Pretrained language models (PLMs) for data-to-text (D2T) generation can use human-readable data labels such as column headings, keys, or relation names to generalize to out-of-domain examples. However, the models are well-known in producing semantically inaccurate outputs if these labels are ambiguous or incomplete, which is often the case in D2T datasets. In this paper, we expose this issue on the task of descibing a relation between two entities. For our experiments, we collect a novel dataset for verbalizing a diverse set of 1,522 unique relations from three large-scale knowledge graphs (Wikidata, DBPedia, YAGO). We find that although PLMs for D2T generation expectedly fail on unclear cases, models trained with a large variety of relation labels are surprisingly robust in verbalizing novel, unseen relations. We argue that using data with a diverse set of clear and meaningful labels is key to training D2T generation systems capable of generalizing to novel domains.

pdf/mind-the-labels-describing-relations-in-knowledge-graphs-2anhaiiu.pdf

Mind the Labels: Describing Relations in Knowledge Graphs With Pretrained Models

Due to the under-specified interfaces, developers face challenges in correctly integrating machine learning (ML) APIs in software. Even when the ML API and the software are well designed on their own, the resulting application misbehaves when the API output is incompatible with the software. It is desirable to have an adapter that converts ML API output at runtime to better fit the software need and prevent integration failures. In this paper, we conduct an empirical study to understand ML API integration problems in real-world applications. Guided by this study, we present SmartGear, a tool that automatically detects and converts mismatching or incorrect ML API output at run time, serving as a middle layer between ML API and software. Our evaluation on a variety of open-source applications shows that SmartGear detects 70% incompatible API outputs and prevents 67% potential integration failures, outperforming alternative solutions.

Run-Time Prevention of Software Integration Failures of Machine Learning APIs

The distinction between types and individuals is key to most conceptual modeling techniques and knowledge representation languages. Despite that, there are a number of situations in which modelers navigate this distinction inadequately, leading to problematic models. We show evidence of a large number of representation mistakes associated with the failure to employ this distinction in the Wikidata knowledge graph, which can be identified with the incorrect use of instantiation, which is a relation between an instance and a type, and specialization (or subtyping), which is a relation between two types. The prevalence of the problems in Wikidata’s taxonomies suggests that methodological and computational tools are required to mitigate the issues identified, which occur in many settings when individuals, types, and their metatypes are included in the domain of interest. We conduct a conceptual analysis of entities involved in recurrent erroneous cases identified in this empirical data, and present a tool that supports users in identifying some of these mistakes.

Evidence of large-scale conceptual disarray in multi-level taxonomies in Wikidata

What will Semantic Web research focus on in 20 years from now? We asked this question to the community and collected their visions in the "Next 20 years" track of ESWC 2023. We challenged the participants to submit "future" research papers, as if they were submitting to the 2043 edition of the conference. The submissions - entirely fictitious - were expected to be full scientific papers, with research questions, state of the art references, experimental results and future work, with the goal to get an idea of the research agenda for the late 2040s and early 2050s. We received ten submissions, eight of which were accepted for presentation at the conference, that mixed serious ideas of potential future research themes and discussion topics with some fun and irony. In this paper, we intend to provide a survey of those "science fiction" papers, considering the emerging research themes and topics, analysing the research methods applied by the authors in these very special submissions, and investigating also the most fictitious parts (e.g., neologisms, fabricated references). Our goal is twofold: on the one hand, we investigate what this special track tells us about the Semantic Web community and, on the other hand, we aim at getting some insights on future research practices and directions. 

The Time Traveler's Guide to Semantic Web Research: Analyzing Fictitious Research Themes in the ESWC "Next 20 Years" Track

Constraints play an important role to ensure data integrity. While the Shapes Constraint Language (SHACL) provides a W3C recommendation for validating RDF Knowledge Graphs (KG) against such constraints, real-world KG have adopted their own constraint formalisms. Wikidata (WD), one of the largest collaboratively Open Data Knowledge Graphs available on the Web, represents property constraints through its own RDF data model, within its own authoritative namespaces, which might be an indication that the nature of WD property constraints is different from other Knowledge Graphs. In this paper we investigate the semantics of WD constraints, and unambiguously formalize all current constraints using SPARQL to retrieve violations; we also discuss the expressiveness of WD constraint language compared with SHACL core and discuss the evolution of constraint violations. We found that, while all current WD property constraint types can be expressed using SPARQL, only 86% (26 out of 30) can be expressed using SHACL core: the rest face issues related to using separator properties and arithmetic expressions.

Formalizing Property Constraints in Wikidata

An Analysis of Links in Wikidata

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From Semantic Web to Wisdom Web: A Retrospective on the Journey to 2043

Wikibase, the open-source software behind Wikidata, increasingly gains popularity among third-party Linked Data publishers. However, the platform’s unique data model decreases the degree of interoperability with existing Semantic Web standards and tools that underlie Linked Data as codified by Linked Data principles. In particular, this unique data model of Wikibase also undermines the direct reuse of ontologies and vocabularies, in a manner compliant with Semantic Web standards and Linked Data principles. To this end, firstly, we compare the Wikibase data model to the established RDF data model. Secondly, we enumerate a series of challenges for importing existing ontologies into Wikibase. Thirdly, we present practical solutions to these challenges and introduce a tool for importing and re-using ontologies within Wikibase. Thus, the paper aims to promote ontology interoperability in Wikibase and by doing so hopes to contribute to higher degree of inter-linkage of Wikibase instances with Linked Open Data.

Analysing and promoting ontology interoperability in Wikibase

Several initiatives have been undertaken to conceptually model the domain of scholarly data using ontologies and to create respective Knowledge Graphs. Yet, the full potential seems unleashed, as automated means for automatic population of said ontologies are lacking, and respective initiatives from the Semantic Web community are not necessarily connected: we propose to make scholarly data more sustainably accessible by leveraging Wikidata's infrastructure and automating its population in a sustainable manner through LLMs by tapping into unstructured sources like conference Web sites and proceedings texts as well as already existing structured conference datasets. While an initial analysis shows that Semantic Web conferences are only minimally represented in Wikidata, we argue that our methodology can help to populate, evolve and maintain scholarly data as a community within Wikidata. Our main contributions include (a) an analysis of ontologies for representing scholarly data to identify gaps and relevant entities/properties in Wikidata, (b) semi-automated extraction -- requiring (minimal) manual validation -- of conference metadata (e.g., acceptance rates, organizer roles, programme committee members, best paper awards, keynotes, and sponsors) from websites and proceedings texts using LLMs. Finally, we discuss (c) extensions to visualization tools in the Wikidata context for data exploration of the generated scholarly data. Our study focuses on data from 105 Semantic Web-related conferences and extends/adds more than 6000 entities in Wikidata. It is important to note that the method can be more generally applicable beyond Semantic Web-related conferences for enhancing Wikidata's utility as a comprehensive scholarly resource. Source Repository: https://github.com/scholarly-wikidata/ DOI: https://doi.org/10.5281/zenodo.10989709 License: Creative Commons CC0 (Data), MIT (Code)

Scholarly Wikidata: Population and Exploration of Conference Data in Wikidata Using LLMs

How Does Knowledge Evolve in Open Knowledge Graphs?

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