Knowledge graphs (KGs) have gradually become valuable assets for many AI applications. In a KG, a node denotes an entity, and an edge (or link) denotes a relationship between the entities represented by the nodes. Knowledge graph completion infers and predicts missing edges in a KG automatically. Knowledge graph embeddings have shed light on addressing this task. Recent research embeds KGs in hyperbolic (negatively curved) space instead of conventional Euclidean (zero curved) space and is effective in capturing hierarchical structures. However, as multi-relational graphs, KGs are not structured uniformly and display intrinsic heterogeneous structures. They usually contain rich types of structures, such as hierarchical and cyclic typed structures. Embedding KGs in single-curvature space, such as Euclidean or hyperbolic space, overlooks the intrinsic heterogeneous structures of KGs, and therefore cannot accurately capture their structures. To address this issue, we propose Mixed-Curvature Multi-Relational Graph Neural Network (M2GNN), a generic approach that embeds multi-relational KGs in a mixed-curvature space for knowledge graph completion. Specifically, we define and construct a mixed-curvature space through a product manifold combining multiple single-curvature spaces (e.g., spherical, hyperbolic, or Euclidean) with the purpose of modeling a variety of structures. However, constructing a mixed-curvature space typically requires manually defining the fixed curvatures, which needs domain knowledge and additional data analysis. Improperly defined curvature space also cannot capture the structures of KGs accurately. To address this problem, we set mixed-curvatures as trainable parameters to better capture the underlying structures of the KGs. Furthermore, we propose a Graph Neural Updater by leveraging the heterogeneous relational context in mixed-curvature space to improve the quality of the embedding. Experiments on three KG datasets demonstrate that the proposed M2GNN can outperform its single geometry counterpart as well as state-of-the-art embedding methods on the KG completion task.

Geospatial data integration combines two or more data layers to facilitate advanced querying, analysis, reasoning, and visualization. In general, different layers (e.g., ZIP codes, census blocks, school districts, and land use parcels) have different spatial partitions and different types of associated semantic descriptors. In addition, geospatial data may contain errors (e.g., due to imprecision in the measurements or to representation constraints) causing uncertainty that needs to be incorporated and quantified in the query answers. In this paper, we leverage semantic descriptors in heterogeneous information layers to build a data structure that enables efficient processing of geospatial range queries by returning an estimate of the answer together with an error bound. We present the processing algorithms and evaluate our approach by means of experiments that encompass large datasets, demonstrating the benefits of our approach.

Faceted interfaces are omnipresent on the web to support data exploration and filtering. A facet is a triple: a domain (e.g., Book), a property (e.g., author, language), and a set of property values (e.g., Austen, Beauvoir, Coelho, Dostoevsky, Eco, Kerouac, Suskind, ..., French, English, German, Italian, Portuguese, Russian, ... ). Given a property (e.g., language), selecting one or more of its values (English and Italian) returns the domain entities (of type Book) that match the given values (the books that are written in English or Italian). To implement faceted interfaces in a way that is scalable to very large datasets, it is necessary to automate facet extraction. Prior work associates a facet domain with a set of homogeneous values, but does not annotate the facet property. In this paper, we annotate the facet property with a predicate from a reference Knowledge Base (KB) so as to maximize the semantic similarity between the property and the predicate. We define semantic similarity in terms of three new metrics: specificity, coverage, and frequency. Our experimental evaluation uses the DBpedia and YAGO KBs and shows that for the facet annotation problem, we obtain better results than a state-of-the-art approach for the annotation of web tables as modified to annotate a set of values.

As the underground infrastructure systems of cities age, maintenance and repair become an increasing concern. Cities face difficulties in planning maintenance, predicting and responding to infrastructure related issues, and in realizing their vision to be a smart city due to their incomplete understanding of the existing state of the infrastructure. Only few cities have accurate and complete digital information on their underground infrastructure (e.g., electricity, water, natural gas) systems, which poses problems to those planning and performing construction projects. To address these issues, we introduce GUIDES as a new data conversion and management framework for urban underground infrastructure systems that enable city administrators, workers, and contractors along with the general public and other users to query digitized and integrated data to make smarter decisions. This demo paper presents the GUIDES architecture and describes two of its central components: (i) mapping of underground infrastructure systems, and (ii) integration of heterogeneous geospatial data.

