Semantic Web technologies are increasingly used in the architecture, engineering, and construction (AEC) industry, yet the Resource Description Framework (RDF) and its query language, SPARQL, still lack native support for 3D geometry. Existing approaches either reduce geometry to 2D, rely on external spatial databases, or require processing workflows outside the semantic layer. This paper introduces geof3D, an extension to SPARQL that enables 3D geometric computation directly inside RDF triple stores. The framework is grounded in a formal function space derived from Architectural Geometry and provides typed operators for measurement, spatial predicates, constructive solid modeling, and affine transformations. These functions are implemented as SPARQL built-ins in RDF4J, supported by an execution backend that uses Java-based processing together with SFCGAL, a robust computational geometry engine accessed through the Java Native Interface (JNI). The system supports operations including geometric validation, Boolean solids, 3D spatial queries, and shape transformations without leaving the RDF environment. We evaluate geof3D using real building models from the Large-Scale Construction Robotics Laboratory and show that the framework supports spatial alignment, clash detection, and algorithmic modeling entirely through RDF-native queries. The evaluation examines both expressiveness and implementation performance, combining in-browser benchmarking with direct JNI measurements and comparative testing against a PostGIS configuration to assess performance, scalability, and geometric fidelity. All code, queries, datasets, and benchmarks are openly released. This work shows that SPARQL can serve not only as a semantic query language but also as a computational interface for 3D co-design, enabling integrated, interoperable, and geometry-aware workflows for building information management.

Predicting answers to queries over knowledge graphs is called a complex reasoning task because answering a query requires subdividing it into subqueries. Existing query embedding methods use this decomposition to compute the embedding of a query as the combination of the embedding of the subqueries. This requirement limits the answerable queries to queries having a single free variable and being decomposable, which are called tree-form queries and correspond to the SROI- description logic. In this paper, we define a more general set of queries, called DAG queries and formulated in the ALCOIR description logic, propose a query embedding method for them, called DAGE, and a new benchmark to evaluate query embeddings on them. Given the computational graph of a DAG query, DAGE combines the possibly multiple paths between two nodes into a single path with a trainable operator that represents the intersection of relations and learns DAG-DL concepts from tautologies. We implement DAGE on top of existing query embedding methods, and we empirically measure the improvement of our method over the results of vanilla methods evaluated in tree-form queries that approximate the DAG queries of our proposed benchmark.

Main tasks of usability experts for Web sites comprise the analysis of user interaction behavior on graphical user interfaces, the discovery of issues, and the derivation of improvements to the interface. The analysis of user interaction behavior and corresponding discovery of issues are made difficult by modern Web interfaces that incorporate dynamic interface elements and that orchestrate complex reactions to user responses. We propose a semi-automated approach for discovering visual stimuli, which capture summarized views of the interface as encountered by users during interaction. Discovered visual stimuli allow for meaningful aggregations of user interactions based on what users encountered on the interface such that the analysis by usability experts can relate the interface views with user interactions correctly and identify arising issues. We provide WebVSD as an implementation of the approach and perform a set of evaluations with real-world Web sites that show the accuracy of proposed methods in isolation and in the tool chain, as well as case studies and a survey of usability experts indicating the usefulness of the suggested approach.

To address bias issues, fair machine learning usually jointly optimizes two (or more) metrics aiming at predictive utility and fairness. However, the inherent under-representation of minorities in the data often makes the disparate impact of subpopulations less noticeable and difficult to deal with during learning. In this paper, we propose a novel adversarial reweighting method to address such disparate impact. To balance the data distribution between the majority and the minority groups, our approach prefers samples from the majority group that are closer to the minority group as evaluated by the Wasserstein distance. Theoretical analysis shows the effectiveness of our adversarial reweighting approach. Experiments demonstrate that our approach mitigates disparate impact without sacrificing classification accuracy, outperforming related state-of-the-art methods on image and tabular benchmark datasets. Code is available at https://github.com/zhaoxuan00707/wasserstein_reweight.

