Horrocks, Ian; Antonyrajah, Denvar; Hadian, Ali & Lee, Jaehun (2021). Augmenting Ontology Alignment by Semantic Embedding and Distant Supervision. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 12731. doi: 10.1007/978-3-030-77385-4_23.
• Chen, Jiaoyan; Jimenez-Ruiz, Ernesto; Horrocks, Ian; Chen, Xi & Myklebust, Erik Bryhn (2021). An assertion and alignment correction framework for large scale knowledge bases. Semantic Web Journal. ISSN 1570-0844. 14(1), p. 29–53. doi: 10.3233/SW-210448. Show summary

  Various knowledge bases (KBs) have been constructed via information extraction from encyclopedias, text and tables, as well as alignment of multiple sources. Their usefulness and usability is often limited by quality issues. One common issue is the presence of erroneous assertions and alignments, often caused by lexical or semantic confusion. We study the problem of correcting such assertions and alignments, and present a general correction framework which combines lexical matching, context-aware sub-KB extraction, semantic embedding, soft constraint mining and semantic consistency checking. The framework is evaluated with one set of literal assertions from DBpedia, one set of entity assertions from an enterprise medical KB, and one set of mapping assertions from a music KB constructed by integrating Wikidata, Discogs and MusicBrainz. It has achieved promising results, with a correction rate (i.e., the ratio of the target assertions/alignments that are corrected with right substitutes) of 70.1%, 60.9% and 71.8%, respectively.

• Mailis, Theofilos; Kotidis, Yannis; Nikolopoulos, Vaggelis; Kharlamov, Evgeny; Horrocks, Ian & Ioannidis, Yannis (2019). MV-Index: An efficient index for graph-query containment. CEUR Workshop Proceedings. ISSN 1613-0073. 2456(50), p. 193–196.
• Chen, Jiaoyan; Jimenez-Ruiz, Ernesto; Horrocks, Ian & Sutton, Charles (2019). ColNet: Embedding the Semantics of Web Tables for Column Type Prediction. Proceedings of the AAAI Conference on Artificial Intelligence. ISSN 2159-5399. 30, p. 29–36. doi: 10.1609/aaai.v33i01.330129. Show summary

  Automatically annotating column types with knowledge base (KB) concepts is a critical task to gain a basic understanding of web tables. Current methods rely on either table metadata like column name or entity correspondences of cells in the KB, and may fail to deal with growing web tables with incomplete meta information. In this paper we propose a neural network based column type annotation framework named ColNet which is able to integrate KB reasoning and lookup with machine learning and can automatically train Convolutional Neural Networks for prediction. The prediction model not only considers the contextual semantics within a cell using word representation, but also embeds the semantics of a column by learning locality features from multiple cells. The method is evaluated with DBPedia and two different web table datasets, T2Dv2 from the general Web and Limaye from Wikipedia pages, and achieves higher performance than the state-of-the-art approaches.

