Useful hypertext is constrained by the need for users to be able to nd documents about similar topics without extensive navigation. We show how examining the properties of a graph built by a document's hypertext links can be used to evaluate the usefulness of the document. To formally measure the quality of hypertext linking in a corpus, we compare the semantic similarity of pairs of documents ...

We think of Match as an operator which takes two graph-like structures and produces a mapping between semantically related nodes. The matching process is essentially divided into two steps: element level and structure level. Element level matchers consider only labels of nodes, while structure level matchers start from this information to consider the full graph. In this paper we present variou...

This paper presents a literature based discovery (LBD) implementation that uses Lucene for indexing, the Semantic Vectors (SV) package for latent semantic analysis, Neo4j for graph database storage, Gephi for visual representation along with custom code written by the author. The approach of using a latent semantic analysis based systems like SV to do LBD is not new, but going the next steps of...

In this paper we formulate the task of semantic image segmentation as a manifold embedding problem and solve it using graph Laplacian approximation. This allows for unsupervised learning of graph Laplacian parameters individually for each image without using any prior information. We perform experiments on GrabCut, Graz and Pascal datasets. At a low computational cost proposed learning method s...

We introduce semantic flow graph (SFG), a novel graph representation and interaction framework that enables users to explore the relationships among key objects (i.e., streamlines, critical points, and spatial regions) of a 3D flow field. The objects and their relationships are organized as a heterogeneous network. We assign each object a set of attributes, based on which a semantic abstraction...

In this work, we propose a novel uncertainty-aware object detection framework with structured-graph, where nodes and edges are denoted by objects their spatial-semantic similarities, respectively. Specifically, aim to consider relationships among for effectively contextualizing them. To achieve this, first detect then measure semantic spatial distances construct an graph, which is represented g...

In this paper, we introduce a model analysis approach suitable for multiple analysis purposes. The approach is based on generic graph pattern matching and semantic standardization of model elements. Due to its generic nature, it is applicable to conceptual models of any graph-based modelling technique. Through semantic standardization, the approach relies on unambiguous model contents. Therefor...

With new standards like RDF or OWL paving the way for the much anticipated semantic web, a new breed of large scale semantic systems is about to appear. Even if research on semantic reconciliation methods is abundant, it is not clear how interoperable very large scale semantic systems can be. This paper represents a first effort towards analytically analyzing semantic interoperability in the la...

Research on semantic search has become heated these years. In this paper we propose an approach focusing on searching for resources with descriptions. The knowledge representation we employ is based on conceptual graphs and is expressive with negation. We carry out semantic search by graph matching, which can be performed in polynomial time. Before matching we enrich the resource graphs with ba...

Knowledge encoded in semantic graphs such as Freebase has been shown to benefit semantic parsing and interpretation of natural language user utterances. In this paper, we propose new methods to assign weights to semantic graphs that reflect common usage types of the entities and their relations. Such statistical information can improve the disambiguation of entities in natural language utteranc...