Basil Ell

• Blum, Moritz; Ell, Basil & Cimiano, Philipp (2022). Exploring the impact of literal transformations within Knowledge Graphs for Link Prediction, 978-1-7281-6117-4. IEEE conference proceedings. ISSN 978-1-7281-6117-4.
• Lin, Tengteng; Chen, Qiaosheng; Cheng, Gong; Soylu, Ahmet; Ell, Basil & Zhao, Ruoqi [Show all 10 contributors for this article] (2022). ACORDAR: A Test Collection for Ad Hoc Content-Based (RDF) Dataset Retrieval. In Amigó, Enrique; Castells, Pablo; Gonzalo, Julio; Carterette, Ben; Culpepper, J. Shane & Kazai, Gabriella (Ed.), SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 - 15, 2022. Association for Computing Machinery (ACM). ISSN 978-1-4503-8732-3. p. 2981–2991. doi: 10.1145/3477495.3531729. Show summary

  Ad hoc dataset retrieval is a trending topic in IR research. Methods and systems are evolving from metadata-based to content-based ones which exploit the data itself for improving retrieval accuracy but thus far lack a specialized test collection. In this paper, we build and release the first test collection for ad hoc content-based dataset retrieval, where content-oriented dataset queries and content-based relevance judgments are annotated by human experts who are assisted with a dashboard designed specifically for comprehensively and conveniently browsing both the metadata and data of a dataset. We conduct extensive experiments on the test collection to analyze its difficulty and provide insights into the underlying task.

• Sanchez-Graillet, Olivia; Witte, Christian; Grimm, Frank; Grautoff, Steffen; Ell, Basil & Cimiano, Philipp (2022). Synthesizing evidence from clinical trials with dynamic interactive argument trees. Journal of Biomedical Semantics. ISSN 2041-1480. 13(1). doi: 10.1186/s13326-022-00270-8.
• Ell, Basil; Elahi, Mohammad Fazleh & Cimiano, Philipp (2021). Bridging the Gap Between Ontology and Lexicon via Class-Specific Association Rules Mined from a Loosely-Parallel Text-Data Corpus. In Gromann, Dagmar; Gilles, Sérasset; Declerck, Thierry; McCrae, John P.; Gracia, Jorge; Bosque-Gil, Julia; Bobillo, Fernando & Heinisch, Barbara (Ed.), 3rd Conference on Language, Data and Knowledge (LDK 2021). Schloss Dagstuhl-Leibniz-Zentrum für Informatik.. ISSN 978-3-95977-199-3. p. 33:1–33:21. doi: 10.4230/OASIcs.LDK.2021.33. Show summary

  There is a well-known lexical gap between content expressed in the form of natural language (NL) texts and content stored in an RDF knowledge base (KB). For tasks such as Information Extraction (IE), this gap needs to be bridged from NL to KB, so that facts extracted from text can be represented in RDF and can then be added to an RDF KB. For tasks such as Natural Language Generation, this gap needs to be bridged from KB to NL, so that facts stored in an RDF KB can be verbalized and read by humans. In this paper we propose LexExMachina, a new methodology that induces correspondences between lexical elements and KB elements by mining class-specific association rules. As an example of such an association rule, consider the rule that predicts that if the text about a person contains the token "Greek", then this person has the relation nationality to the entity Greece. Another rule predicts that if the text about a settlement contains the token "Greek", then this settlement has the relation country to the entity Greece. Such a rule can help in question answering, as it maps an adjective to the relevant KB terms, and it can help in information extraction from text. We propose and empirically investigate a set of 20 types of class-specific association rules together with different interestingness measures to rank them. We apply our method on a loosely-parallel text-data corpus that consists of data from DBpedia and texts from Wikipedia, and evaluate and provide empirical evidence for the utility of the rules for Question Answering.

• Holter, Ole Magnus & Ell, Basil (2021). Towards Scope Detection in Textual Requirements. In Gromann, Dagmar; Gilles, Sérasset; Declerck, Thierry; McCrae, John P.; Gracia, Jorge; Bosque-Gil, Julia; Bobillo, Fernando & Heinisch, Barbara (Ed.), 3rd Conference on Language, Data and Knowledge (LDK 2021). Schloss Dagstuhl-Leibniz-Zentrum für Informatik.. ISSN 978-3-95977-199-3.

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• Elahi, Mohammad Fazleh; Ell, Basil & Cimiano, Philipp (2023). LexExMachinaQA: A framework for the automatic induction ofontology lexica for Question Answering over Linked Data. Show summary

  An open issue for Semantic Question Answering Systems is bridging the so called lexical gap, referring to the fact that the vocabulary used by users in framing a question needs to be interpreted with respect to the logical vocabulary used in the data model of a given knowledge base or knowledge graph. Building on previous work to automatically induce ontology lexica from language corpora by using association rules to identify correspondences between lexical elements on the one hand and ontological vocabulary elements on the other, in this paper we propose LexExMachinaQA, a framework allowing us to evaluate the impact of automatically induced lexicalizations in terms of alleviating the lexical gap in QA systems. Our framework combines the LexExMachina approach for lexicon induction with the QueGG system proposed by Benz et al. that relies on grammars automatically generated from ontology lexica to parse questions into SPARQL. We show that automatically induced lexica yield a decent performance i.t.o. F1 measure with respect to the QLAD-7 dataset, representing a 34% - 56% performance degradation with respect to a manually created lexicon. While these results show that the fully automatic creation of lexica for QA systems is not yet feasible, the method could certainly be used to bootstrap the creation of a lexicon in a semi-automatic manner, thus having the potential to significantly reduce the human effort involved.

