This Chapter describes a new technique, called "knowledge patterns", for helping construct axiom-rich, formal ontologies, based on identifying and explicitly representing recurring patterns of knowledge (theory schemata) in the ontology, and then stating how those patterns map onto domain-specific concepts in the ontology. From a modeling perspective, knowledge patterns provide an important insight into the structure of a formal ontology: rather than viewing a formal ontology simply as a list of terms and axioms, knowledge patterns views it as a collection of abstract, modular theories (the "knowledge patterns") plus a collection of modeling decisions stating how different aspects of the world can be modeled using those theories. Knowledge patterns make both those abstract theories and their mappings to the domain of interest explicit, thus making modeling decisions clear, and avoiding some of the ontological confusion that can otherwise arise. In addition, from a computational perspective, knowledge patterns provide a simple and computationally efficient mechanism for facilitating knowledge reuse. We describe the technique and an application built using them, and then critique its strengths and weaknesses. We conclude that this technique enables us to better explicate both the structure and modeling decisions made when constructing a formal axiom-rich ontology.

Ontologies are the prime way of organizing data in the Semantic Web. Often, it is necessary to combine several, independently developed ontologies to obtain a knowledge graph fully representing a domain of interest. The complementarity of existing ontologies can be leveraged by merging them. Existing approaches for ontology merging mostly implement a binary merge. However, with the growing number and size of relevant ontologies across domains, scalability becomes a central challenge. A multi-ontology merging technique offers a potential solution to this problem. We present CoMerger, a scalable multiple ontologies merging method. For efficient processing, rather than successively merging complete ontologies pairwise, we group related concepts across ontologies into partitions and merge first within and then across those partitions. The experimental results on well-known datasets confirm the feasibility of our approach and demonstrate its superiority over binary strategies. A prototypical implementation is freely accessible through a live web portal.

Ontologies form the basic interest in various computer science disciplines such as semantic web, information retrieval, database design, etc. They aim at providing a formal, explicit and shared conceptualization and understanding of common domains between different communities. In addition, they allow for concepts and their constraints of a specific domain to be explicitly defined. However, the distributed nature of ontology development and the differences in viewpoints of the ontology engineers have resulted in the so called "semantic heterogeneity" between ontologies. Semantic heterogeneity constitutes the major obstacle against achieving interoperability between ontologies. To overcome this obstacle, we present a multi-purpose framework which exploits the WordNet generic knowledge base for: i) Discovering and correcting the incorrect semantic relations between the concepts of the ontology in a specific domain. This step is a primary step of ontology merging. ii) Merging domain-specific ontologies through computing semantic relations between their concepts. iii) Handling the issue of missing concepts in WordNet through the acquisition of statistical information on the Web. And iv) Enriching WordNet with these missing concepts. An experimental instantiation of the framework and comparisons with state-of-the-art syntactic and semantic-based systems validate our proposal.

In the context of the Covid-19 pandemic, many were quick to spread deceptive information. I investigate here how reasoning in Description Logics (DLs) can detect inconsistencies between trusted medical sources and not trusted ones. The not-trusted information comes in natural language (e.g. "Covid-19 affects only the elderly"). To automatically convert into DLs, I used the FRED converter. Reasoning in Description Logics is then performed with the Racer tool.

We study privacy-preserving query answering in Description Logics (DLs). Specifically, we consider the approach of controlled query evaluation (CQE) based on the notion of instance indistinguishability. We derive data complexity results for query answering over DL-Lite$_{\mathcal{R}}$ ontologies, through a comparison with an alternative, existing confidentiality-preserving approach to CQE. Finally, we identify a semantically well-founded notion of approximated query answering for CQE, and prove that, for DL-Lite$_{\mathcal{R}}$ ontologies, this form of CQE is tractable with respect to data complexity and is first-order rewritable, i.e., it is always reducible to the evaluation of a first-order query over the data instance.

