Learning New Facts From Knowledge Bases With Neural Tensor Networks and Semantic Word Vectors

Knowledge bases provide applications with the benefit of easily accessible, systematic relational knowledge but often suffer in practice from their incompleteness and lack of knowledge of new entities and relations. Much work has focused on building or extending them by finding patterns in large unannotated text corpora. In contrast, here we mainly aim to complete a knowledge base by predicting additional true relationships between entities, based on generalizations that can be discerned in the given knowledgebase. We introduce a neural tensor network (NTN) model which predicts new relationship entries that can be added to the database. This model can be improved by initializing entity representations with word vectors learned in an unsupervised fashion from text, and when doing this, existing relations can even be queried for entities that were not present in the database. Our model generalizes and outperforms existing models for this problem, and can classify unseen relationships in WordNet with an accuracy of 75.8%

Enterprise Documentation: A Formal-Model Approach

Most digital documents within an enterprise reside outside traditional databases. Search engines have been used to retrieve these digital documents but the results are often unsatisfactory. Digital libraries yield search results with greater precision than general search engines and are capable of compartmentalizing a wide variety of digital documents. However, digital libraries cannot subtly control application-specific document retrievals because they lack a formal model for recognizing the intrinsic relationships among digital documents. This research proposes a formal model, the Entity-Oriented Enterprise Documentation Model (EOEDM), to facilitate document retrievals for enterprise applications. This EOEDM is a meta-level modeling schema, exploiting the entity-relationship model in database design as well as the object model in application design. Since enterprise documents usually result from business activities, the proposed model targets the enterprise documentation needs and relies on the patterns of business activities to associate and locate digital documents with higher precision and better recall

RelBAC: Relation Based Access Control

TheWeb 2.0, GRID applications and, more recently, semantic desktop applications are bringing the Web to a situation where more and more data and metadata are shared and made available to large user groups. In this context, metadata may be tags or complex graph structures such as file system or web directories, or (lightweight) ontologies. In turn, users can themselves be tagged by certain properties, and can be organized in complex directory structures, very much in the same way as data. Things are further complicated by the highly unpredictable and autonomous dynamics of data, users, permissions and access control rules. In this paper we propose a new access control model and a logic, called RelBAC (for Relation Based Access Control) which allows us to deal with this novel scenario. The key idea, which differentiates RelBAC from the state of the art, e.g., Role Based Access Control (RBAC), is that permissions are modeled as relations between users and data, while access control rules are their instantiations on specific sets of users and objects. As such, access control rules are assigned an arity which allows a fine tuning of which users can access which data, and can evolve independently, according to the desires of the policy manager(s). Furthermore, the formalization of the RelBAC model as an Entity-Relationship (ER) model allows for its direct translation into Description Logics (DL). In turn, this allows us to reason, possibly at run time, about access control policies

Initial report on Object Spreadsheets

There is a growing demand for data-driven web applications that help automate organizational and business processes of low to medium complexity by letting users view and update structured data in controlled ways. We present Object Spreadsheets, an end-user development tool that combines a spreadsheet interface with a rich data model to help the process administrators build the logic for such applications themselves. Its all-in-one interface with immediate feedback has the potential to bring more complex tasks within reach of end-user developers, compared to existing approaches. Our data model is based on the structure of entity-relationship models and directly supports nested variable-size collections and object references, which are common in web applications but poorly accommodated by traditional spreadsheets. Object Spreadsheets has a formula language suited to the data model and supports stored procedures to specify the forms of updates that application users may make. Formulas can be used to assemble data in the exact structure in which it is to be shown in the application UI, simplifying the task of UI building; we intend for Object Spreadsheets to be integrated with a UI builder to provide a complete solution for application development. We describe our prototype implementation and several example applications we built to demonstrate the applicability of the tool

ANALYTICS DRIVEN DATA MODEL IN DIGITAL SERVICES

Data models are generally applied to construct consolidated abstraction of various rich and different domains of data. In this paper, we focus on the digital services domain in particularly customer related data model and its structure in helping to shape the analytics capabilities. The traditional Entity Relationship Diagram (ERD) is used as the cornerstone of the strategy and further elaboration is made through abstraction to encompass areas in the digital services. A data model is developed to cover both static aspects (customers’ profile) and dynamic aspects (customers’ behaviour). The foundation of the customer aspect is constructed in classes that represent different types of customer touch points represented as digital footprint which analogize physical activities. The customer dynamic aspects of digital service are modeled with a group of classes where priority is embodied in different associations involving creation and termination of the identified interaction. The suggested data model can be deployed in development of frameworks for customer related applications and enhancement of analytics capabilities.Keywords: Data model, Analytics, Digital service