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.

Abstract-- Open-Source Projects and Libraries are being used in software development while also bearing multiple security vulnerabilities. This use of third party ecosystem creates a new kind of attack surface for a product in development. An intelligent attacker can attack a product by exploiting one of the vulnerabilities present in linked projects and libraries. In this paper, we mine threat intelligence about open source projects and libraries from bugs and issues reported on public code repositories. We also track library and project dependencies for installed software on a client machine. We represent and store this threat intelligence, along with the software dependencies in a security knowledge graph. Security analysts and developers can then query and receive alerts from the knowledge graph if any threat intelligence is found about linked libraries and projects, utilized in their products. I. INTRODUCTION In the normal course of software development, developers and coders rely on various open source projects and libraries. Open source projects and libraries comprise of source code that is open for anyone to inspect, modify, update or enhance [17].

In ontology-based data access (OBDA), ontologies have been successfully employed for querying possibly unstructured and incomplete data. In this paper, we advocate using ontologies not only to formulate queries and compute their answers, but also for modifying queries by relaxing or restraining them, so that they can retrieve either more or less answers over a given dataset. Towards this goal, we first illustrate that some domain knowledge that could be naturally leveraged in OBDA can be expressed using complex role inclusions (CRI). Queries over ontologies with CRI are not first-order (FO) rewritable in general. We propose an extension of DL-Lite with CRI, and show that conjunctive queries over ontologies in this extension are FO rewritable. Our main contribution is a set of rules to relax and restrain conjunctive queries (CQs). Firstly, we define rules that use the ontology to produce CQs that are relaxations/restrictions over any dataset. Secondly, we introduce a set of data-driven rules, that leverage patterns in the current dataset, to obtain more fine-grained relaxations and restrictions.

A previous similar case in common law systems can be used as a reference with respect to the current case such that identical situations can be treated similarly in every case. However, current approaches for judgment document similarity computation failed to capture the core semantics of judgment documents and therefore suffer from lower accuracy and higher computation complexity. In this paper, a knowledge block summarization based machine learning approach is proposed to compute the semantic similarity of Chinese judgment documents. By utilizing domain ontologies for judgment documents, the core semantics of Chinese judgment documents is summarized based on knowledge blocks. Then the WMD algorithm is used to calculate the similarity between knowledge blocks. At last, the related experiments were made to illustrate that our approach is very effective and efficient in achieving higher accuracy and faster computation speed in comparison with the traditional approaches.