This paper describes Triplex-ST, a novel information extraction system for collecting spatio-temporal information from textual resources. Triplex-ST is based on a distantly supervised approach, which leverages rich linguistic annotations together with information in existing knowledge bases. In particular, we leverage triples associated with temporal and/or spatial contexts, e.g., as available from the YAGO knowledge base, so as to infer templates that capture new facts from previously unseen sentences.

The papers in this special section were presented at the 30th IEEE International Conference on Data Engineering (ICDE) took place in Chicago, IL, from March 31 to April 4, 2014. This special section is comprised of the long versions of five exceptional papers selected from the research program.

The availability of a wide variety of geospatial datasets demands new mechanisms to perform their integrated analysis and visualization. In this demo paper, we describe our semantic framework, GIVA, for Geospatial and temporal data Integration, Visualization, and Analytics. Given a geographic region and a time interval, GIVA addresses the problem of accessing simultaneously several datasets and of establishing mappings between the underlying concepts and instances, using automatic methods. These methods must consider several challenges, such as those that arise from heterogeneous formats, lack of metadata, and multiple spatial and temporal data resolutions. A web interface lets users interact with a map and select datasets to be integrated, displaying as a result reports where values pertaining to different datasets are compared, analyzed, and visualized.

When compared to a gold standard, the set of mappings that are generated by an automatic ontology matching process is neither complete nor are the individual mappings always correct. However, given the explosion in the number, size, and complexity of available ontologies, domain experts no longer have the capability to create ontology mappings without considerable effort. We present a solution to this problem that consists of making the ontology matching process interactive so as to incorporate user feedback in the loop. Our approach clusters mappings to identify where user feedback will be most beneficial in reducing the number of user interactions and system iterations. This feedback process has been implemented in the Agreement Maker system and is supported by visual analytic techniques that help users to better understand the matching process. Experimental results using the OAEI benchmarks show the effectiveness of our approach. We will demonstrate how users can interact with the ontology matching process through the Agreement Maker user interface to match real-world ontologies.

This paper introduces the problem of modeling urban transportation systems in a database where certain aspects of the data are probabilistic in nature. The transportation network is composed of multiple modes (e.g., automobile, bus, train, pedestrian) that the user can alternate between. A trip – a path between an origin and destination subject to some constraints – is the central concept. How these trips and the network can be represented as both a graph and relational model, as well as the requirements for querying are the main contributions of this paper. A set of operators are defined to work over these transportation concepts and they are integrated within a SQL-like syntax to express queries over the uncertain transportation network. Additionally, the paper shows how this model can be integrated within other moving objects and spatio-temporal data models, and how these graph-based queries can be processed. 1.

This work addresses the problem of processing continuous nearest neighbor (NN) queries for moving objects trajectories when the exact position of a given object at a particular time instant is not known, but is bounded by an uncertainty region. As has already been observed in the literature, the answers to continuous NN-queries in spatio-temporal settings are time parameterized in the sense that the objects in the answer vary over time. Incorporating uncertainty in the model yields additional attributes that affect the semantics of the answer to this type of queries. In this work, we formalize the impact of uncertainty on the answers to the continuous probabilistic NN-queries, provide a compact structure for their representation and efficient algorithms for constructing that structure. We also identify syntactic constructs for several qualitative variants of continuous probabilistic NN-queries for uncertain trajectories and present efficient algorithms for their processing.

