Property graphs are widely used to model complex, interconnected data, but flexible property annotations often undermine consistency due to weak schema support. We introduce PG-Schema-PC, an extension of PG-Schema for constraints over property sets, covering structural, cardinality, and range constraints (including alternative groupings, multiplicity control, and value conditions). We formalize its abstract grammar and illustrate its use with practical examples.
The rapid emergence of novel psychoactive substances and evolving slang presents ongoing challenges for effective drug surveillance and public health intervention. Traditional keyword-based methods often fail to capture informal, misspelled, or newly coined terminology prevalent in online communities. In this study, we explore a data-driven approach to slang detection by analyzing Reddit posts from drug-related subreddits. Beginning with a curated list of known drug names, we collect Reddit data using PRAW, a Python-based Reddit API wrapper, and focus on high-engagement posts from sections like "hot," "new," and "top." After preprocessing the text via SpaCy for lemmatization and normalization, we identify frequent out-of-vocabulary terms not present in the seed list. To assess their potential as drug slang, we evaluate the semantic similarity between these candidate terms and known drugs using two NLP models: Word2Vec and BERT-based transformers. While Word2Vec captures surface-level morphological similarities, BERT models leverage deeper contextual understanding for phrase-level comparisons. We compare the outputs of both methods by analyzing their overlap with known drug variants and inspecting topscoring matches. Our results indicate that BERT models generally identify more semantically valid yet lexically distinct slang terms than Word2Vec. This comparative analysis highlights the relative strengths of each embedding approach and suggests transformer-based models are more effective for scalable and adaptive slang discovery from social media.
We investigate the use of Web agents that use planning to achieve goals involving preconditions on the dynamic Web. We implement our approach, AEPIS, based on Semantic Web technologies and rely on environmental information evaluated exclusively at run-time to take dynamic changes into account. We evaluate AEPIS in static and dynamic environments, based on the Web of Things, and compare its performance to a rule-based and an agent using large language models. We find that AEPIS combines adaptivity with rapid planning at scale, even in a dynamic environment.
Extracting structured knowledge from text in the form of (subject, predicate, object) triples is a key task for many artificial intelligence applications, in particular for knowledge graphs (KGs) construction. In closed information extraction (cIE) where the extracted triples are constrained by a predefined KG schema, most existing approaches rely on Wikidata for entities and relation extraction. As a result, they often lack the flexibility to adapt to other KGs without prior retraining and costly data annotation. In this paper, to address these limitations, we propose FlexCIE, an approach for cIE which leverages Large Language Models (LLMs) and a KG completeness analysis tool. Given an input text, it identifies a list of entity mentions, links them to entities in the target KG using embedding techniques combined with LLMs, and constructs triples from these extracted entities using relevant properties retrieved from the KG. This enables the use of LLMs for cIE, while ensuring that the generated triples are accurate and compliant with the KG schema. Therefore, our work contributes to making cIE more practical and flexible, particularly for domain specific or enterprise KGs.
Cultural heritage data, particularly from colonial contexts, frequently presents an incomplete and biased view, reflecting historical institutional priorities more than contemporary knowledge requirements. Consequently, knowledge graphs derived from these records often contain incomplete, fragmented, and skewed data, including absent attributes or values, missing semantic links, and under-represented perspectives. This work addresses the challenge of knowledge discovery under such limitations, presenting a real-world case study on the provenance research of colonial cultural heritage. We present a task-aware design method for building a tool to facilitate this process. The design approach of this application is rooted in a Knowledge Discovery in Database (KDD) framework and is particularly novel due to its formalisation and operationalisation of three distinct types of semantic association: explicit, abstract, and implicit. These semantic associations, grounded in domain interpretation, are crucial for bridging data gaps where user information needs cannot be directly met by existing data. We further demonstrate how these associations can be effectively communicated through user interface components, enabling users to infer new knowledge. We evaluated the resultant application through a user study among domain experts to assess its efficacy. The evaluation confirms the effectiveness of the tool in enabling new knowledge discovery and reveals opportunities to improve the representation of the underlying data, as users could successfully infer insights even when information was missing or poorly captured in the original data sets.
