Verb Physics: Relative Physical Knowledge of Actions and Objects

Learning commonsense knowledge from natural language text is nontrivial due to reporting bias: people rarely state the obvious, e.g., "My house is bigger than me." However, while rarely stated explicitly, this trivial everyday knowledge does influence the way people talk about the world, which provides indirect clues to reason about the world. For example, a statement like, "Tyler entered his house" implies that his house is bigger than Tyler. In this paper, we present an approach to infer relative physical knowledge of actions and objects along five dimensions (e.g., size, weight, and strength) from unstructured natural language text. We frame knowledge acquisition as joint inference over two closely related problems: learning (1) relative physical knowledge of object pairs and (2) physical implications of actions when applied to those object pairs. Empirical results demonstrate that it is possible to extract knowledge of actions and objects from language and that joint inference over different types of knowledge improves performance.Comment: 11 pages, published in Proceedings of ACL 201

An introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them

A Practical Blended Analysis for Dynamic Features in JavaScript

The JavaScript Blended Analysis Framework is designed to perform a general-purpose, practical combined static/dynamic analysis of JavaScript programs, while handling dynamic features such as run-time generated code and variadic func- tions. The idea of blended analysis is to focus static anal- ysis on a dynamic calling structure collected at runtime in a lightweight manner, and to rene the static analysis us- ing additional dynamic information. We perform blended points-to analysis of JavaScript with our framework and compare results with those computed by a pure static points- to analysis. Using JavaScript codes from actual webpages as benchmarks, we show that optimized blended analysis for JavaScript obtains good coverage (86.6% on average per website) of the pure static analysis solution and nds ad- ditional points-to pairs (7.0% on average per website) con- tributed by dynamically generated/loaded code

Simplification of UML/OCL schemas for efficient reasoning

Ensuring the correctness of a conceptual schema is an essential task in order to avoid the propagation of errors during software development. The kind of reasoning required to perform such task is known to be exponential for UML class diagrams alone and even harder when considering OCL constraints. Motivated by this issue, we propose an innovative method aimed at removing constraints and other UML elements of the schema to obtain a simplified one that preserve the same reasoning outcomes. In this way, we can reason about the correctness of the initial artifact by reasoning on a simplified version of it. Thus, the efficiency of the reasoning process is significantly improved. In addition, since our method is independent from the reasoning engine used, any reasoning method may benefit from it.Peer ReviewedPostprint (author's final draft

Ontology-based model abstraction

In recent years, there has been a growth in the use of reference conceptual models to capture information about complex and critical domains. However, as the complexity of domain increases, so does the size and complexity of the models that represent them. Over the years, different techniques for complexity management in large conceptual models have been developed. In particular, several authors have proposed different techniques for model abstraction. In this paper, we leverage on the ontologically well-founded semantics of the modeling language OntoUML to propose a novel approach for model abstraction in conceptual models. We provide a precise definition for a set of Graph-Rewriting rules that can automatically produce much-reduced versions of OntoUML models that concentrate the models’ information content around the ontologically essential types in that domain, i.e., the so-called Kinds. The approach has been implemented using a model-based editor and tested over a repository of OntoUML models

Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning

Reasoning is essential for the development of large knowledge graphs, especially for completion, which aims to infer new triples based on existing ones. Both rules and embeddings can be used for knowledge graph reasoning and they have their own advantages and difficulties. Rule-based reasoning is accurate and explainable but rule learning with searching over the graph always suffers from efficiency due to huge search space. Embedding-based reasoning is more scalable and efficient as the reasoning is conducted via computation between embeddings, but it has difficulty learning good representations for sparse entities because a good embedding relies heavily on data richness. Based on this observation, in this paper we explore how embedding and rule learning can be combined together and complement each other's difficulties with their advantages. We propose a novel framework IterE iteratively learning embeddings and rules, in which rules are learned from embeddings with proper pruning strategy and embeddings are learned from existing triples and new triples inferred by rules. Evaluations on embedding qualities of IterE show that rules help improve the quality of sparse entity embeddings and their link prediction results. We also evaluate the efficiency of rule learning and quality of rules from IterE compared with AMIE+, showing that IterE is capable of generating high quality rules more efficiently. Experiments show that iteratively learning embeddings and rules benefit each other during learning and prediction.Comment: This paper is accepted by WWW'1

iStarJSON : a lightweight data-format for i* models

JSON is one of the most widely used data-interchange format. There is a large number of tools open for modelling with i*. However, none of them provides supporting for JSON. In this paper we propose iStarJSON language, a JSON-based proposal for interchanging i* models. We also, present an open source software that transforms XML-based format models to JSON models that expose a set of web services for mining iStarJSON models.Peer ReviewedPostprint (author's final draft