• Savković, Ognjen; Kharlamov, Evgeny; Ringsquandl, Martin; Xiao, Guohui; Mehdi, Gulnar & Kalayc, Elem Güzel [Show all 8 contributors for this article] (2018). Semantic diagnostics of smart factories. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 11341 LNCS, p. 277–294. doi: 10.1007/978-3-030-04284-4_19.
• Potter, Anthony; Motik, Boris; Nenov, Yavor & Horrocks, Ian (2018). Dynamic Data Exchange in Distributed RDF Stores. IEEE Transactions on Knowledge and Data Engineering. ISSN 1041-4347. 30(12), p. 2312–2325. doi: 10.1109/TKDE.2018.2818696.
• Pinkel, Christoph; Binning, Carsten; Jimenez-Ruiz, Ernesto; Kharlamov, Evgeny; May, Wolfgang & Nikolov, Andriy [Show all 12 contributors for this article] (2018). RODI: Benchmarking Relational-to-Ontology Mapping Generation Quality. Semantic Web Journal. ISSN 1570-0844. 9(1), p. 25–52. doi: 10.3233/SW-170268. Full text in Research Archive
• Jimenez-Ruiz, Ernesto; Carapella, Valentina; Lukaschuk, Elena; Aung, Nay; Fung, Kenneth & Paiva, Jose [Show all 11 contributors for this article] (2017). Towards the creation of the cardiovascular magnetic resonance quality assessment ontology (CMR-QA). CEUR Workshop Proceedings. ISSN 1613-0073. 1795.
• Soylu, Ahmet; Giese, Martin; Jimenez-Ruiz, Ernesto; Kharlamov, Evgeny; Schlatte, Rudolf & Neuenstadt, Christian [Show all 11 contributors for this article] (2016). Ontology-based visual querying with OptiqueVQS: Statoil and Siemens cases. CEUR Workshop Proceedings. ISSN 1613-0073. 1818, p. 34–39.
• Kharlamov, Evgeny; Mailis, Theofilis P.; Bereta, Konstantina; Bilidas, Dimitris; Brandt, Sebastian & Jimenez-Ruiz, Ernesto [Show all 20 contributors for this article] (2016). A Semantic Approach to Polystores. In Joshi, James (Eds.), Proceedings 2016 IEEE International Conference on Big Data. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-4673-9005-7. p. 2565–2573. doi: 10.1109/BigData.2016.7840898.
• Horrocks, Ian; Giese, Martin; Kharlamov, Evgeny & Waaler, Arild (2016). Using Semantic Technology to Tame the Data Variety Challenge. IEEE Internet Computing. ISSN 1089-7801. 20(6), p. 62–66. doi: 10.1109/MIC.2016.121.
• Kharlamov, Evgeny; Brandt, Sebastian; Giese, Martin; Jimenez-Ruiz, Ernesto; Kotidis, Yannis & Lamparter, Steffen [Show all 17 contributors for this article] (2016). Enabling semantic access to static and streaming distributed data with optique: demo. In Gal, Avigdor; Weidlich, Matthias; Kalogeraki, Vana & Venkasubramanian, Nalini (Ed.), Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems, DEBS '16, Irvine, CA,USA, June 20 -24. ACM Publications. ISSN 978-1-4503-4021-2. p. 350–353. doi: 10.1145/2933267.2933290.
• Zheleznyakov, Dmitriy; Kharlamov, Evgeny; Klungre, Vidar Norstein; Skjæveland, Martin G; Hovland, Dag & Giese, Martin [Show all 8 contributors for this article] (2016). KeywDB: A system for keyword-driven ontology-to-RDB mapping construction. CEUR Workshop Proceedings. ISSN 1613-0073. 1690.

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• Baader, Franz; Horrocks, Ian; Lutz, Carsten & Sattler, Ulrike (2017). An introduction to description logic. Cambridge University Press. ISBN 978-0-521-87361-1. 252 p.

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• He, Yuan; Chen, Jiaoyan; Jimenez-Ruiz, Ernesto; Dong, Hang & Horrocks, Ian (2023). Language Model Analysis for Ontology Subsumption Inference. arXiv.org. ISSN 2331-8422. doi: 10.48550/arXiv.2302.06761.
• Horrocks, Ian; Olivares, Jordi; Cocchi, Valerio; Motik, Boris & Roy, Dylan (2022). The Dow Jones Knowledge Graph. Show summary

  Dow Jones is a leading provider of market, industry and portfolio intelligence serving a wide range of financial applications including asset management, trading, analysis and bankruptcy/restructuring. The information needed to provide such intelligence comes from a variety of heterogeneous data sources. Integrating this information and answering complex queries over it presents both conceptual and computational challenges. In order to address these challenges Dow Jones have used the RDFox system to integrate the various sources in a large RDF knowledge graph. The knowledge graph is being used to power an expanding range of internal processes and market intelligence products.