Intuitively, locally finiteness means that the refinement operator is computable, completeness means we can generate, by refinement of a, any element of G related to a given element g 1 by the order relation, and properness means that a refinement operator does not generate elements which are equivalent to the element being refined. When a refinement operator is locally finite, complete and proper, we say that it is ideal. Notice that all the subsumption relations presented above satisfy the reflexive 2 and transitive 3 properties. Therefore, the pair (G,), where G is the set of all DLGs given a set of labels L, and is any of the subsumption relations defined above is a quasi-ordered set. Thus, this opens the door to defining refinement operators for DLGs. Intuitively, a downward refinement operator for DLGs will generate refinements of a given DLG by either adding vertices, edges, or by making some of the labels more specific, thus making the graph more specific. In the following subsections, we will introduce a collection of refinement operators for connected DLGs, and discuss their theoretical properties. A summary of these operators is shown in Table 1, where we show that under the object-identity constraint, all the refinement operators presented in this document are ideal. If we do not impose object-identity, then the operators are locally complete and complete, but not proper.

One of the most important goals of digital humanities is to provide researchers with data and tools for new research questions, either by increasing the scale of scholarly studies, linking existing databases, or improving the accessibility of data. Here, the FAIR principles provide a useful framework as these state that data needs to be: Findable, as they are often scattered among various sources; Accessible, since some might be offline or behind paywalls; Interoperable, thus using standard knowledge representation formats and shared vocabularies; and Reusable, through adequate licensing and permissions. Integrating data from diverse humanities domains is not trivial, research questions such as "was economic wealth equally distributed in the 18th century?", or "what are narratives constructed around disruptive media events?") and preparation phases (e.g. data collection, knowledge organisation, cleaning) of scholars need to be taken into account. In this chapter, we describe the ontologies and tools developed and integrated in the Dutch national project CLARIAH to address these issues across datasets from three fundamental domains or "pillars" of the humanities (linguistics, social and economic history, and media studies) that have paradigmatic data representations (textual corpora, structured data, and multimedia). We summarise the lessons learnt from using such ontologies and tools in these domains from a generalisation and reusability perspective.

The big rub on the first generation of graph databases was that although RDF triple stores were great at storing the simple sentence, they had a hard time with the adverbs, adjectives and clarifying phrases of your data story. If I wanted to store'John is a carpenter since 2001' or'John from Alberta Canada is a carpenter liked by 702 people', the syntax of old-school triple stores had a more tedious, but not impossible way of handling it. It involved creating extra nodes that were confusing to some and a process called reification. Until about a year ago, labeled property graphs (LPG) were better at color and detail than RDF, having a more intuitive syntax for clarifying adverbs, adjectives, and phrases. That was, of course, until recently.

Interoperability in the construction sector is a key issue and researchers, developers and designers have tackled since the introduction of CAD systems. Traditionally, engineers, architects and site operators interact and track their information exchange through paper or digitalized drawings and e-mails. With the introduction of Building Information Modelling (BIM) techniques and tools, operators are using new solutions and methods to keep track and exploit these data. Cover image: ifcOWL ontology (version IFC4ADD2) visualized thanks to WebVOWL, available hereWhat has been described as traditional method corresponds to Level 0 in well-known BIM levels definition. The concept of BIM level 1 represents the criteria needed for the full collaboration for the industry.

In this paper, we introduce a novel interpreting framework that learns an interpretable model based on an ontology-based sampling technique to explain agnostic prediction models. Different from existing approaches, our algorithm considers contextual correlation among words, described in domain knowledge ontologies, to generate semantic explanations. To narrow down the search space for explanations, which is a major problem of long and complicated text data, we design a learnable anchor algorithm, to better extract explanations locally. A set of regulations is further introduced, regarding combining learned interpretable representations with anchors to generate comprehensible semantic explanations. An extensive experiment conducted on two real-world datasets shows that our approach generates more precise and insightful explanations compared with baseline approaches.