A universal relation data model with semantic abstractions

Two important features of modern database models are support for complex data structures and support for high-level data retrieval and update. The first issue has been studied by the development of various semantic data models; the second issue has been studied through universal relation data models. How the advantages of these two approaches can be combined is presently examined. A new data model that incorporates standard concepts from semantic data models such as entities, aggregations, and ISA hierarchies is introduced. It is then shown how nonnavigational queries and updates can be interpreted in this model. The main contribution is to demonstrate how universal relation techniques can be extended to a more powerful data model. Moreover, the semantic constructs of the model allow one to eliminate many of the limitations of previous universal relation models     

A semantic matching energy function for learning with multi-relational data

Large-scale relational learning becomes crucial for handling the huge amounts of structured data generated daily in many application domains ranging from computational biology or information retrieval, to natural language processing. In this paper, we present a new neural network architecture designed to embed multi-relational graphs into a flexible continuous vector space in which the original data is kept and enhanced. The network is trained to encode the semantics of these graphs in order to assign high probabilities to plausible components. We empirically show that it reaches competitive performance in link prediction on standard datasets from the literature as well as on data from a real-world knowledge base (WordNet). In addition, we present how our method can be applied to perform word-sense disambiguation in a context of open-text semantic parsing, where the goal is to learn to assign a structured meaning representation to almost any sentence of free text, demonstrating that it can scale up to tens of thousands of nodes and thousands of types of relation.     

A new multi-view learning machine with incomplete data

Pattern Analysis and Applications - Multi-view learning with incomplete views (MVL-IV) is a reliable algorithm to process incomplete datasets which consist of instances with missing views or...     

A semantic framework for textual data enrichment

In this work we present a semantic framework suitable of being used as support tool for recommender systems. Our purpose is to use the semantic information provided by a set of integrated resources to enrich texts by conducting different NLP tasks: WSD, domain classification, semantic similarities and sentiment analysis. After obtaining the textual semantic enrichment we would be able to recommend similar content or even to rate texts according to different dimensions. First of all, we describe the main characteristics of the semantic integrated resources with an exhaustive evaluation. Next, we demonstrate the usefulness of our resource in different NLP tasks and campaigns. Moreover, we present a combination of different NLP approaches that provide enough knowledge for being used as support tool for recommender systems. Finally, we illustrate a case of study with information related to movies and TV series to demonstrate that our framework works properly.     

A unified multimedia and semantic perspective for data retrieval in the semantic web

In recent years, the emerging diffusion of peer-to-peer networks is going beyond the single-domain paradigm like, for instance, the mono-thematic file sharing one (e.g. Napster for music). Peers are more and more heterogeneous data sources which need to share data with commercial, educational, and/or collaboration purposes, just to mention a few. Moreover, in current information processing applications data cannot be meaningfully searched by precise database queries that would return exact matches (e.g. when dealing with multimedia, proteomic, statistical data).     

A parametric model for fusing heterogeneous fuzzy data

Presented is a model that integrates three data types (numbers, intervals, and linguistic assessments). Data of these three types come from a variety of sensors. One objective of sensor-fusion models is to provide a common framework for data integration, processing, and interpretation. That is what our model does. We use a small set of artificial data to illustrate how problems as diverse as feature analysis, clustering, cluster validity, and prototype classifier design can all be formulated and attacked with standard methods once the data are converted to the generalized coordinates of our model. The effects of reparameterization on computational outputs are discussed. Numerical examples illustrate that the proposed model affords a natural way to approach problems which involve mixed data types     

Intimate machine interaction

It is argued that current digital media offer exciting new opportunities for experimental work based on sharing responsibilities and tasks between man and machine. The artist can get a better in-deph understanding of his individual exploratory behaviour through navigating in behaviour-rich problem domains. Interactive methods are instrumental to introspection and flexible enough to control and explore many different types of dynamic systems.     

A semantic enriched data model for sensor network interoperability

One of the main open issues in the development of applications for sensor network management is the definition of interoperability mechanisms among the several monitoring systems and heterogeneous data. Interesting researches related to integration techniques have taken place, they are primary based on the adoption of sharing data mechanisms. In the last years, the Service-Oriented Architecture (SOA) approach has become predominant in many sensor network projects as it enables the cooperation and interoperability of different sensor platforms at an higher level of abstraction. In this paper we propose a novel architecture for the interoperability of sensor networks, which is based on web services technologies and on a common data model enriched with semantic concepts and annotations. The proposed architecture allows the development of complex decision support system applications by integration of heterogeneous data, accessible through services, according to standard data format and standard protocols.     