SPARQL CONSTRUCT queries allow for the specification of data processing pipelines that transform given input graphs into new output graphs. It is now common to constrain graphs through SHACL shapes allowing users to understand which data they can expect and which not. However, it becomes challenging to understand what graph data can be expected at the end of a data processing pipeline without knowing the particular input data: Shape constraints on the input graph may affect the output graph, but may no longer apply literally, and new shapes may be imposed by the query template. In this paper, we study the derivation of shape constraints that hold on all possible output graphs of a given SPARQL CONSTRUCT query. We assume that the SPARQL CONSTRUCT query is fixed, e.g., being part of a program, whereas the input graphs adhere to input shape constraints but may otherwise vary over time and, thus, are mostly unknown. We study a fragment of SPARQL CONSTRUCT queries (SCCQ) and a fragment of SHACL (Simple SHACL). We formally define the problem of deriving the most restrictive set of Simple SHACL shapes that constrain the results from evaluating a SCCQ over any input graph restricted by a given set of Simple SHACL shapes. We propose and implement an algorithm that statically analyses input SHACL shapes and CONSTRUCT queries and prove its soundness and complexity.

Knowledge graphs have been conceived to collect heterogeneous data and knowledge about large domains, e.g. medical or engineering domains, and to allow versatile access to such collections by means of querying and logical reasoning. A surge of methods has responded to additional requirements in recent years. (i) Knowledge graph embeddings use similarity and analogy of structures to speculatively add to the collected data and knowledge. (ii) Queries with shapes and schema information can be typed to provide certainty about results. We survey both developments and find that the development of techniques happens in disjoint communities that mostly do not understand each other, thus limiting the proper and most versatile use of knowledge graphs.

Medical decision-making processes can be enhanced by comprehensive biomedical knowledge bases, which require fusing knowledge graphs constructed from different sources via a uniform index system. The index system often organizes biomedical terms in a hierarchy to provide the aligned entities with fine-grained granularity. To address the challenge of scarce supervision in the biomedical knowledge fusion (BKF) task, researchers have proposed various unsupervised methods. However, these methods heavily rely on ad-hoc lexical and structural matching algorithms, which fail to capture the rich semantics conveyed by biomedical entities and terms. Recently, neural embedding models have proved effective in semantic-rich tasks, but they rely on sufficient labeled data to be adequately trained. To bridge the gap between the scarce-labeled BKF and neural embedding models, we propose HiPrompt, a supervision-efficient knowledge fusion framework that elicits the few-shot reasoning ability of large language models through hierarchy-oriented prompts. Empirical results on the collected KG-Hi-BKF benchmark datasets demonstrate the effectiveness of HiPrompt.

Predicting missing links between entities in a knowledge graph is a fundamental task to deal with the incompleteness of data on the Web. Knowledge graph embeddings map nodes into a vector space to predict new links, scoring them according to geometric criteria. Relations in the graph may follow patterns that can be learned, e.g., some relations might be symmetric and others might be hierarchical. However, the learning capability of different embedding models varies for each pattern and, so far, no single model can learn all patterns equally well. In this paper, we combine the query representations from several models in a unified one to incorporate patterns that are independently captured by each model. Our combination uses attention to select the most suitable model to answer each query. The models are also mapped onto a non-Euclidean manifold, the Poincaré ball, to capture structural patterns, such as hierarchies, besides relational patterns, such as symmetry. We prove that our combination provides a higher expressiveness and inference power than each model on its own. As a result, the combined model can learn relational and structural patterns. We conduct extensive experimental analysis with various link prediction benchmarks showing that the combined model outperforms individual models, including state-of-the-art approaches.