• Horrocks, Ian; He, Yuan; Chen, Jiaoyan; Jimenez-Ruiz, Ernesto & Hadian, Ali (2022). Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching. Show summary

  Ontology Matching (OM) plays an important role in many domains such as bioinformatics and the Semantic Web, and its research is becoming increasingly popular, especially with the application of machine learning (ML) techniques. Although the Ontology Alignment Evaluation Initiative (OAEI) represents an impressive effort for the systematic evaluation of OM systems, it still suffers from several limitations including limited evaluation of subsumption mappings, suboptimal reference mappings, and limited support for the evaluation of ML-based systems. To tackle these limitations, we introduce five new biomedical OM tasks involving ontologies extracted from Mondo and UMLS. Each task includes both equivalence and subsumption matching; the quality of reference mappings is ensured by human curation, ontology pruning, etc.; and a comprehensive evaluation framework is proposed to measure OM performance from various perspectives for both ML-based and non-ML-based OM systems. We report evaluation results for OM systems of different types to demonstrate the usage of these resources, all of which are publicly available as part of the new BIO-ML track at OAEI 2022.

• Horrocks, Ian; He, Yuan; Chen, Jiaoyan & Antonyrajah, Denvar (2022). BERTMap: A BERT-Based Ontology Alignment System. Show summary

  Ontology alignment (a.k.a ontology matching (OM)) plays a critical role in knowledge integration. Owing to the success of machine learning in many domains, it has been applied in OM. However, the existing methods, which often adopt ad-hoc feature engineering or non-contextual word embeddings, have not yet outperformed rule-based systems especially in an unsupervised setting. In this paper, we propose a novel OM system named BERTMap which can support both unsupervised and semi-supervised settings. It first predicts mappings using a classifier based on fine-tuning the contextual embedding model BERT on text semantics corpora extracted from ontologies, and then refines the mappings through extension and repair by utilizing the ontology structure and logic. Our evaluation with three alignment tasks on biomedical ontologies demonstrates that BERTMap can often perform better than the leading OM systems LogMap and AML.

• Ronca, Alessandro; Kaminski, Mark; Grau, Bernardo Cuenca & Horrocks, Ian (2018). The Window Validity Problem in Rule-Based Stream Reasoning.
• Chen, Jiaoyan; Lécué, Freddy; Pan, Jeff Z.; Horrocks, Ian & Chen, Huajun (2018). Knowledge-Based Transfer Learning Explanation.
• Savkovic, Ognen; Kharlamov, Evgeny; Ringsquandl, Martin; Xiao, Guihui; Mehdi, Gulnar & Kalayci, Elem Güzel [Show all 8 contributors for this article] (2018). Semantic Diagnostics of Smart Factories.
• Ronca, Alessandro; Kaminski, Mark; Grau, Bernardo Cuenca; Motik, Boris & Horrocks, Ian (2018). Stream Reasoning in Temporal Datalog.
• Hu, Pan; Motik, Boris & Horrocks, Ian (2018). Optimised Maintenance of Datalog Materialisations.
• Soylu, Ahmet; Giese, Martin; Jimenez-Ruiz, Ernesto; Kharlamov, Evgeny; Schlatte, Rudolf & Neuenstadt, Christian [Show all 11 contributors for this article] (2016). Ontology-based Visual Querying with OptiqueVQS: Statoil and Siemens Cases. CEUR Workshop Proceedings. ISSN 1613-0073. 1818.
• Chen, Jiaoyan; He, Yuan; Geng, Yuxia; Jimenez-Ruiz, Ernesto; Dong, Hang & Horrocks, Ian (2022). Contextual Semantic Embeddings for Ontology Subsumption Prediction. arXivLabs.
• He, Yuan; Chen, Jiaoyan; Dong, Hang; Jimenez-Ruiz, Ernesto; Hadian, Ali & Horrocks, Ian (2022). Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching. arXivLabs.
• Horrocks, Ian; Grau, Bernardo Cuenca & Harzli, Ouns El (2022). The Minimal Feature Removal Problem in Neural Networks. arXivLabs.

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