As data and knowledge expand rapidly, adopting systematic methodologies for ontology generation has become crucial. With the daily increases in data volumes and frequent content changes, the demand for databases to store and retrieve information for the creation of knowledge graphs has become increasingly urgent. The previously established Knowledge Acquisition and Representation Methodology (KNARM) outlines a systematic approach to address these challenges and create knowledge graphs. However, following this methodology highlights the existing challenge of seamlessly integrating Neo4j databases with the Web Ontology Language (OWL). Previous attempts to integrate data from Neo4j into an ontology have been discussed, but these approaches often require an understanding of description logics (DL) syntax, which may not be familiar to many users. Thus, a more accessible method is necessary to bridge this gap. This paper presents a user-friendly approach that utilizes Python and its rdflib library to support ontology development. We showcase our novel approach through a Neo4j database we created by integrating data from the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database. Using this dataset, we developed a Python script that automatically generates the required classes and their axioms, facilitating a smoother integration process. This approach offers a practical solution to the challenges of ontology generation in the context of rapidly growing adverse drug event datasets, supporting improved drug safety monitoring and public health decision-making.

The importance of taking individual, potentially conflicting perspectives into account when dealing with knowledge has been widely recognised. Many existing ontology management approaches fully merge knowledge perspectives, which may require weakening in order to maintain consistency; others represent the distinct views in an entirely detached way. As an alternative, we propose Standpoint Logic, a simple, yet versatile multi-modal logic "add-on" for existing KR languages intended for the integrated representation of domain knowledge relative to diverse, possibly conflicting standpoints, which can be hierarchically organised, combined and put in relation to each other. Starting from the generic framework of First-Order Standpoint Logic (FOSL), we subsequently focus our attention on the fragment of sentential formulas, for which we provide a polytime translation into the standpoint-free version. This result yields decidability and favourable complexities for a variety of highly expressive decidable fragments of first-order logic. Using some elaborate encoding tricks, we then establish a similar translation for the very expressive description logic SROIQb_s underlying the OWL 2 DL ontology language. By virtue of this result, existing highly optimised OWL reasoners can be used to provide practical reasoning support for ontology languages extended by standpoint modelling.

Recently, the field of knowledge representation is drawing a lot of inspiration from database theory. In particular, in the area of description logics and ontology languages, interest has shifted from satisfiability checking to query answering, with various query notions adopted from databases, like (unions of) conjunctive queries or different kinds of path queries. Likewise, the finite model semantics is being established as a viable and interesting alternative to the traditional semantics based on unrestricted models. In this paper, we investigate diverse database-inspired reasoning problems for very expressive description logics (all featuring the worrisome trias of inverses, counting, and nominals) which have in common that role paths of unbounded length can be described (in the knowledge base or of the query), leading to a certain non-locality of the reasoning problem. We show that for all the cases considered, undecidability can be established by very similar means. Most notably, we show undecidability of finite entailment of unions of conjunctive queries for a fragment of SHOIQ (the logic underlying the OWL DL ontology language), and undecidability of finite entailment of conjunctive queries for a fragment of SROIQ (the logical basis of the more recent and popular OWL 2 DL standard).

Ontology, as a discipline of philosophy, explains the nature of existence and has its roots in Aristotle and Plato studies on "metaphysics" (Welty and Guarino, 2001). However, the word ontology originated from two Greek words: ontos (being) and logos (word), and conceived for the first time during the Sixteen century by German philosophers (Welty and Guarino, 2001). From then till the mid-twentieth, ontology evolved mainly as a branch of philosophy. However, with the advent of Artificial Intelligence since the 1950s, researchers perceived the necessity of ontology to describe a new world of intelligent systems (Welty and Guarino, 2001). Moreover, with the development of the World Wide Web in the 1990s, ontology development got to be common among different domain specialists to define and share the concepts and entities in their fields on the Internet (Noy et al., 2001). During the last three decades, ontology development studies have evolved and shifted from theoretical issues of ontology to practical implications of the use of ontology in real-world, large-scale applications (Noy et al., 2001). Nowadays, ontology development focuses mainly on defining machine interpretable concepts and their relationships in a domain. However, ontology development also pursues other goals, such as providing a common conceptualization of the domain on which different experts agree, (Métral and Cutting-Decelle, 2011) and enable them to reuse the domain knowledge (Noy et al., 2001). It also enables researchers to easily analyze the domain knowledge and eloquently express the domain assumptions.

