RuleHub

Open Government Data (OGD) is being published by various public administration organizations around the globe. Within the metadata of OGD data catalogs, the publishing organizations (1) are not uniquely and unambiguously identifiable and, even worse, (2) change over time, by public administration units being merged or restructured. In order to enable fine-grained analyzes or searches on Open Government Data on the level of publishing organizations, linking those from OGD portals to publicly available knowledge graphs (KGs) such as Wikidata and DBpedia seems like an obvious solution. Still, as we show in this position paper, organization linking faces significant challenges, both in terms of available (portal) metadata and KGs in terms of data quality and completeness. We herein specifically highlight five main challenges, namely regarding (1) temporal changes in organizations and in the portal metadata, (2) lack of a base ontology for describing organizational structures and changes in public knowledge graphs, (3) metadata and KG data quality, (4) multilinguality, and (5) disambiguating public sector organizations. Based on available OGD portal metadata from the Open Data Portal Watch, we provide an in-depth analysis of these issues, make suggestions for concrete starting points on how to tackle them along with a call to the community to jointly work on these open challenges.KeywordsOpen dataDataset evolutionEntity linkingKnowledge graphsKnowledge graph evolution

Last years witnessed a shift from the potential utility in digitisation to a crucial need to enjoy activities virtually. In fact, before 2019, data curators recognised the utility of performing data digitisation, while during the lockdown caused by the COVID-19, investing in virtual and remote activities to make culture survive became crucial as no one could enjoy Cultural Heritage in person. The Cultural Heritage community heavily invested in digitisation campaigns, mainly modelling data as Knowledge Graphs by becoming one of the most successful Semantic Web technologies application domains. Despite the vast investment in Cultural Heritage Knowledge Graphs, the syntactic complexity of RDF query languages, e.g., SPARQL, negatively affects and threatens data exploitation, risking leaving this enormous potential untapped. Thus, we aim to support the Cultural Heritage community (and everyone interested in Cultural Heritage) in querying Knowledge Graphs without requiring technical competencies in Semantic Web technologies. We propose an engaging exploitation tool accessible to all without losing sight of developers’ technological challenges. Engagement is achieved by letting the Cultural Heritage community leave the passive position of the visitor and actively create their Virtual Assistant extensions to exploit proprietary or public Knowledge Graphs in question-answering. By accessible to all, we mean that the proposed software framework is freely available on GitHub and Zenodo with an open-source license. We do not lose sight of developers’ technical challenges, which are carefully considered in the design and evaluation phases. This article first analyses the effort invested in publishing Cultural Heritage Knowledge Graphs to quantify data developers can rely on in designing and implementing data exploitation tools in this domain. Moreover, we point out challenges developers may face in exploiting them in automatic approaches. Second, it presents a domain-agnostic Knowledge Graph exploitation approach based on virtual assistants as they naturally enable question-answering features where users formulate questions in natural language directly by their smartphones. Then, we discuss the design and implementation of this approach within an automatic community-shared software framework (a.k.a. generator) of virtual assistant extensions and its evaluation in terms of performance and perceived utility according to end-users. Finally, according to a taxonomy of the Cultural Heritage field, we present a use case for each category to show the applicability of the proposed approach in the Cultural Heritage domain. In overviewing our analysis and the proposed approach, we point out challenges that a developer may face in designing virtual assistant extensions to query Knowledge Graphs, and we show the effect of these challenges in practice.

Knowledge graphs encode semantic knowledge that can be exploited to enhance different data-driven tasks, e.g., query answering, data mining, ranking or recommendation. However, knowledge graphs may be incomplete, and relevant relations may be not included in the graph, affecting accuracy of these data-driven tasks. We tackle the problem of relation discovery in a knowledge graph, and devise $$\mathcal {KOI}$$KOI, a semantic based approach able to discover relations in portions of knowledge graphs that comprise similar entities. $$\mathcal {KOI}$$KOI exploits both datatype and object properties to compute the similarity among entities, i.e., two entities are similar if their datatype and object properties have similar values. $$\mathcal {KOI}$$KOI implements graph partitioning techniques that exploit similarity values to discover relations from knowledge graph partitions. We conduct an experimental study on a knowledge graph of TED talks with state-of-the-art similarity measures and graph partitioning techniques. Our observed results suggest that $$\mathcal {KOI}$$KOI is able to discover missing edges between related TED talks that cannot be discovered by state-of-the-art approaches. These results reveal that combining semantics encoded both in the similarity measures and in the knowledge graph structure, has a positive impact on the relation discovery problem.