The NIL-linking task in Entity Linking deals with cases where the text mentions do not have a corresponding entity in the associated knowledge base. NIL-linking has two sub-tasks: NIL-detection and NIL-disambiguation. NIL-detection identifies NIL-mentions in the text. Then, NIL-disambiguation determines if some NIL-mentions refer to the same out-of-knowledge base entity. Although multiple existing datasets can be adapted for NIL-detection, none of them address the problem of NIL-disambiguation. This paper presents NILK, a new dataset for NIL-linking processing, constructed from WikiData and Wikipedia dumps from two different timestamps. The NILK dataset has two main features: 1) It marks NIL-mentions for NIL-detection by extracting mentions which belong to newly added entities in Wikipedia text. 2) It provides an entity label for NIL-disambiguation by marking NIL-mentions with WikiData IDs from the newer dump. We make available the annotated dataset along with the code1. The NILK dataset is available at: https://zenodo.org/record/6607514

Recent knowledge graph (KG) embeddings have been advanced by hyperbolic geometry due to its superior capability for representing hierarchies. The topological structures of real-world KGs, however, are rather heterogeneous, i.e., a KG is composed of multiple distinct hierarchies and non-hierarchical graph structures. Therefore, a homogeneous (either Euclidean or hyperbolic) geometry is not sufficient for fairly representing such heterogeneous structures. To capture the topological heterogeneity of KGs, we present an ultrahyperbolic KG embedding (UltraE) in an ultrahyperbolic (or pseudo-Riemannian) manifold that seamlessly interleaves hyperbolic and spherical manifolds. In particular, we model each relation as a pseudo-orthogonal transformation that preserves the pseudo-Riemannian bilinear form. The pseudo-orthogonal transformation is decomposed into various operators (i.e., circular rotations, reflections and hyperbolic rotations), allowing for simultaneously modeling heterogeneous structures as well as complex relational patterns. Experimental results on three standard KGs show that UltraE outperforms previous Euclidean, hyperbolic, and mixed-curvature KG embedding approaches.

Graphs are used in several applications to represent similarities between instances. For text data, we can represent texts by different features such as bag-of-words, static embeddings (Word2vec, GloVe, etc.), and contextual embeddings (BERT, RoBERTa, etc.), leading to multiple similarities (or graphs) based on each representation. The proposal posits that incorporating the local invariance within every graph and the consistency across different graphs leads to a consensus clustering that improves the document clustering. This problem is complex and challenged with the sparsity and the noisy data included in each graph. To this end, we rely on the modularity metric, which effectively evaluates graph clustering in such circumstances. Therefore, we present a novel approach for text clustering based on both a sparse tensor representation and graph modularity. This leads to cluster texts (nodes) while capturing information arising from the different graphs. We iteratively maximize a Tensor-based Graph Modularity criterion. Extensive experiments on benchmark text clustering datasets are performed, showing that the proposed algorithm referred to as Tensor Graph Modularity -TGM- outperforms other baseline methods in terms of clustering task. The source code is available at https://github.com/TGMclustering/TGMclustering.

In 2013 property paths were introduced with the release of SPARQL 1.1. These property paths allow for describing complex queries in a more concise and comprehensive way. The W3C introduced a formal specification of the semantics of property paths, to which implementations should adhere. Most commonly used RDF stores claim to support property paths. In order to give insight into how well current implementations of property paths work we have developed BeSEPPI, a benchmark for the semantic-based evaluation of property path implementations. BeSEPPI checks whether RDF stores follow the W3Cs semantics by testing the correctness and completeness of query result sets. The results of our benchmark show that only one out of 5 benchmarked RDF stores returns complete and correct result sets for all benchmark queries.

