Knowledge graph embedding represents entities and relations in knowledge graph as low-dimensional, continuous vectors, and thus enables knowledge graph compatible with machine learning models. Though there have been a variety of models for knowledge graph embedding, most methods merely concentrate on the fact triples, while supplementary textual descriptions of entities and relations have not b...

Representing Arabic Text semantically using Rich Semantic Graph (RSG) is one of the recent techniques that facilitate the process of manipulating the Arabic Language in Natural Language Processing (NLP) field. The work presented in this paper is a part of an ongoing research to create an abstractive summary for a single input document in Arabic Language. The abstractive summary is generated thr...

In this paper we describe our progress towards building an Interlingua based machine translation system, by capturing the semantics of the source language sentences in the form of Universal Networking Language (UNL) graphs from which the target language sentences can be produced. There are two stages to the UNL graph generation: first, the conceptual arguments of a situation are identified in t...

This paper describes preprocessor and whitespace-aware tools for parsing and transforming Erlang source code. The presented tools are part of RefactorErl, a refactoring tool for Erlang programs. RefactorErl represents programs as a ”semantic graph” that extends the AST with semantic nodes and edges for efficient information retrieval. The paper focuses on describing the construction of the AST ...

A distributed graph processing system that provides locality control, indexing, graph query, and parallel processing capabilities is presented. Keywords—graph, distributed, semantic, query, analytics

Although pictorial renditions of statistical data are ubiquitous, few techniques and standards exist to exchange, search and query these graphical representations. We present several improvements to human-graph interaction including i) a new approach to manage statistical graph knowledge by semantic annotation of graphs that bridges the gap between Web 2.0 social tagging and formal, logic-based...

We propose a graph cut based automatic method for prostate segmentation using image features, context information and semantic knowledge. A volume of interest (VOI) is first identified using supervoxel oversegmentation and their subsequent classification. All voxels within the VOI are labeled prostate or background using graph cuts. Semantic information obtained from Random forest (RF) classifi...

Parsing Chinese serial verb sentences is a key issue in NLP. Many controversies arise from serial verb sentences. This paper puts forward a novel model “the Feature Structure theory” to resolve the semantic labeling of Chinese serial verb sentences. We analyze the difficulties in annotating these sentences, and compare Feature Structure with traditional dependency structure. Feature Structure r...

VisPro is a general-purpose visual language generation system, which can produce a wide range of diagrammatic visual programming languages (VPLs) based on Reserved Graph Grammar (RGG), a context sensitive graph grammar. This paper presents an approach to specify the semantic execution sequence of VPLs based on VisPro. In this approach, we use an ordering mechanism to facilitate the parsing form...

In this paper, we present a computational method for transforming a syntactic graph, which represents all syntactic interpretations of a sentence, into a semantic graph which filters out certain interpretations, but also incorporates any remaining ambiguities. We argue that the resulting ambiguous graph, supported by an exclusion matrix, is a useful data structure for question answering and oth...