Conducting complex logical queries over knowledge graphs remains a significant challenge. Recent research has successfully leveraged Pre-trained Language Models (PLMs) to tackle Knowledge Graph Complex Query Answering (KGCQA) tasks, which is attributed to PLMs' ability to comprehend logical semantics of queries through context learning. However, existing PLM-based KGCQA methods usually overlook the harm of disordered syntax or fragmented contexts within a serialized query, posing the problem of “impossible language” to limit PLMs in grasping the logical semantics. To address this problem, we propose a Progressive Prefix-Memory Tuning (PPMT) framework for KGCQA tasks, which effectively rectifies erroneous segments in serialized queries to assist PLMs in query answering. First, we propose a prefix-memory rectification mechanism embedded in a PLM module. This mechanism assigns rectification parameters in memory stores to polish the language segments of entities, relations, and queries through specific prefixes. To further capture the logical semantics in queries, we design a progressive fine-tuning strategy, which optimizes our model through a conditional gradient update process guided by knowledge translation constraints. Extensive experiments on widely used KGCQA benchmarks demonstrate the significant superiority of PPMT in terms of HR@3 and MRR. Our codes are available at https://github.com/lazyloafer/PPMT.

Large Language Models (LLMs) face significant challenges when attempting to utilize knowledge not encoded during pre-training. In-context learning (ICL) addresses this limitation by embedding relevant information directly into prompts, enabling LLMs to leverage external knowledge without updating their parameters. Recent advances have explored integrating knowledge graph (KG) information into prompts, taking advantage of the structured and factual representations of entities and relationships that KGs provide. A common approach involves identifying key concepts in a task, grounding them to KG nodes, extracting ego-subgraphs centered on these concepts, and incorporating them into prompts. However, not all subgraphs are equally relevant to the task, and selecting appropriate knowledge remains a critical challenge, as irrelevant or noisy subgraphs can reduce model accuracy. This study investigates how KG-based information related to task-specific concepts influences LLM performance on multiple-choice question answering (MCQA) tasks. We implement a four-stage pipeline that (1) identifies key concepts from questions and grounds them in the KG, (2) extracts individual ego-subgraphs for each concept, (3) integrates these subgraphs into prompts, and (4) evaluates their impact on LLM reasoning via probability scoring. Our analysis highlights which concept-specific subgraphs enhance performance, which introduce misleading information, and which have neutral effects. We show that carefully selected KG subgraphs can substantially outperform others in semantic relevance. Furthermore, we examine how the size and connectivity of selected KG subgraphs influence model performance. Overall, this work deepens understanding of KG-based knowledge selection in ICL and informs the design of more effective, targeted prompting strategies. This project's source code is publicly available at https://github.com/maryam-ghanbari/InformativenessOfKGs.

Besides the challenge that a human can ask one question in many different ways, a key aspect in Question Answering approaches over Knowledge Graphs (KGQA) is to deal with the vast amount of information present in the knowledge graphs. Modern real-world knowledge graphs contain nearly millions of entities and relationships. Additionally, they are enriched with new facts every day. However, not all facts are relevant for answering particular questions, thus fostering several challenges to KGQA systems, which require interpretable and query-able data. One solution to filtering the extra data in knowledge graphs is to rely on graph summarization techniques. Graph-based summarization approaches aim to resize knowledge graphs to be more concise and precise by storing only relevant information. In this paper, we propose a framework named LAUREN that applies different summarization techniques on knowledge graphs to be used in KGQA systems. Our experiments show that LAUREN summarizes large knowledge graphs such as DBpedia by 2 million entities and its summarization still achieves the same performance on both question answering and linking tasks compared to the complete DBpedia.

It is challenging to generate high quality answers for healthcare queries in online platforms. Recent studies proposed deep models for healthcare question answering (HQA) tasks. However, these models have not been thoroughly compared, and they were only tested on self-created datasets. This paper demonstrates a novel system, denoted by HQADeepHelper, to facilitate the learning and practicing of deep models for HQA. We have implemented a wide spectrum of state-of-the-art deep models for HQA retrieval. Users can upload self-collected HQA datasets and knowledge graphs, and do simple configurations by selecting datasets, knowledge graphs, neural network models, and evaluation metrics. Based on user’s configuration specified, the system can automatically train and test the model, conduct extensive experimental evaluation of the models selected, and report comprehensive findings. The reports provide new insights about the strengths and weaknesses of deep models that can guide practitioners to select appropriate models for various scenarios. Moreover, users can download the datasets, knowledge graphs, experimental reports and source codes of neural network models for their own practice and evaluations further.

Entity set expansion with semantic features of knowledge graphs

Multi-source temporal knowledge graph embedding for edge computing enabled internet of vehicles

More and more knowledge graphs are becoming available on the Web, many of them accessible via queryable interfaces, such as SPARQL endpoints. These knowledge graphs are often linked to each other and in doing so form the so-called Web of Data. The great strength and potential of exploiting these links is that we can obtain answers for queries that would not yield any results over the knowledge graphs if considered individually. However, one of the fundamental problems is that Web-accessible knowledge graphs are not always available when we would like to access them. This means that the exact same query issued at different points in time might have different results or, in the worst case, no results if one of the knowledge graphs that are essential to answer the query is (temporarily) not available. Often this is caused by crashes of the server hosting the knowledge graph - a problem that is deeply rooted within the architecture of the current Web of Data, i.e., relying on data providers to keep interfaces and knowledge graphs available and accessible. Building upon the basics of federated query processing over knowledge graphs, this talk will discuss challenges as well as strategies on how to overcome the unavailability problem and keep the data available and queryable.