Axiom pinpointing refers to the task of finding the specific axioms in an ontology which are responsible for a consequence to follow. This task has been studied, under different names, in many research areas, leading to a reformulation and reinvention of techniques. In this work, we present a general overview to axiom pinpointing, providing the basic notions, different approaches for solving it, and some variations and applications which have been considered in the literature. This should serve as a starting point for researchers interested in related problems, with an ample bibliography for delving deeper into the details.

Your computer is continuously executing programs, but does it really understand them? Not in any meaningful sense. That burden falls upon human knowledge workers, who are increasingly asked to write and understand code. They would benefit greatly from intelligent tools that reveal the connections between their code and its subject matter. Towards this prospect, we develop an AI system that forms semantic representations of computer programs, using techniques from knowledge representation and program analysis. We focus on code written for data science, although our method is more generally applicable. The semantic representations are created through a novel algorithm for the semantic enrichment of dataflow graphs. This algorithm is undergirded by a new ontology language for modeling computer programs and a new ontology about data science, written in this language.

Adding datatypes to ontology-mediated queries (OMQs) often makes query answering hard. As a consequence, the use of datatypes in OWL 2 QL has been severely restricted. In this paper we propose a new, non-uniform, way of analyzing the data-complexity of OMQ answering with datatypes. Instead of restricting the ontology language we aim at a classification of the patterns of datatype atoms in OMQs into those that can occur in non-tractable OMQs and those that only occur in tractable OMQs. To this end we establish a close link between OMQ answering with datatypes and constraint satisfaction problems over the datatypes. In a case study we apply this link to prove a P/coNP-dichotomy for OMQs over DL-Lite extended with the datatype (Q,<=). The proof employs a recent dichotomy result by Bodirsky and Kára for temporal constraint satisfaction problems.

Editor's Note: An update to this article has been posted here on 7/14/04. As the hype of past decades fades, the current heir to the artificial intelligence legacy may well be ontologies. Evolving from semantic network notions, modern ontologies are proving quite useful. And they are doing so without relying on the jumble of rule-based techniques common in earlier knowledge representation efforts. These structured depictions or models of known (and accepted) facts are being built today to make a number of applications more capable of handling complex and disparate information. They appear most effective when the semantic distinctions that humans take for granted are crucial to the application's purpose.

Ontology-based data access (OBDA) is a novel paradigm facilitating access to relational data, realized by linking data sources to an ontology by means of declarative mappings. DL-Lite_R, which is the logic underpinning the W3C ontology language OWL 2 QL and the current language of choice for OBDA, has been designed with the goal of delegating query answering to the underlying database engine, and thus is restricted in expressive power. E.g., it does not allow one to express disjunctive information, and any form of recursion on the data. The aim of this paper is to overcome these limitations of DL-Lite_R, and extend OBDA to more expressive ontology languages, while still leveraging the underlying relational technology for query answering. We achieve this by relying on two well-known mechanisms, namely conservative rewriting and approximation, but significantly extend their practical impact by bringing into the picture the mapping, an essential component of OBDA. Specifically, we develop techniques to rewrite OBDA specifications with an expressive ontology to "equivalent" ones with a DL-Lite_R ontology, if possible, and to approximate them otherwise. We do so by exploiting the high expressive power of the mapping layer to capture part of the domain semantics of rich ontology languages. We have implemented our techniques in the prototype system OntoProx, making use of the state-of-the-art OBDA system Ontop and the query answering system Clipper, and we have shown their feasibility and effectiveness with experiments on synthetic and real-world data.

The Distributed Ontology Language DOL, currently being standardized as ISO WD 17347 within the OntoIOp (Ontology Integration and Interoperability) activity of ISO/TC 37, provides a unified framework for (1) ontologies formalized in heterogeneous logics, (2) modular ontologies, (3) links between ontologies, and (4) ontology annotation. A DOL ontology consists of modules formalized in languages such as OWL or Common Logic, serialized in the existing syntaxes of these languages. On top, DOL’s meta level allows for expressing heterogeneous ontologies and links between ontologies, including (heterogeneous) imports and alignments, conservative extensions, and theory interpretations. We present the abstract syntax of these meta-level constructs, with three alternative semantics: direct, translational, and collapsed semantics.