The popularization and quick growth of Linked Open Data (LOD) has led to challenging aspects regarding quality assessment and data exploration of the RDF triples that shape the LOD cloud.Particularly, we are interested in the completeness of data and its potential to provide concept definitions in terms of necessary and sufficient conditions.In this work we propose a novel technique based on Formal Concept Analysis which organizes RDF data into a concept lattice.This allows data exploration as well as the discovery of implications, which are used to automatically detect missing information and then to complete RDF data.Moreover, this is a way of reconciling syntax and semantics in the LOD cloud.Finally, experiments on the DBpedia knowledge base show that the approach is well-founded and effective.

There is a great deal of interest in big data, focusing mostly on data set size. The use of semantics in this integration descriptions and then integrating the data within process is key to building an approach that scales this unified framework. Finally, we conclude by to large numbers of heterogeneous sources. For example, in and (4) integrate the data across sources using this our museum use case, we received data in spreadsheets model. Karma has been used on a variety of types of (figure 1), comma-separated values (CSV), data, including biological data, mobile phone data, JSON (figure 3), XML, and relational databases (figure geospatial data, and cultural heritage data. In order to illustrate the approach to integrating One challenge in integrating diverse data sources is data in Karma, we will use an example from the cultural the ability to import different data formats into a heritage domain.

We describe an approach for identifying fine-grained entity types in heterogeneous data graphs that is effective for unstructured data or when the underlying ontologies or semantic schemas are unknown. Identifying fine-grained entity types, rather than a few high-level types, supports coreference resolution in heterogeneous graphs by reducing the number of possible coreference relations that must be considered. Big data problems that involve integrating data from multiple sources can benefit from our approach when the datas ontologies are unknown, inaccessible or semantically trivial. For such cases, we use supervised machine learning to map entity attributes and relations to a known set of attributes and relations from appropriate background knowledge bases to predict instance entity types. We evaluated this approach in experiments on data from DBpedia, Freebase, and Arnetminer using DBpedia as the background knowledge base.

We discuss the nature of big data and address the role of semantics in analyzing and processing big data that arises in the context of physical-cyber-social systems. To handle volume, we advocate semantic perception that can convert low-level observational data to higher-level abstractions more suitable for decision-making. To handle variety, we resort to semantic models and annotations of data so that intelligent processing can be done independent of heterogeneity of data formats and media. To handle velocity, we seek to use continuous semantics capability to dynamically create event or situation specific models and recognize relevant new concepts, entities and facts. To handle veracity, we explore trust models and approaches to glean trustworthiness. These four v's of big data are harnessed by the semantics-empowered analytics to derive value to support applications transcending physical-cyber-social continuum.

While catchphrases such as big data, smart data, data-intensive science, or smart dust highlight different aspects, they share a common theme: Namely, a shift towards a data-centric perspective in which the synthesis and analysis of data at an ever-increasing spatial, temporal, and thematic resolution promises new insights, while, at the same time, reducing the need for strong domain theories as starting points. In terms of the envisioned methodologies, those catchphrases tend to emphasize the role of predictive analytics, that is, statistical techniques including data mining and machine learning, as well as supercomputing. Interestingly, however, while this perspective takes the availability of data as a given, it does not answer the question how one would discover the required data in today’s chaotic information universe, how one would understand which datasets can be meaningfully integrated, and how to communicate the results to humans and machines alike. The semantic web addresses these questions. In the following, we argue why the data train needs semantic rails. We point out that making sense of data and gaining new insights works best if inductive and deductive techniques go hand-in-hand instead of competing over the prerogative of interpretation.

The design of ontologies for new commonsense domains continues to pose challenges, particularly in cases where multiple potential axiomatizations satisfy the requirements for the ontology. One approach is to specify the requirements with respect to the intended semantics of the terminology; from a mathematical perspective the requirements may be characterized by the class of structures(referred to as the required models) which capturethe intended semantics. This approach leads to a natural notion of the correctness as a relationship between the models of the axiomatization of the ontology and the required models for the ontology. In this paper, we consider three possible generalizations of the notion of the correctness of an ontology in the case in which the ontology and the required models have different signatures.We show that these notions of correctness lead to different approaches for ontology evaluation and discuss the benefits and drawbacks of each approach.

In this extended abstract we examine the principles that underlie the construction of what we call Ontologies in Variation — a human-comprehensible knowledge representation scheme for natural kinds, objects, and concepts that captures both prototypical (or canonical) properties of classes of objects as well as those properties that are in variation. A fundamental characteristic of our work is that the variability captured in our representation is derived from data and as such that the provenance of statistical knowledge — the dataset — is directly associated with the ontology. This reliance on empirical data directs us towards a frequentist view of variation as statistical assertions, in contrast to much of the current work that integrates logic and uncertainty. Our formalism's novelty lies in the strategic complementation of axiomatic knowledge by statistical knowledge, and by the desire to preserve human comprehension of the resulting representation. We illustrate this work in the context of an ongoing project to create a representation of human anatomy — a queryable digital anatomy book that fits all of us in some variation.

Systems that build general knowledge bases from concept definitions mostly focus on knowledge extraction techniques on a per-definition basis. But, definitions rely on subtext and other definitions to concisely encode a concept's meaning. We present a probabilistic inference process where we systematically augment knowledge extracted from several WordNet glosses with subtext and then infer likely states of the world. From those states we learn new semantic relations among properties, states, and events. We show that our system learns more relations than one without subtext and verify this knowledge using human evaluators.

The representation of dates and their relationship to time and duration has long been recognized as an important problem in commonsense reasoning. However, existing date ontologies, such as OWL-Time and Date-Time Foundation Vocabulary from the Object Modeling Group, take either over-simplistic or convoluted approaches to defining the key semantics for dates. We show that such approaches are inadequate and provide an improved solution: a first-order Date Ontology that is an extension of the Process Specification Language and an existing duration ontology. Rather than treat dates as a class of timepoints, we axiomatize dates as a class of complex activities which have multiple periodic occurrences. We consider two modules of the Date Ontology, and characterize the models of the Date Ontology up to elementary equivalence.

This paper describes an end-to-end learning framework that allows a novice to create a model from data easily by helping structure the model building process and capturing extended aspects of domain knowledge. By treating the whole modeling process interactively and exploiting high-level knowledge in the form of an ontology, the framework is able to aid the user in a number of ways, including in helping to avoid pitfalls such as data dredging. Prudence must be exercised to avoid these hazards: certain conclusions may be supported by extra knowledge if, for example, there are reasons to trust a particular narrower set of hypotheses. This paper adopts the solution of using higher-level knowledge in order to allow this sort of domain knowledge to be inferred automatically, thereby selecting only relevant input attributes and thence constraining the hypothesis space. We describe how the framework automatically exploits structured knowledge in an ontology to identify relevant concepts, and how a data extraction component can make use of online data sources to find measurements of those concepts so that their relevance can be evaluated. To validate our approach, models of four different problem domains were built using our implementation of the framework. Prediction error on unseen examples of these models show that our framework, making use of the ontology, helps to improve model generalization.