Knowledge Graphs (KGs) provide a widely used format for representing entities and their relationships and have found use in diverse applications including question answering and recommendation. A majority of current research on KG inference has focused on reasoning with atomic facts (triples) and has disregarded the possibility of making complex evidential observations involving logical operators (negation, conjunction, disjunction) and quantifiers (existential, universal). Further, while the application of complex evidence has been explored in KG-based query answering (KGQA) research, in many practical online settings, observations are made sequentially. For example, in KGQA, additional context may be incrementally suggested to narrow down the answer. Or in interactive recommendation, user critiques may be expressed sequentially in order to narrow down a set of preferred items. Both settings are indicative of information filtering or tracking tasks that are reminiscent of belief tracking in Bayesian inference. In fact, in this paper, we precisely cast the problem of belief tracking over unknown KG entities given incremental complex KG evidence as a Bayesian filtering problem. Specifically, we leverage Knowledge-based Model Construction (KBMC) over the logical KG evidence to instantiate a Markov Random Field (MRF) likelihood representation to perform closed-form Bayesian inference with complex KG evidence (BIKG). We experimentally evaluate BIKG in incremental KGQA and interactive recommendation tasks demonstrating that it outperforms non-incremental methodologies and leads to better incorporation of conjunctive evidence vs. existing complex KGQA methods like CQD that leverage fuzzy T-norm operators. Overall, this work demonstrates a novel, efficient, and unified perspective of logic, KGs, and online inference through the lens of closed-form BIKG.

A review of graph neural networks and pretrained language models for knowledge graph reasoning

Learning structured embeddings of knowledge graphs with generative adversarial framework

Performing complex First-Order Logic (FOL) queries on knowledge graphs is crucial for advancing knowledge reasoning. Knowledge graphs encapsulate rich semantic interactions among entities, encompassing both explicit structural knowledge represented by triples <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(e_{1}, r, e_{2})$</tex-math></inline-formula> and implicit relational knowledge through multi-hop paths <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(e_{1} \stackrel{r_{1}}{\rightarrow } \cdots e_{3} \cdots \stackrel{r_{2}}{\rightarrow } e_{2})$</tex-math></inline-formula>. Traditional models often focus solely on either triple-level or path-level knowledge, overlooking the benefits of integrating both to enhance logic query answering. This oversight leads to suboptimal representation learning and inefficient query reasoning. To overcome these challenges, we introduce a new <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b>emantic-<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b>ware representation learning model for <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</b>uery-answering <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b>mbeddings (<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SAQE</b>). Specifically, SAQE employs a joint learning approach that integrates triple-level and path-level knowledge semantics and captures both explicit and implicit contextual nuances within the knowledge graph, yielding more accurate and contextually relevant representations. To efficiently handle the large combinatorial search spaces in FOL reasoning, we propose a novel hierarchical reasoning optimization strategy by a multi-hop tree thus optimizing subqueries rooted at variable nodes in a divide-and-conquer manner. Theoretical analysis confirms that SAQE effectively supports various types of FOL reasoning and enhances generalizations for query answering. Extensive experiments demonstrate that our model achieves state-of-the-art performance across several established datasets.

Connecting the physical world to the Internet of Things (IoT) allows for the development of a wide variety of applications. Things can be searched, managed, analyzed, and even included in collaborative games. Industries, health care, and cities are exploiting IoT data-driven frameworks to make these organizations more efficient, thus, improving the lives of citizens. For making IoT a reality, data produced by sensors, smart phones, watches, and other wearables need to be integrated; moreover, the meaning of IoT data should be explicitly represented. However, the Big Data nature of IoT data imposes challenges that need to be addressed in order to provide scalable and efficient IoT data-driven infrastructures. We tackle these issues and focus on the problems of describing the meaning of IoT streaming data using ontologies and integrating this data in a knowledge graph. We devise DESERT, a SPARQL query engine able to on-Demand factorizE and Semantically Enrich stReam daTa in a knowledge graph. Resulting knowledge graphs model the semantics or meaning of merged data in terms of entities that satisfy the SPARQL queries and relationships among those entities; thus, only data required for query answering is included in the knowledge graph. We empirically evaluate the results of DESERT on SRBench, a benchmark of Streaming RDF data. The experimental results suggest that DESERT allows for speeding up query execution while the size of the knowledge graphs remains relatively low.