• Blum, Moritz; Ell, Basil & Cimiano, Philipp (2023). Insights from an OTTR-centric Ontology Engineering Methodology. Show summary

  OTTR is a language for representing ontology modeling patterns, which enables to build ontologies or knowledge bases by instantiating templates. Thereby, particularities of the ontological representation language are hidden from the domain experts, and it enables ontology engineers to, to some extent, separate the processes of deciding about what information to model from deciding about how to model the information, e. g., which design patterns to use. Certain decisions can thus be postponed for the benefit of focusing on one of these processes. To date, only few works on ontology engineering where ontology templates are applied are described in the literature. In this paper, we outline our methodology and report findings from our ontology engineering activities in the domain of Material Science. In these activities, OTTR templates play a key role. Our ontology engineering process is bottom-up, as we begin modeling activities from existing data that is then, via templates, fed into a knowledge graph, and it is top-down, as we first focus on which data to model and postpone the decision of how to model the data. We find, among other things, that OTTR templates are especially useful as a means of communication with domain experts. Furthermore, we find that because OTTR templates encapsulate modeling decisions, the engineering process becomes flexible, meaning that design decisions can be changed at little cost.

• Holter, Ole Magnus & Ell, Basil (2023). Reading Between the Lines: Information Extraction from Industry Requirements. Show summary

  Industry requirements describe the qualities that a project or a service must provide. Most requirements are, however, only available in natural language format and are embedded in textual documents. To be machine-understandable, a requirement needs to be represented in a logical format. We consider that a requirement consists of a scope, which is the requirement's subject matter, a condition, which is any condition that must be fulfilled for the requirement to be relevant, and a demand, which is what is required. We introduce a novel task, the identification of the semantic components scope, condition, and demand in a requirement sentence, and establish baselines using sequence labelling and few-shot learning. One major challenge with this task is the implicit nature of the scope, often not stated in the sentence. By including document context information, we improved the average performance for scope detection. Our study provides insights into the difficulty of machine understanding of industry requirements and suggests strategies for addressing this challenge.

• Holter, Ole Magnus & Ell, Basil (2023). Human-Machine Collaborative Annotation: A Case Study with GPT-3. Show summary

  Within industry, it is vital to adequately communicate the qualities and features of what is to be built, and requirements are important artefacts for this purpose. Having machine-readable requirements can enhance the level of control over the requirements, allowing more efficient requirement management and communication. Training a semantic parser typically requires a dataset with thousands of examples. However, creating such a dataset for textual requirements poses significant challenges. In this study, we investigate to what extent a large language model can assist a human annotator in creating a gold corpus for semantic parsing of textual requirements. The language model generates a semantic parse of a textual requirement that is then corrected by a human and then added to the gold standard. Instead of incrementally fine-tuning the language model on the growing gold standard, we investigate different strategies of including examples from the growing gold standard in the prompt for the language model. We found that selecting the requirements most semantically similar to the target sentence and ordering them with the most similar requirement first yielded the best performance on all the metrics we used. The approach resulted in 41 % fewer edits compared to creating the parses from scratch, - thus, significantly less human effort is involved in the creation of the gold standard in collaborative annotation. Our findings indicate that having more requirements in the gold standard improves the accuracy of the initial parses.

• Blum, Moritz; Ell, Basil & Cimiano, Philipp (2022). Exploring the impact of literal transformations within Knowledge Graphs for Link Prediction. ACM Publications.
• Elahi, Mohammad Fazleh; Ell, Basil; Grimm, Frank & Cimiano, Philipp (2021). Question Answering on RDF Data based on Grammars Automatically Generated from Lemon Models. Technical University of Aachen. ISSN 1613-0073. 2941(1). Show summary

  Many question answering (QA) systems over RDF induced from question-query pairs using some machine learning technique suffer from a lack of controllability, making the governance and incremental improvement of the system challenging, not to mention the initial effort of collecting and providing training data. As an alternative, we present a model-based QA approach that uses an ontology lexicon in lemon format and automatically generates a lexicalized grammar used to interpret and parse questions into SPARQL queries. The approach gives maximum control over the QA system to the developer as every lexicon extension increases the coverage of the grammar, and thus of the QA system, in a predictable way. We describe our approach to generating grammars from lemon lexica and show how these grammars generate specific questions that we index to support fast QA performance in a prototype that answers questions with respect to DBpedia.

• Nolano, Gennaro; Elahi, Mohammad Fazleh; De Buono, Maria Pia; Ell, Basil & Cimiano, Philipp (2021). An Italian Question Answering System based on grammars automatically generated from ontology lexica. Technical University of Aachen. ISSN 1613-0073. Show summary

  The paper presents an Italian question answering system over linked data. We use a model-based approach to question answering based on an ontology lexicon in lemon format. The system exploits an automatically generated lexicalized grammar that can then be used to interpret and transform questions into SPARQL queries. We apply the approach for the Italian language and implement a question answering system that can answer more than 1.6 million questions over the DBpedia knowledge graph.

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