Nowadays, large collections of photos are tagged with GPS coordinates. The modelling of such large geo-tagged corpora is an important problem in data mining and information retrieval, and involves the use of geographical information to detect topics with a spatial component. In this paper, we propose a novel geographical topic model which captures dependencies between geographical regions to support the detection of topics with complex, non-Gaussian distributed spatial structures. The model is based on a multi-Dirichlet process (MDP), a novel generalisation of the hierarchical Dirichlet process extended to support multiple base distributions. Our method thus is called the MDP-based geographical topic model (MGTM). We show how to use a MDP to dynamically smooth topic distributions between groups of spatially adjacent documents. In systematic quantitative and qualitative evaluations using independent datasets from prior related work, we show that such a model can exploit the adjacency of regions and leads to a significant improvement in the quality of topics compared to the state of the art in geographical topic modelling.

Querying data from presentation formats like HTML, for purposes such as information extraction, requires the consideration of tree structures as well as the consideration of spatial relationships between laid out elements. The underlying rationale is that frequently the rendering of tree structures is very involved and undergoing more frequent updates than the resulting layout structure. Therefore, in this paper, we present Spatial XPath (SXPath), an extension of XPath 1.0 that allows for inclusion of spatial navigation primitives into the language resulting in conceptually simpler queries on Web documents. The SXPath language is based on a combination of a spatial algebra with formal descriptions of XPath navigation, and maintains polynomial time combined complexity. Practical experiments demonstrate the usability of SXPath.

Semantic desktop environments aim at improving the effectiveness and efficiency of users carrying out daily tasks within their personal information management (PIM) infrastructure. They support the user by transferring and exploiting the explicit semantics of data items across different PIM applications. Whether such an approach does indeed reach its aim of facilitating users' life and--if so--to which extent, however, remains an open question. In this paper we address this question with the first summative evaluation of a semantic desktop. We have developed a test environment to evaluate two semantic PIM applications against standard PIM tools. As result, we have found significant efficiency and satisfaction improvements for typical PIM tasks.

Process modeling is a core task in software engineering. The explicit management of process models for purposes such as process selection and process reuse requires intelligent retrieval of process structures based on process entities and relationships, i.e. process activities, hierarchical relationship between activities, temporal relationships between activities, conditions on process flows and the modeling of domain knowledge.

The lack of a formal model of events hinders interoperability in distributed event-based systems. In this paper, we present a formal model of events, called Event-Model-F. The model is based on the foundational ontology DOLCE+DnS Ultralight (DUL) and provides comprehensive support to represent time and space, objects and persons, as well as mereological, causal, and correlative relationships between events. In addition, the Event-Model-F provides a flexible means for event composition, modeling event causality and event correlation, and representing different interpretations of the same event. The Event-Model-F is developed following the pattern-oriented approach of DUL, is modularized in different ontologies, and can be easily extended by domain specific ontologies.

Easy reuse and integration of declaratively described information in a distributed setting is one of the main motivations for building the Semantic Web. Despite of this claim, reuse and recombination of RDF data today is mostly done using data replication and procedural code. A simple declarative mechanism for reusing and combining RDF data would help users to generate content for the semantic web. Having such a mechanism, the Semantic Web could better benefit from user generated content, as it is broadly present in the so called Web 2.0, but also from better linkage of existing content.We propose Networked Graphs, which allow users to define RDF graphs both, by extensionally listing content, but also by using views on other graphs. These views can be used to include parts of other graphs, to transform data before including it and to denote rules. The relationships between graphs are described declaratively using SPARQL queries and an extension of the SPARQL semantics. Networked Graphs are easily exchangeable between and interpretable on different computers. Using existing protocols, Networked Graphss can be evaluated in a distributed setting.

The Semantic Web is based on accessing and reusing RDF data from many different sources, which one may assign different levels of authority and credibility. Existing Semantic Web query languages, like SPARQL, have targeted the retrieval, combination and reuse of facts, but have so far ignored all aspects of meta knowledge, such as origins, authorship, recency or certainty of data, to name but a few. In this paper, we present an original, generic, formalized and implemented approach for managing many dimensions of meta knowledge, like source, authorship, certainty and others. The approach re-uses existing RDF modeling possibilities in order to represent meta knowledge. Then, it extends SPARQL query processing in such a way that given a SPARQL query for data, one may request meta knowledge without modifying the query proper. Thus, our approach achieves highly flexible and automatically coordinated querying for data and meta knowledge, while completely separating the two areas of concern. 1.

