The problem of vocabulary gap among students in the early years of school, and the resulting impact on school success have received significant attention in the past [3-5, 18, 20, 23]. Early introduction of vocabulary through either direct or indirect instruction helps children learn to read well and forms a strong foundation for literacy, which in turn helps children in accelerated reading to learn. Importantly, while reading new texts, children tend to connect the words they are familiar with to the words exposed in the texts; hence, greater and diverse vocabulary leads to better comprehension of the texts being read. Given the enormity of vocabulary in English language (and most languages in general), new word acquisition is an ongoing process for many years, and 1 sometimes is even lifelong for many people. However, the highest rate of vocabulary development happens in the early years, and teachers in elementary schools focus (often in their own ways, since no universally standardized word lists or procedures exist) a nontrivial amount of time in introducing words to children through both direct and implicit instruction.

Reasoning about exceptions in ontologies is nowadays one of the challenges the description logics community is facing. The paper describes a preferential approach for dealing with exceptions in Description Logics, based on the rational closure. The rational closure has the merit of providing a simple and efficient approach for reasoning with exceptions, but it does not allow independent handling of the inheritance of different defeasible properties of concepts. In this work we outline a possible solution to this problem by introducing a variant of the lexicographical closure, that we call skeptical closure, which requires to construct a single base. We develop a bi-preference semantics semantics for defining a characterization of the skeptical closure.

We survey the AI research carried out in Greece recently. A milestone for AI research in Greece came in 1988, when the Hellenic Artificial Intelligence Society (EETN) was founded as a nonprofit scientific organization devoted to organizing and promoting AI research in Greece and abroad. EETN is an affiliated society of the European Association for Artificial Intelligence (EurAI, formerly known as ECCAI). One of the many roles of EETN is the organization of conferences, workshops, summer schools, and other events, such as the Hellenic Conference on Artificial Intelligence (SETN). The first SETN was Science with a team well grounded in KR.

While the amount of data stored in current information systems continuously grows, and the processes making use of such data become more and more complex, extracting knowledge and getting insights from these data, as well as governing both data and the associated processes, are still challenging tasks. The problem is complicated by the proliferation of data sources and services both within a single organization, and in cooperating environments. Effectively accessing, integrating and managing data in complex organizations is still one of the main issues faced by the information technology industry today. Indeed, it is not surprising that data scientists spend a comparatively large amount of time in the data preparation phase of a project, compared with the data minining and knowledge discovery phase. Whether you call it data wrangling, data munging, or data integration, it is estimated that 50%-80% of a data scientists time is spent on collecting and organizing data for analysis. If we consider that in any complex organization, data governance is also essential for tasks other than data analytics, we can conclude that the challenge of identifying, gathering, retaining, and providing access to all relevant data for the business at an acceptable cost, is huge.

We present an OWL 2 ontology representing the Saint Gall plan, one of the most ancient documents arrived intact to us, which describes the ideal model of a Benedictine monastic complex that inspired the design of many European monasteries.

Semantic vectors are learned from data to express semantic relationships between elements of information, for the purpose of solving and informing downstream tasks. Other models exist that learn to map and classify supervised data. However, the two worlds of learning rarely interact to inform one another dynamically, whether across types of data or levels of semantics, in order to form a unified model. We explore the research problem of learning these vectors and propose a framework for learning the semantics of knowledge incrementally and online, across multiple mediums of data, via binary vectors. We discuss the aspects of this framework to spur future research on this approach and problem.

Formal ontologies are axiomatizations in a logic-based formalism. The development of formal ontologies, and their important role in the Semantic Web area, is generating considerable research on the use of automated reasoning techniques and tools that help in ontology engineering. One of the main aims is to refine and to improve axiomatizations for enabling automated reasoning tools to efficiently infer reliable information. Defects in the axiomatization can not only cause wrong inferences, but can also hinder the inference of expected information, either by increasing the computational cost of, or even preventing, the inference. In this paper, we introduce a novel, fully automatic white-box testing framework for first-order logic ontologies. Our methodology is based on the detection of inference-based redundancies in the given axiomatization. The application of the proposed testing method is fully automatic since a) the automated generation of tests is guided only by the syntax of axioms and b) the evaluation of tests is performed by automated theorem provers. Our proposal enables the detection of defects and serves to certify the grade of suitability --for reasoning purposes-- of every axiom. We formally define the set of tests that are generated from any axiom and prove that every test is logically related to redundancies in the axiom from which the test has been generated. We have implemented our method and used this implementation to automatically detect several non-trivial defects that were hidden in various first-order logic ontologies. Throughout the paper we provide illustrative examples of these defects, explain how they were found, and how each proof --given by an automated theorem-prover-- provides useful hints on the nature of each defect. Additionally, by correcting all the detected defects, we have obtained an improved version of one of the tested ontologies: Adimen-SUMO.

Problems faced by international standardization bodies become more and more crucial as the number and the size of the standards they produce increase. Sometimes, also, the lack of coordination among the committees in charge of the development of standards may lead to overlaps, mistakes or incompatibilities in the documents. The aim of this study is to present a methodology enabling an automatic extraction of the technical concepts (terms) found in normative documents, through the use of semantic tools coming from the field of language processing. The first part of the paper provides a description of the standardization world, its structure, its way of working and the problems faced; we then introduce the concepts of semantic annotation, information extraction and the software tools available in this domain. The next section explains the concept of ontology and its potential use in the field of standardization. We propose here a methodology enabling the extraction of technical information from a given normative corpus, based on a semantic annotation process done according to reference ontologies. The application to the ISO 15531 MANDATE corpus provides a first use case of the methodology described in this paper. The paper ends with the description of the first experimental results produced by this approach, and with some issues and perspectives, notably its application to other standards and, or Technical Committees and the possibility offered to create pre-defined technical dictionaries of terms.

In this paper, we present an embedding-based framework (TrQuery) for recommending solutions of a SPARQL query, including approximate solutions when exact querying solutions are not available due to incompleteness or inconsistencies of real-world RDF data. Within this framework, embedding is applied to score solutions together with edit distance so that we could obtain more fine-grained recommendations than those recommendations via edit distance. For instance, graphs of two querying solutions with a similar structure can be distinguished in our proposed framework while the edit distance depending on structural difference becomes unable. To this end, we propose a novel score model built on vector space generated in embedding system to compute the similarity between an approximate subgraph matching and a whole graph matching. Finally, we evaluate our approach on large RDF datasets DBpedia and YAGO, and experimental results show that TrQuery exhibits an excellent behavior in terms of both effectiveness and efficiency.

The main finding, that adding additional techniques for summarization for a combined approach can produce improved results, is unsurprising. I don't see any link to software on github, etc., which makes the article hard to follow up on.