A review of data mining ontologies

PurposeThis work provides a generic review of the existing data mining ontologies (DMOs) and also provides a base platform for ontology developers and researchers for gauging the ontologies for satisfactory coverage and usage.Design/methodology/approachThe study uses a systematic literature review approach to identify 35 DMOs in the domain between the years 2003 and 2021. Various parameters, like purpose, design methodology, operations used, language representation, etc. are available in the literature to review ontologies. Accompanying the existing parameters, a few parameters, like semantic reasoner used, knowledge representation formalism was added and a list of 20 parameters was prepared. It was then segregated into two groups as generic parameters and core parameters to review DMOs.FindingsIt was observed that among the 35 papers under the study, 26 papers were published between the years 2006 and 2016. Larisa Soldatova, Saso Dzeroski and Pance Panov were the most productive authors of these DMO-related publications. The ontological review indicated that most of the DMOs were domain and task ontologies. Majority of ontologies were formal, modular and represented using web ontology language (OWL). The data revealed that Ontology development 101, METHONTOLOGY was the preferred design methodology, and application-based approaches were preferred for evaluation. It was also observed that around eight ontologies were accessible, and among them, three were available in ontology libraries as well. The most reused ontologies were OntoDM, BFO, OBO-RO, OBI, IAO, OntoDT, SWO and DMOP. The most preferred ontology editor was Protégé, whereas the most used semantic reasoner was Pellet. Even ontology metrics for 16 DMOs were also available.Originality/valueThis paper carries out a basic level review of DMOs employing a parametric approach, which makes this study the first of a kind for the review of DMOs.

Representing and Reasoning with Event Models for Epistemic Planning

The standard representation formalism for multi-agent epistemic planning has one central disadvantage: When you use event models in dynamic epistemic logic (DEL) to describe the action of one agent, the model must specify not only the actual change and the change of that agent's knowledge. Also required is the epistemic change of any agents that may be observing the first agent performing the action, plus the epistemic change for any further agents that failed to observe that anything had taken place. To overcome the gap between this complex DEL notion of events and a more commonsense notion of actions, we propose a simple high-level action description language for multi-agent epistemic planning domains with just one type of effect laws: a causes x if y. Effect x can either be a physical effect, or an observation from an independent set that is specific to individual agents. We formally prove that any DEL event model can be described in this way. We show how this language provides a framework for expressing a variety of executability and action models; such as describing actions that are both ontic and epistemic, partially observable, or nondeterministic. We further combine our representation of event models with a description language for finitary initial epistemic theories, and we show how this allows us to reason about the effects of a sequence of actions in a multi-agent epistemic domain by updating a single multi-pointed epistemic model.

DaRLing: A Datalog rewriter for OWL 2 RL ontological reasoning under SPARQL queries

The W3C Web Ontology Language (OWL) is a powerful knowledge representation formalism at the basis of many semantic-centric applications. Since its unrestricted usage makes reasoning undecidable already in case of very simple tasks, expressive yet decidable fragments have been identified. Among them, we focus on OWL 2 RL, which offers a rich variety of semantic constructors, apart from supporting all RDFS datatypes. Although popular Web resources - such as DBpedia - fall in OWL 2 RL, only a few systems have been designed and implemented for this fragment. None of them, however, fully satisfy all the following desiderata: (i) being freely available and regularly maintained; (ii) supporting query answering and SPARQL queries; (iii) properly applying the sameAs property without adopting the unique name assumption; (iv) dealing with concrete datatypes. To fill the gap, we present DaRLing, a freely available Datalog rewriter for OWL 2 RL ontological reasoning under SPARQL queries. In particular, we describe its architecture, the rewriting strategies it implements, and the result of an experimental evaluation that demonstrates its practical applicability.