In this paper, we present X-COSIM, a framework for cross context semantic information management consisting of the ontology X-COSIMO and the application programming interface X-COSIMA. X-COSIM provides what is lacking in current semantic desktops: A consistent seamless integration of a conceptually broad reference model and context dependent conceptualizations as required by applications dedicated to supporting complex work processes. We present the design of X-COSIMO and illustrate the application of X-COSIM in a scenario that involves multiple desktop applications.

Without the proliferation of formal semantic annotations, the Semantic Web is certainly doomed to failure. In earlier work we presented a new paradigm to avoid this: the 'Self Annotating Web', in which globally available knowledge is used to annotate resources such as web pages. In particular, we presented a concrete method instantiating this paradigm, called PANKOW (Pattern-based ANnotation through Knowledge On the Web). In PANKOW, a named entity to be annotated is put into several linguistic patterns that convey competing semantic meanings. The patterns that are matched most often on the Web indicate the meaning of the named entity --- leading to automatic or semi-automatic annotation.In this paper we present C-PANKOW (Context-driven PANKOW), which alleviates several shortcomings of PANKOW. First, by downloading abstracts and processing them off-line, we avoid the generation of large number of linguistic patterns and correspondingly large number of Google queries.Second, by linguistically analyzing and normalizing the downloaded abstracts, we increase the coverage of our pattern matching mechanism and overcome several limitations of the earlier pattern generation process. Third, we use the annotation context in order to distinguish the significance of a pattern match for the given annotation task. Our experiments show that C-PANKOW inherits all the advantages of PANKOW (no training required etc.), but in addition it is far more efficient and effective.

The success of the Semantic Web depends on the availability of ontologies as well as on the proliferation of web pages annotated with metadata conforming to these ontologies. Thus, a crucial question is where to acquire these metadata from. In this paper wepropose PANKOW (Pattern-based Annotation through Knowledge on theWeb), a method which employs an unsupervised, pattern-based approach to categorize instances with regard to an ontology. The approach is evaluated against the manual annotations of two human subjects. The approach is implemented in OntoMat, an annotation tool for the Semantic Web and shows very promising results.

In peer-to-peer networks, finding the appropriate answer for an information request, such as the answer to a query for RDF(S) data, depends on selecting the right peer in the network. We hereinvestigate how social metaphors can be exploited effectively andefficiently to solve this task. To this end, we define a method for query routing, REMINDIN', that lets (i) peers observewhich queries are successfully answered by other peers,(ii), memorizes this observation, and, (iii),subsequently uses this information in order to select peers to forward requests to.REMINDIN' has been implemented for the SWAP peer-to-peer platformas well as for a simulation environment. We have used the simulation environment in order to investigate how successfulvariations of REMINDIN' are and how they compare to baseline strategies in terms of number of messages forwarded in the networkand statements appropriately retrieved.

Text document clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large sets of documents into a small number of meaningful clusters. The bag of words representation used for these clustering methods is often unsatisfactory as it ignores relationships between important terms that do not cooccur literally. In order to deal with the problem, we integrate core ontologies as background knowledge into the process of clustering text documents. Our experimental evaluations compare clustering techniques based on pre-categorizations of texts from Reuters newsfeeds and on a smaller domain of an eLearning course about Java. In the experiments, improvements of results by background knowledge compared to a baseline without background knowledge can be shown in many interesting combinations.