We present the AgreementMaker system for matching real-world schemas and ontologies, which may consist of hundreds or even thousands of concepts. The end users of the system are sophisticated domain experts whose needs have driven the design and implementation of the system: they require a responsive, powerful, and extensible framework to perform, evaluate, and compare matching methods. The system comprises a wide range of matching methods addressing different levels of granularity of the components being matched (conceptual vs. structural), the amount of user intervention that they require (manual vs. automatic), their usage (stand-alone vs. composed), and the types of components to consider (schema only or schema and instances). Performance measurements (recall, precision, and runtime) are supported by the system, along with the weighted combination of the results provided by those methods. The AgreementMaker has been used and tested in practical applications and in the Ontology Alignment Evaluation Initiative (OAEI) competition. We report here on some of its most advanced features, including its extensible architecture that facilitates the integration and performance tuning of a variety of matching methods, its capability to evaluate, compare, and combine matching results, and its user interface with a control panel that drives all the matching methods and evaluation strategies.

In this paper, we follow the role-based access control (RBAC) approach and extend it to provide for the dynamic association of roles with users. In our framework, privileges associated with resources are assigned depending on the attribute values of the resources, attribute values associated with users determine the association of users with privileges, and a location mapping function between physical and logical locations allows to enable/disable roles depending on the logical location of the users and thus preserve the privacy of the location. We use Semantic Web technologies and a graphical user interface based on the Google Maps API.

This work tackles the problem of answer-aggregation for continuous spatio-temporal range queries in distributed settings. We assume a grid-like coverage of the spatial universe of discourse, in which each cell is governed by a Base Station (BS) that communicates with the mobile users in its zone, and is also equipped with a server that has Moving Objects Database (MOD) capabilities. The MOD server stores the data for the moving objects in a given cell, processes the continuous queries pertaining to that cell, and is connected to the MOD servers in the neighboring cells. We demonstrate that, when a range query that spans over more than one cell needs to have its answer computed for a user located in a particular cell, by intelligently combining the transmission and the aggregation of the partial results, substantial improvements can be achieved at the global level. Towards this end, we present the BORA (Bresenham-based Overlay for Routing and Aggregation) tree, which is used to combine the transmission and local data aggregation along the routes to the destination of the query's answer.

In a real-world decision support application, users often want to search data from various sources according to some criteria, build a visualization based on the data being retrieved, and use the visualization to explore the data. With our approach, these activities are supported within the same workspace. Views are dynamically created by binding each data set to a visualization template according to the user's preferences. The resulting views are then arranged into a larger coordinated view. In our layered architecture, data flows through the layers becoming encapsulated inside of metadata that describes the visual attributes being added. This metadata determines both the individual views and the dynamic interactions within a coordinated view. Dynamic interactions are implemented using a mediated notification services architecture.

This paper describes a Web-based query system for semantically heterogeneous geospatial data. Although Web-based information systems are currently being developed by the GIS community to provide data discovery and download capabilities for distributed Web data sets, they do not include the ability to pose DBMS type queries over the data. We developed a system that provides DBMS querying and that also resolves semantic differences that occur in distributed sources. We are working in the context of a proposed statewide land information system.

In this paper, we further develop a proposed layered approach for the semantic Web. Our objective is to build a specific solution to the problem of providing data interoperability among different databases, so as to allow for schematic data integration. In particular, we solve the problem of translating queries on a database schema into queries on another database schema, using their relationship with an ontology. We use RDF schema to model the databases and the ontology. A common vocabulary expresses the mappings between each database schema and the ontology.

This paper describes our experiences with the Webware, Interfaces and Networking Experimental (WINE) Laboratory. The WINE Lab was created to assist in teaching the topics of computer networks, user interfaces and webware. The goal of the lab is to provide students with the opportunity to complete projects, experiment with relevant techniques and make connections between topics with resources not available in a general purpose Unix-based computing environment. The results from offering courses with the lab show success in meeting these goals. 1 Introduction The paper describes our experiences with the Webware, Interfaces and Networking Experimental Laboratory (WINE Lab) here at Worcester Polytechnic Institute (WPI). This lab was developed to assist in teaching the topics of computer networks, user interfaces and webware. The objective in developing the WINE Lab was to improve these three courses by providing students with the opportunity to complete projects, experiment with relevant...