A semantic approach to improving machine readability of a large-scale attack graph

Automation in cyber security can be achieved by using attack graphs. Attack graphs allow us to model possible paths that a potential attacker can use to intrude into a target network. In particular, graph representation is often used to increase visibility of information, but it is not effective when a large-scale attack graph is produced. However, it is inevitable that such a voluminous attack graph is generated by modeling a variety of data from an increasing number of network hosts. Therefore, we need more intelligent ways of inferring the knowledge required to harden network security from the attack graph, beyond getting information such as possible attack paths. Ontology technology enables a machine to understand information and makes it easier to infer knowledge based on relational facts from big data. Constructing ontology in the domain of attack graph generation is a prerequisite for increasing machine intelligence and implementing an automated process. In this paper, we propose a semantic approach to make a large-scale attack graph machine readable. The approach provides several benefits. First, users can obtain relational facts based on reasoning from a large-scale attack graph, and the semantics of an attack graph can provide intuition to users. In addition, intelligence-based security assessment can be possible using the obtained ontological structures. By improving the machine readability of an attack graph, our approach could lead to automated assessment of network security.

     

Exemplar-based statistical model for semantic parametric design of human body

This paper presents an exemplar-based method to provide intuitive way for users to generate 3D human body shape from semantic parameters. In our approach, human models and their semantic parameters are correlated as a single linear system of equations. When users input a new set of semantic parameters, a new 3D human body will be synthesized from the exemplar human bodies in the database. This approach involves simpler computation compared to non-linear methods while maintaining quality outputs. A semantic parametric design in interactive speed can be implemented easily. Furthermore, a new method is developed to quickly predict whether the parameter values is reasonable or not, with the training models in the human body database. The reconstructed human bodies in this way will all have the same topology (i.e., mesh connectivity), which facilitates the freeform design automation of human-centric products.     

RDBM—A relational data base machine

After a short introduction to the field of data base machines the design of the RDBM (Relational Data Base Machine) is presented, which provides all the functions required of a relational data base system. Frequently used and time-consuming functions are supported by appropriate hardware components. A transaction-oriented multi-user system was designed which exploits the inherent parallelism of user tasks and data base functions.The RDBM consists of a quasi-associative mass store together with a system of special function processors with common access to a large main memory, and a general purpose mini-computer exercising overall control over all hardware components.     

LOD search engine: A semantic search over linked data

Journal of Intelligent Information Systems - In the last few years, there has been a significant growth in the amount of data published in RDF and adoption of Linked Data principles. Every day, a...     

Scalable semantic brokering over dynamic heterogeneous data sources in InfoSleuth/spl trade/

InfoSleuth is an agent-based system for information discovery and retrieval in a dynamic, open environment. Brokering in InfoSleuth is a matchmaking process, recommending agents that provide services to agents requesting services. This paper discusses InfoSleuth's distributed multibroker design and implementation. InfoSleuth's brokering function combines reasoning over both the syntax and semantics of agents in the domain. This means the broker must reason over explicitly advertised information about agent capabilities to determine which agent can best provide the requested services. Robustness and scalability issues dictate that brokering must be distributable across collaborating agents. Our multibroker design is a peer-to-peer system that requires brokers to advertise to and receive advertisements from other brokers. Brokers collaborate during matchmaking to give a collective response to requests initiated by nonbroker agents. This results in a robust, scalable brokering system.     

Multi levels semantic architecture for multimodal interaction

This paper presents a semantic architecture for solving multimodal interaction. Our architecture is based on multi agent systems where agents are purely semantic using ontologies and inference system. Multi levels concepts and behavioural models are taken into account to bring a fast high level reasoning on a big amount of percepts and low level actions. We apply this architecture to make a system aware of different situations in a network like tracking object behaviours of the environment. As a proof of concept, we apply our architecture to an assistant robot helping blind or disabled people to cross a road in a virtual reality environment.     

Cardinality constraints in semantic data models

Constraints are central to the notion of a semantic data model. How well a model captures constraints affects its power and viability as a semantic data model. Cardinality constraints are an important subclass of general constraints. In this paper we provide formal definitions for cardinality constraints of several semantic models, as described in the literature. We construct a partial ordering of these constraints that shows the relative power expressed by each cardinality constraints. We discuss our results and offer possible extensions to contemporary cardinality constraint definitions. Our contributions include a collection and formal definition of existing cardinality constraints, a partial ordering of this set, and recommendations for cardinality constraint mechanisms in semantic data models.