Because it is difficult to engineer a complex ontology with time, we here consider a method that allows for factorizing the complexity of the engineering process, FONTE (Factorizing ONTology Engineering complexity). FONTE divides the engineering task into building a time-less domain ontology and a temporal theory independently from each other. FONTE provides an operator ⊗ that assembles the two independently developed ontologies into the targeted ontology. We investigate the quality of the proposed operator ⊗ by applying it to a practical case study, viz. the engineering of an ontology about researchers including temporal interactions.

The success of the Semantic Web crucially depends on the easy creation, integration and use of semantic data. For this purpose, we consider an integration scenario that defies core assumptions of current metadata construction methods. We describe a framework of metadata creation when web pages are generated from a database and the database owner is cooperatively participating in the Semantic Web. This leads us to the definition of ontology mapping rules by manual semantic annotation and the usage of the mapping rules and of web services for semantic queries. In order to create metadata, the framework combines the presentation layer with the data description layer -- in contrast to "conventional" annotation, which remains at the presentation layer. Therefore, we refer to the framework as deep annotation 1.We consider deep annotation as particularly valid because, (i), web pages generated from databases outnumber static web pages, (ii), annotation of web pages may be a very intuitive way to create semantic data from a database and, (iii), data from databases should not be materialized as RDF files, it should remain where it can be handled most efficiently -- in its databases.

Richly interlinked, machine-understandable data constitute the basis for the Semantic Web. We provide a framework, CREAM, that allows for creation of metadata. While the annotation mode of CREAM allows to create metadata for existing web pages, the authoring mode lets authors create metadata --- almost for free --- while putting together the content of a page.As a particularity of our framework, CREAM allows to create relational metadata, i.e. metadata that instantiate interrelated definitions of classes in a domain ontology rather than a comparatively rigid template-like schema asm Dublin Core. We discuss some of the requirements one has to meet when developing such an ontology-based framework, e.g. the integration of a metadata crawler, inference services, document management and a meta-ontology, and describe its implementation, viz. Ont-O-Mat, a component-based, ontology-driven Web page authoring and annotation tool.

Text clustering typically involves clustering in a high dimensional space, which appears difficult with regard to virtually all practical settings. In addition, given a particular clustering result it is typically very hard to come up with a good explanation of why the text clusters have been constructed the way they are. We propose a new approach for applying background knowledge (in terms of an ontology) during preprocessing in order to improve clustering results and allow for selection between results. The results may be distinguished and explained by the corresponding selection of concepts in the ontology. Our results compare favourably with a sophisticated baseline preprocessing strategy.

Richly interlinked, machine-understandable data constitutes the basis for the Semantic Web. Annotating web documents is one of the major techniques for creating metadata on the Web. However, annotation tools so far are restricted in their capabilities of providing richly interlinked and truely machine-understandable data. They basically allow the user to annotate with plain text according to a template structure, such as Dublin Core. We here present CREAM (Creating RElational, Annotation-based Metadata), a framework for an annotation environment that allows to construct relational metadata, i.e. metadata that comprises class instances and relationship instances. These instances are not based on a fix structure, but on a domain ontology. We discuss some of the requirements one has to meet when developing such a framework, e.g. the integration of a metadata crawler, inference services, document management and information extraction, and describe its implementation, viz. Ont-O-Mat a component-based, ontology-driven annotation tool.

The core idea of the Semantic Web is to make information accessible to human and software agents on a semantic basis. Hence, Web sites may feed directly from the Semantic Web exploiting the underlying structures for human and machine access. We have developed a domain-independent approach for developing semantic portals, viz. SEAL (SEmantic portAL), that exploits semantics for providing and accessing information at a portal as well as constructing and maintaining the portal. In this paper we focus on semantics-based means that make semantic Web sites accessible from the outside, i.e. semantics-based browsing, semantic querying, querying with semantic similarity, and machine access to semantic information. In particular, we focus on methods for acquiring and structuring community information as well as methods for sharing information.As a case study we refer to the AIFB portal - a place that is increasingly driven by Semantic Web technologies. We also discuss lessons learned from the ontology development of the AIFB portal..