The Shapes Constraint Language (SHACL) is a recent W3C recommendation language for validating RDF data. Specifically, SHACL documents are collections of constraints that enforce particular shapes on an RDF graph. Previous work on the topic has provided theoretical and practical results for the validation problem, but did not consider the standard decision problems of satisfiability and containment, which are crucial for verifying the feasibility of the constraints and important for design and optimization purposes. In this paper, we undertake a thorough study of different features of non-recursive SHACL by providing a translation to a new first-order language, called SCL, that precisely captures the semantics of SHACL w.r.t. satisfiability and containment. We study the interaction of SHACL features in this logic and provide the detailed map of decidability and complexity results of the aforementioned decision problems for different SHACL sublanguages. Notably, we prove that both problems are undecidable for the full language, but we present decidable combinations of interesting features.

MOTIVATION: Recognizing named entities (NER) and their associated attributes like negation are core tasks in natural language processing. However, manually labeling data for entity tasks is time consuming and expensive, creating barriers to using machine learning in new medical applications. Weakly supervised learning, which automatically builds imperfect training sets from low cost, less accurate labeling rules, offers a potential solution. Medical ontologies are compelling sources for generating labels, however combining multiple ontologies without ground truth data creates challenges due to label noise introduced by conflicting entity definitions. Key questions remain on the extent to which weakly supervised entity classification can be automated using ontologies, or how much additional task-specific rule engineering is required for state-of-the-art performance.

This paper investigates the feasibility of automated reasoning over temporal DL-Lite (TDL-Lite) knowledge bases (KBs). We test the usage of off-the-shelf LTL reasoners to check satisfiability of TDL-Lite KBs. In particular, we test the robustness and the scalability of reasoners when dealing with TDL-Lite TBoxes paired with a temporal ABox. We conduct various experiments to analyse the performance of different reasoners by randomly generating TDL-Lite KBs and then measuring the running time and the size of the translations. Furthermore, in an effort to make the usage of TDL-Lite KBs a reality, we present a fully fledged tool with a graphical interface to design them. Our interface is based on conceptual modelling principles and it is integrated with our translation tool and a temporal reasoner.

Wikidata constraints, albeit useful, are represented and processed in an incomplete, ad hoc fashion. Constraint declarations do not fully express their meaning, and thus do not provide a precise, unambiguous basis for constraint specification, or a logical foundation for constraint-checking implementations. In prior work we have proposed a logical framework for Wikidata as a whole, based on multi-attributed relational structures (MARS) and related logical languages. In this paper we explain how constraints are handled in the proposed framework, and show that nearly all of Wikidata's existing property constraints can be completely characterized in it, in a natural and economical fashion. We also give characterizations for several proposed property constraints, and show that a variety of non-property constraints can be handled in the same framework.

Multi-attributed relational structures (MARSs) have been proposed as a formal data model for generalized property graphs, along with multi-attributed rule-based predicate logic (MARPL) as a useful rule-based logic in which to write inference rules over property graphs. Wikidata can be modelled in an extended MARS that adds the (imprecise) datatypes of Wikidata. The rules of inference for the Wikidata ontology can be modelled as a MARPL ontology, with extensions to handle the Wikidata datatypes and functions over these datatypes. Because many Wikidata qualifiers should participate in most inference rules in Wikidata a method of implicitly handling qualifier values on a per-qualifier basis is needed to make this modelling useful. The meaning of Wikidata is then the extended MARS that is the closure of running these rules on the Wikidata data model. Wikidata constraints can be modelled as multi-attributed predicate logic (MAPL) formulae, again extended with datatypes, that are evaluated over this extended MARS.

Here we understand 'intelligence' as referring to items of knowledge collected for the sake of assessing and maintaining national security. The intelligence community (IC) of the United States (US) is a community of organizations that collaborate in collecting and processing intelligence for the US. The IC relies on human-machine-based analytic strategies that 1) access and integrate vast amounts of information from disparate sources, 2) continuously process this information, so that, 3) a maximally comprehensive understanding of world actors and their behaviors can be developed and updated. Herein we describe an approach to utilizing outcomes-based learning (OBL) to support these efforts that is based on an ontology of the cognitive processes performed by intelligence analysts.

In a recruitment industry, selecting a best CV from a particular job post within a pile of thousand CV's is quite challenging. Finding a perfect candidate for an organization who can be fit to work within organizational culture is a difficult task. In order to help the recruiters to fill these gaps we leverage the help of AI. We propose a methodology to solve these problems by matching the skill graph generated from CV and Job Post. In this report our approach is to perform the business understanding in order to justify why such problems arise and how we intend to solve these problems using natural language processing and machine learning techniques. We limit our project only to solve the problem in the domain of the computer science industry.

In this paper, we study the problem of learning continuous vector representations of knowledge graphs for predicting missing links. We present a new approach called ConEx, which infers missing links by leveraging the composition of a 2D convolution with a Hermitian inner product of complex-valued embedding vectors. We evaluate ConEx against state-of-the-art approaches on the WN18RR, FB15K-237, KINSHIP and UMLS benchmark datasets. Our experimental results show that ConEx achieves a performance superior to that of state-of-the-art approaches such as RotatE, QuatE and TuckER on the link prediction task on all datasets while requiring at least 8 times fewer parameters. We ensure the reproducibility of our results by providing an open-source implementation which includes the training, evaluation scripts along with pre-trained models at https://github.com/conex-kge/ConEx.

In the abundance of information, both machines and human researchers need tools to navigate and process it. Structuring and formalization of data into hierarchies, such as trees, may establish the relations between the data required for efficient machine processing and may make the information more readable for data analysts. Yet, in more complex domains, such as in natural language processing, relations between concepts go beyond simple hierarchies and form thesaurus-like networks. For such cases, researchers use ontologies as common vocabularies for specialists who need to share information in a domain. Ontologies were first defined as "explicit formal specifications of the terms in the domain and relations among them" (Gruber 1993) and, more specifically, "a formal, explicit specification of a shared conceptualization" (Studer et al. 1998) and are used in a number of applications, including the following, as specified by Noy and McGuinness (Noy and McGuinness 2001): Ontologies are the tools to provide comprehensive description of the domain of interest with respect to the users' needs It is something that we see when, for example, medical information is published on, several different websites.

Features mined from knowledge graphs are widely used within multiple knowledge discovery tasks such as classification or fact-checking. Here, we consider a given set of vertices, called seed vertices, and focus on mining their associated neighboring vertices, paths, and, more generally, path patterns that involve classes of ontologies linked with knowledge graphs. Due to the combinatorial nature and the increasing size of real-world knowledge graphs, the task of mining these patterns immediately entails scalability issues. In this paper, we address these issues by proposing a pattern mining approach that relies on a set of constraints (e.g., support or degree thresholds) and the monotonicity property. As our motivation comes from the mining of real-world knowledge graphs, we illustrate our approach with PGxLOD, a biomedical knowledge graph.