The Delaunay/sup MM/ system supports an interactive, customizable interface for querying multimedia distributed databases, such as digital libraries. Through this interface, users select virtual document styles that cater the display of query results to their needs, while also offering transparent preand post-query refinement and querying. Delaunay/sup MM/'s virtual documents preserve context by maintaining a single customizable interface for result viewing. The advanced transparent query features rely on mediation to provide adept access to information. In this paper, we present the framework for Delaunay/sup MM/, its architecture, the user interface, and results of the first usability study.

Sophisticated applications and software development on the Web demand an extensive and thorough understanding of a variety of computer science disciplines, as well as providing their own set of issues. Therefore, we have created an advanced undergraduate computer science course called Webware: Computational Technology for Network Information Systems that builds upon and extends knowledge previously gathered by the students. We describe its contents, our teaching experience, and address the challenges of teaching both the foundations and current technological issues of Web programming.

Facilitating information retrieval in the vastly growing realm of digital media has become increasingly difficult. DelaunayMM seeks to assist all users in finding relevant information through an interactive interface that supports pre- and post-query refinement, and a customizable multimedia information display. This project leverages the strengths of visual query languages with a resourceful framework to provide users with a single intuitive interface. The interface and its supporting framework are described in this paper.

Multimedia data has become readily available from a variety of resources, such as the Web, to users (ranging from naive to sophisticated) who need to select and to present the data in a way that is meaningful to their particular applications.Delaunay"" is our framework for querying and presenting multimedia data stored in distributed data repositories, including the Web.It is unique in combining user-defined layouts with ad hoc querying capabilities, thereby enabling users to tailor, in a simple way, the layout of virtual documents composed of retrieved mdimedia objects.In this paper, we focus on the object-oriented data models, on the declarative query languages, and on how the results of the queries to disparate resources are integrated to form coherent user-defined documents.

Visual query systems have traditionally supported a set of pre-defined visual displays. We describe the Delaunay system, which supports visualizations of object-oriented databases specified by the user with a visual constraint-based query language. The highlights of our approach are the expressiveness of the visual query language, the efficiency of the query engine, and the overall flexibility and extensibility of the framework. The user interface is implemented using Java and is available on the WWW.

No abstract available.

In this paper we introduce DOODLE, a new visual and declarative language for object-oriented databases. The main principle behind the language is that it is possible to display and query the database with arbitrary pictures. We allow the user to tailor the display of the data to suit the application at hand or her preferences. We want the user-defined visualizations to be stored in the database, and the language to express all kinds of visual manipulations. For extendibility reasons, the language is object-oriented. The semantics of the language is given by a well-known deductive query language for object-oriented databases. We hope that the formal basis of our language will contribute to the theoretical study of database visualizations and visual query languages, a subject that we believe is of great interest, but largely left unexplored.

The aggregative closure operator is defined and its usefulness is demonstrated in a wide variety of applications. The concepts and definitions of closed semirings and the aggregating relational operators provide a mathematical framework for the presentation of algorithms for these applications. A novel algorithm is also presented which is intended for the computation of the aggregate closure. All of these algorithms but the last are generalizations of existing algorithms intended for transitive closure.>

We define a language G for querying data represented as a labeled graph G. By considering G as a relation, this graphical query language can be viewed as a relational query language, and its expressive power can be compared to that of other relational query languages. We do not propose G as an alternative to general purpose relational query languages, but rather as a complementary language in which recursive queries are simple to formulate. The user is aided in this formulation by means of a graphical interface. The provision of regular expressions in G allows recursive queries more general than transitive closure to be posed, although the language is not as powerful as those based on function-free Horn clauses. However, we hope to be able to exploit well-known graph algorithms in evaluating recursive queries efficiently, a topic which has received widespread attention recently.