Synthesizing Argumentation Frameworks from Examples

Argumentation is today a topical area of artificial intelligence (AI) research. Abstract argumentation, with argumentation frameworks (AFs) as the underlying knowledge representation formalism, is a central viewpoint to argumentation in AI. Indeed, from the perspective of AI and computer science, understanding computational and representational aspects of AFs is key in the study of argumentation. Realizability of AFs has been recently proposed as a central notion for analyzing the expressive power of AFs under different semantics. In this work, we propose and study the AF synthesis problem as a natural extension of realizability, addressing some of the shortcomings arising from the relatively stringent definition of realizability. In particular, realizability gives means of establishing exact conditions on when a given collection of subsets of arguments has an AF with exactly the given collection as its set of extensions under a specific argumentation semantics. However, in various settings within the study of dynamics of argumentation---including revision and aggregation of AFs---non-realizability can naturally occur. To accommodate such settings, our notion of AF synthesis seeks to construct, or synthesize, AFs that are semantically closest to the knowledge at hand even when no AFs exactly representing the knowledge exist. Going beyond defining the AF synthesis problem, we study both theoretical and practical aspects of the problem. In particular, we (i) prove NP-completeness of AF synthesis under several semantics, (ii) study basic properties of the problem in relation to realizability, (iii) develop algorithmic solutions to NP-hard AF synthesis using the constraint optimization paradigms of maximum satisfiability and answer set programming, (iv) empirically evaluate our algorithms on different forms of AF synthesis instances, as well as (v) discuss variants and generalizations of AF synthesis.

Enhancing Lazy Grounding with Lazy Normalization in Answer-Set Programming

Answer-Set Programming (ASP) is an expressive rule-based knowledge-representation formalism. Lazy grounding is a solving technique that avoids the well-known grounding bottleneck of traditional ASP evaluation but is restricted to normal rules, severely limiting its expressive power. In this work, we introduce a framework to handle aggregates by normalizing them on demand during lazy grounding, hence relieving the restrictions of lazy grounding significantly. We term our approach as lazy normalization and demonstrate its feasibility for different types of aggregates. Asymptotic behavior is analyzed and correctness of the presented lazy normalizations is shown. Benchmark results indicate that lazy normalization can bring up-to exponential gains in space and time as well as enable ASP to be used in new application areas.

Exploiting Justifications for Lazy Grounding of Answer Set Programs

Answer set programming (ASP) is an established knowledge representation formalism. Lazy grounding avoids the so-called grounding bottleneck of ASP by interleaving grounding and solving; this technique was recently extended to work with conflict-driven clause learning. Unfortunately, it often happens that such a lazy grounding ASP system, at the fixpoint of the evaluation, arrives at an assignment that contains literals that are true but unjustified. The system then is unable to determine the actual causes of the situation and falls back to chronological backtracking, potentially wasting an exponential amount of time. In this paper, we show how top-down query mechanisms can be used to analyze the situation, learn a new clause or nogood, and backjump further in the search tree. Contributions include a rephrasing of lazy grounding in terms of justifications and algorithms to construct relevant justifications without grounding. Initial experiments indicate that the newly developed techniques indeed allow for an exponential speed-up.

Logic on MARS: Ontologies for Generalised Property Graphs

Graph-structured data is used to represent large information collections, called knowledge graphs, in many applications. Their exact format may vary, but they often share the concept that edges can be annotated with additional information, such as validity time or provenance information. Property Graph is a popular graph database format that also provides this feature. We give a formalisation of a generalised notion of Property Graphs, called multi-attributed relational structures (MARS), and introduce a matching knowledge representation formalism, multi-attributed predicate logic (MAPL). We analyse the expressive power of MAPL and suggest a simpler, rule-based fragment of MAPL that can be used for ontological reasoning on Property Graphs. To the best of our knowledge, this is the first approach to making Property Graphs and related data structures accessible to symbolic AI.

Between Language and Consciousness: Linguistic Qualia, Awareness, and Cognitive Models

The main goal of the paper is to present a putative role of consciousness in language capacity. The paper contrasts the two approaches characteristic for cognitive semiotics and cognitive science. Language is treated as a mental phenomenon and a cognitive faculty (in contrast to approaches that define language as a primarily social phenomenon). The analysis of language activity is based on the Chalmers’ (1996) distinction between the two forms of consciousness: phenomenal (simply “consciousness”) and psychological (“awareness”). The approach is seen as an alternative to phenomenological analyses typical for cognitive semiotics. Further, a cognitive model of the language faculty is described. The model is implemented in SNePS/GLAIR architecture and based on GATN grammar and semantic networks as a representation formalism. The model - reflecting traditionally distinguished linguistic structures (Jackendoff 2002: 198) - consists of phonological, syntactic, and semantic modules. I claim that the most important role in the phenomenon of language (and in explanations thereof) is played by psychological consciousness. Phenomenal consciousness accompanies various stages of language functioning (e.g. linguistic qualia), but is not indispensable in explanations of the language faculty.