Fuzzy Description Logics (DLs) are used to represent and reason about vague and imprecise knowledge that is inherent to many application domains. It was recently shown that the complexity of reasoning in finitely valued fuzzy DLs is often not higher than that of the underlying classical DL. We show that this does not hold for fuzzy extensions of the light-weight DL EL, which is used in many biomedical ontologies, under the Lukasiewicz semantics. The complexity of reasoning increases from PTime to ExpTime, even if only one additional truth value is introduced. The same lower bound holds also for infinitely valued Lukasiewicz extensions of EL.

In dynamic settings where data is exposed by streams, knowledge discovery aims at learning associations of data across streams. In the semantic Web, streams expose their meaning through evolutive versions of ontologies. Such settings pose challenges of scalability for discovering (a posteriori) knowledge. In our work, the semantics, identifying knowledge similarity and rarity in streams, together with incremental, approximate maintenance, control scalability while preserving accuracy of streams associations (as semantic rules) discovery.

We consider the scenario of ontology-based query answering. It is generally accepted that true scalability in this setting can only be achieved via query rewriting, which in turn allows for the exploitation of standard RDBMSs. In this work, we close two open fundamental questions related to query rewriting. We establish that linear existential rules are polynomially combined rewritable, while full linear rules are polynomially (purely) rewritable; in both cases, the target query language consists of first-order or non-recursive Datalog queries. An immediate consequence of our results is that DLR-Lite_R, the extension of DL-Lite_R with n-ary roles, is polynomially combined rewritable.

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.

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.

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.

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.

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.

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.