Conceptual Graphs for Semantic Web Applications

Dieng-Kuntz, Rose; Corby, Olivier

doi:10.1007/11524564_2

Cited by 17 publications

(12 citation statements)

References 32 publications

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“…In real life applications the ontologies and their relationships can typically be represented as standardized ontology graphs [4], [7], [13], [19]. An ontology graph O = (V r , E r ) is an undirected graph, where (1) V r is a node set, where each node v r ∈ V r is a label referring to an entity; and (2) E r is a set of edges among the labels, where each edge e r ∈ E r represents a semantic relation (e.g., "refer to", "is a", "specialization" [19]) between two nodes.…”

Section: A Graphs Queries and Ontology Graphsmentioning

confidence: 99%

“…In practice, the ontology graphs and sim () can be obtained from e.g., semantic Web applications [13], Web mining [20], or domain experts [4]. While proposing more sophisticated models for ontologies and similarity functions are beyond the scope of this paper, we focus on technique that applies to a class of similarity functions sim ().…”

Section: A Graphs Queries and Ontology Graphsmentioning

confidence: 99%

“…construct Vq v and Eq v with mat(vq) and Go; 13. construct Gv:= (V qv , E qv , L qv ); 14. return Gv; G v construction (lines [11][12][13][14]. After all the concept graphs in I are processed, Gview constructs G v with a node set V qv , which contains a node for each match set, and a corresponding edge set E qv (lines [11][12][13].…”

Section: B Ontology-based Filteringmentioning

confidence: 99%

“…This is possible given the emerging development of ontology graphs [7], [13], [31]. An ontology graph typically consists of (1) a set of concepts or entities, and (2) a set of semantic relationships among the nodes.…”

Section: Introductionmentioning

confidence: 99%

“…The ontology information has been used in e.g., keyword searching [18], semantic queries [13], [11], [19], and social networks [9]. Nevertheless, little is known on how to exploit ontology graphs for effective subgraph querying.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Ontology-based subgraph querying

Yang

Yan

2013

2013 IEEE 29th International Conference on Data Engineering (ICDE)

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Subgraph querying has been applied in a variety of emerging applications. Traditional subgraph querying based on subgraph isomorphism requires identical label matching, which is often too restrictive to capture the matches that are semantically close to the query graphs. This paper extends subgraph querying to identify semantically related matches by leveraging ontology information. (1) We introduce the ontologybased subgraph querying, which revises subgraph isomorphism by mapping a query to semantically related subgraphs in terms of a given ontology graph. We introduce a metric to measure the similarity of the matches. Based on the metric, we introduce an optimization problem to find top K best matches. (2) We provide a filtering-and-verification framework to identify (top-K) matches for ontology-based subgraph queries. The framework efficiently extracts a small subgraph of the data graph from an ontology index, and further computes the matches by only accessing the extracted subgraph. (3) In addition, we show that the ontology index can be efficiently updated upon the changes to the data graphs, enabling the framework to cope with dynamic data graphs. (4) We experimentally verify the effectiveness and efficiency of our framework using both synthetic and real life graphs, comparing with traditional subgraph querying methods.

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Section: A Graphs Queries and Ontology Graphsmentioning

confidence: 99%

Section: A Graphs Queries and Ontology Graphsmentioning

confidence: 99%

Section: B Ontology-based Filteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ontology-based subgraph querying

Yang

Yan

2013

2013 IEEE 29th International Conference on Data Engineering (ICDE)

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Query-Answering CG Knowledge Bases

Leclère

Moreau

Conceptual Structures: Knowledge Visualization and Reasoning

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Conceptual graphs are a good choice for constructing and exploiting a knowledge base. In several of our projects (semantic portal for e-tourism, exploitation of digital object corpus, etc.), we have to query such bases. So it is natural to consider queries and bases as simple graphs and to compute the set of all projections from a query to a base. However, there is a problem of the return of this set of projections to the user. More generally, the main issue is about the definition of the notion of answers in an query-answering system made of knowledge bases formalized by graphs (Conceptual Graphs, RDF (Resource Description Framework) , Topic Maps, etc.). In this paper, we study several notions of answers and some of their characterizations. We distinguish between notions of answers by subgraphs of the base and answers by creation of result graphs. For the last type of answers, we define completeness, non-redundancy and minimality criteria of the answer sets and propose several notions of answers w.r.t these criteria.

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Web, Graphs and Semantics

Corby

Conceptual Structures: Knowledge Visualization and Reasoning

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In this paper we show how Conceptual Graphs (CG) are a powerful metaphor for identifying and understanding the W3C Resource Description Framework. We also presents CG as a target language and graph homomorphism as an abstract machine to interpret/implement RDF/S, SPARQL and Rules. We show that CG components can be used to implement such notions as named graphs and properties as resources. In brief, we think that CG are an excellent framework to progress in the Semantic Web because the W3C now considers that RDF graphs arealong with XML trees-one of the two standard formats for the Web 1 .

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Conceptual Graphs for Semantic Web Applications

Cited by 17 publications

References 32 publications

Ontology-based subgraph querying

Ontology-based subgraph querying

Query-Answering CG Knowledge Bases

Web, Graphs and Semantics

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