We provide a detailed example for modular ontology modeling based on ontology design patterns. It is similar to the Chess Ontology tutorial in [6], which we suggest to read first. We will be less verbose in this tutorial; we provide it because additional examples should be helpful for those interested in adopting the modular ontology modeling methodology - see [6] and the book [2] in which it is contained. We assume that the reader is familiar with the Web Ontology Language OWL [5, 4]. Before we dive into the actual modeling, let us present the general workflow which we recommend for ontology modeling, and which is the same as in [6]. The steps of this workflow are laid out in Figure 1. We will refer to these steps, and explain them in more detail, as we advance through the tutorial. Every ontology is designed for a purpose; this purpose may be defined by a use case, or by a set of use cases, or possibly by a set of potential use cases, which may include the future extensions or refinements of the ontology, and future reuse of the ontology by others. How specific should a use case be? Conventional wisdom may suggest that it is always better to be more specific. However, in the context of ontology modeling the case is not as clear-cut. A very specific use case may give rise to an ontology which is very specialized, i.e. modeling choices (so-called ontological commitments) may be made which fit only the very specific and detailed use case. As a consequence, later modifications, e.g. by widening the scope of the application (and therefore of the underlying ontology) become very cumbersome as they may conflict with ontological commitments made earlier.

I have been in the space of artificial intelligence for a while and am aware that multiple classifications, distinctions, landscapes, and infographics exist to represent and track the different ways to think about AI. However, I am not a big fan of those categorization exercises, mainly because I tend to think that the effort of classifying dynamic data points into predetermined fixed boxes is often not worth the benefits of having such a "clear" framework (this is a generalization of course as sometimes they are extremely useful). I also believe this landscape is useful for people new to the space to grasp at-a-glance the complexity and depth of this topic, as well as for those more experienced to have a reference point and to create new conversations around specific technologies. What follows is then an effort to draw an architecture to access knowledge on AI and follow emergent dynamics, a gateway of pre-existing knowledge on the topic that will allow you to scout around for additional information and eventually create new knowledge on AI. I call it the AI Knowledge Map (AIKM).

Data Science architecture is bringing ideas together that transforms data into actionable knowledge. The excitement in this is the ability to make more decisions that solve business revenue problems. To engage semantic SEO, it is necessary to have a strategy, tactics, and the right tools for a structured approach to data. The modern web demands connected content is based on the context of the user, the recognition of related data entities, and how they are connected to the searches made. An understanding of Natural Language, users, KPI's, and your structured data architecture is fundamental to generating better search results. By gaining a deeper knowledge about a web site's core UX architecture, you can use it to store and analyze data, specifically structured data. Create a clear business strategy for how to use Google analytics data to compete and deploy the right UX, technology, and information architecture to drive user engagement. Adjusting your business culture to create digital content that drives mobile search results and conversions requires a multifaceted approach. Ask any successful digital marketer as to what they regard as core prerequisites for a successful SEO marketing campaign or your paid search investment, and undoubtedly most responses will say something about obtaining or making wise integrations from data points.

The AI revolution is taking place right now. In contrast to what the scary headlines and stories suggest, the revolution is not about robots or computers taking over humanity. The real revolution does have and will have a continuing impact on all facets of society, but in a more subtle way. This blog will keep you informed about the developments in AI by emphasising the actual practical implications for society and business rather than stating futuristic claims about what may happen. Let us start with the concept of artificial intelligence, AI, for short.

We present an analytical study of the quality of metadata about samples used in biomedical experiments. The metadata under analysis are stored in two well- known databases: BioSample---a repository managed by the National Center for Biotechnology Information (NCBI), and BioSamples---a repository managed by the European Bioinformatics Institute (EBI). We tested whether 11.4M sample metadata records in the two repositories are populated with values that fulfill the stated requirements for such values. Our study revealed multiple anomalies in the metadata. Most metadata field names and their values are not standardized or controlled. Even simple binary or numeric fields are often populated with inadequate values of different data types. By clustering metadata field names, we discovered there are often many distinct ways to represent the same aspect of a sample. Overall, the metadata we analyzed reveal that there is a lack of principled mechanisms to enforce and validate metadata requirements. The significant aberrancies that we found in the metadata are likely to impede search and secondary use of the associated datasets.

Data streams occur widely in various real world applications. The research on streaming data mainly focuses on the data management, query evaluation and optimization on these data, however the work on reasoning procedures for streaming knowledge bases on both the assertional and terminological levels is very limited. Typically reasoning services on large knowledge bases are very expensive, and need to be applied continuously when the data is received as a stream. Hence new techniques for optimizing this continuous process is needed for developing efficient reasoners on streaming data. In this paper, we survey the related research on reasoning on expressive logics that can be applied to this setting, and point to further research directions in this area.

In commonsense knowledge representation, the Open World Assumption is adopted as a general standard strategy for the design, construction and use of ontologies, e.g. in OWL. This strategy limits the inferencing capabilities of any system using these ontologies because non-asserted statements could be assumed to be alternatively true or false in different interpretations. In this paper, we investigate the application of the Closed World Assumption to enable a better exploitation of the structural knowledge encoded in a SUMO-based ontology. To that end, we explore three different Closed World Assumption formulations for subclass and disjoint relations in order to reduce the ambiguity of the knowledge encoded in first-order logic ontologies. We evaluate these formulations on a practical experimentation using a very large commonsense benchmark automatically obtained from the knowledge encoded in WordNet through its mapping to SUMO. The results show that the competency of the ontology improves more than 47 % when reasoning under the Closed World Assumption. As conclusion, applying the Closed World Assumption automatically to first-order logic ontologies reduces their expressed ambiguity and more commonsense questions can be answered.

The word semantic is a Linguistic term. It means something related to meaning in a language or logic. In a natural language, semantic analysis is relating the structures and occurrences of the words, phrases, clauses, paragraphs etc and understanding the idea of what's written in particular text. Does the formation of the sentences, occurrencSemantic Analysis, Semantic Search,Domain Ontology, Natural Language Processinges of the words make any sense? The challenge we face in the technologically advanced world is to make the computer understand the language or logic as much as the human does.

We argue that logical semantics might have faltered due to its failure in distinguishing between two fundamentally very different types of concepts: ontological concepts, that should be types in a strongly-typed ontology, and logical concepts, that are predicates corresponding to properties of and relations between objects of various ontological types. We will then show that accounting for these differences amounts to the integration of lexical and compositional semantics in one coherent framework, and to an embedding in our logical semantics of a strongly-typed ontology that reflects our commonsense view of the world and the way we talk about it in ordinary language. We will show that in such a framework a number of challenges in natural language semantics can be adequately and systematically treated.

We study the problem of finite ontology mediated query answering (FOMQA), the variant of OMQA where the represented world is assumed to be finite, and thus only finite models of the ontology are considered. We adopt the most typical setting with unions of conjunctive queries and ontologies expressed in description logics (DLs). The study of FOMQA is relevant in settings that are not finitely controllable. This is the case not only for DLs without the finite model property, but also for those allowing transitive role declarations. When transitive roles are allowed, evaluating queries is challenging: FOMQA is undecidable for SHOIF and only known to be decidable for the Horn fragment of ALCIF. We show decidability of FOMQA for three proper fragments of SOIF: SOI, SOF, and SIF. Our approach is to characterise models relevant for deciding finite query entailment. Relying on a certain regularity of these models, we develop automata-based decision procedures with optimal complexity bounds.