1962537
Finding and Ranking Knowledge on the Semantic Web
Swoogle helps software agents and knowledge engineers find Semantic Web knowledge encoded in RDF and OWL documents on the Web. Navigating such a Semantic Web on the Web is difficult due to the paucity of explicit hyperlinks beyond the namespaces in URIrefs and the few inter-document links like rdfs:seeAlso and owl:imports. In order to solve this issue, this paper proposes a novel Semantic Web navigation model providing additional navigation paths through Swoogle's search services such as the Ontology Dictionary. Using this model, we have developed algorithms for ranking the importance of Semantic Web objects at three levels of granularity: documents, terms and RDF graphs. Experiments show that Swoogle outperforms conventional web search engine and other ontology libraries in finding more ontologies, ranking their importance, and thus promoting the use and emergence of consensus ontologies.
7138939
A probabilistic extension to ontology language OWL
To support uncertain ontology representation and ontology reasoning and mapping, we propose to incorporate Bayesian networks (BN), a widely used graphic model for knowledge representation under uncertainty and OWL, the de facto industry standard ontology language recommended by W3C. First, OWL is augmented to allow additional probabilistic markups, so probabilities can be attached with individual concepts and properties in an OWL ontology. Secondly, a set of translation rules is defined to convert this probabilistically annotated OWL ontology into the directed acyclic graph (DAG) of a BN. Finally, the BN is completed by constructing conditional probability tables (CPT) for each node in the DAG. Our probabilistic extension to OWL is consistent with OWL semantics, and the translated BN is associated with a joint probability distribution over the application domain. General Bayesian network inference procedures (e.g., belief propagation or junction tree) can be used to compute P(C/spl bsol/e): the degree of the overlap or inclusion between a concept C and a concept represented by a description e. We also provide a similarity measure that can be used to find the most similar concept that a given description belongs to.
47142619
Semantic Resolution for E-commerce
We describe a research project on resolving semantic differences for multi-agent systems (MAS) in electronic commerce. The approach can be characterized as follows: (1) agents in a MAS may have their own specific ontologies defined on top of a shared base ontology; (2) concepts in these ontologies are represented as frame-like structures based on DAML+OIL language; (3) the semantic differences between agents are resolved at runtime through inter-agent communication; and (4) the resolution is viewed as an abductive inference process, and thus necessarily involves approximate reasoning.
183316
Modifying Bayesian Networks by Probability Constraints
This paper deals with the following problem: modify a Bayesian network to satisfy a given set of probability constraints by only change its conditional probability tables, and the probability distribution of the resulting network should be as close as possible to that of the original network. We propose to solve this problem by extending IPFP (iterative proportional fitting procedure) to probability distributions represented by Bayesian networks. The resulting algorithm E-IPFP is further developed to D-IPFP, which reduces the computational cost by decomposing a global E-IPFP into a set of smaller local E-IPFP problems. Limited analysis is provided, including the convergence proofs of the two algorithms. Computer experiments were conducted to validate the algorithms. The results are consistent with the theoretical analysis.
1517191
Semantic resolution for e-commerce
We describe a research project on resolving semantic differences for multi-agent systems (MAS) in electronic commerce. The approach can be characterized as follows: (1) agents in a MAS may have their own specific ontologies defined on top of a shared base ontology; (2) concepts in these ontologies are represented as frame-like structures based on DAML+OIL language; (3) the semantic differences between agents are resolved at runtime through inter-agent communication; and (4) the resolution is viewed as an abductive inference process, and thus necessarily involves approximate reasoning.
723568
A Bayesian Network Approach to Ontology Mapping
This paper presents our ongoing effort on developing a principled methodology for automatic ontology mapping based on BayesOWL, a probabilistic framework we developed for modeling uncertainty in semantic web. In this approach, the source and target ontologies are first translated into Bayesian networks (BN); the concept mapping between the two ontologies are treated as evidential reasoning between the two translated BNs. Probabilities needed for constructing conditional probability tables (CPT) during translation and for measuring semantic similarity during mapping are learned using text classification techniques where each concept in an ontology is associated with a set of semantically relevant text documents, which are obtained by ontology guided web mining. The basic ideas of this approach are validated by positive results from computer experiments on two small real-world ontologies.
18457324
On the higher-order edge toughness of a graph
For an integer c, 1?c??V(G)??1, we define the cth-order edge toughness of a graph G as tc(G)=min|X|?(G?X)?cX?E(G) & ?(G?X)>c The objective of this paper is to study this generalized concept of edge toughness. Besides giving the of the cth-order edge toughness ?c(G) of a graph G, we prove that `?c(G)?k if and only if G has k edge-disjoint spanning forests with exactly c components?. We also study the `balancity? of a graph G of order p and size q, which is defined as the smallest positive integer c such that ?c(G) = p/(p?c).
2551506
Chromatic Equivalence Classes of Certain Generalized Polygon Trees, II
Let P(G) denote the chromatic polynomial of a graph G. Two graphs G and H are chromatically equivalent, if P(G)=P(H). A set of graphs S is called a chromatic equivalence class if for any graph H that is chromatically equivalent with a graph G in S, then [email protected]?S. Peng et al. (Discrete Math. 172 (1997) 103-114), studied the chromatic equivalence classes of certain generalized polygon trees. In this paper, we continue that study and present a solution to Problem 2 in Koh and Teo (Discrete Math. 172 (1997) 59-78).
14722098
TCS-Chord: An Improved Routing Algorithm to Chord Based on the Topology-aware Clustering in Self-organizing Mode
The study on semantic routing efficiency of DHTP2P networks is a key element to promote the development of P2P networks, it is critical for a P2P routing algorithm to be aware of the network physical topology, which leads to improvement of the routing efficiency. By analyzing the Hashing function's properties, some novel logical connections are presented among the destination node, the Chord semantic routing relay node sequence, and the ID of clustering neighbor nodes. This paper describes TCS-Chord (an improved routing algorithm to Chord based on the Topology-aware Clustering in Self-organizing mode) to improve the efficiency of Chord routing. Since the clustering nodes only have local views in the self-organizing mode, some rules are applied for a node to learn other nodes' physical topology-aware locations. The TCS-Chord's routing algorithm is described completely, and our experiments also indicate that TCS-Chord can improve the Chord semantic routing efficiently.
5372255
Holes in Graphs
The celebrated Regularity Lemma of Szemeredi asserts that every sufficiently large graph $G$ can be partitioned in such a way that most pairs of the partition sets span $\epsilon$-regular subgraphs. In applications, however, the graph $G$ has to be dense and the partition sets are typically very small. If only one $\epsilon$-regular pair is needed, a much bigger one can be found, even if the original graph is sparse. In this paper we show that every graph with density $d$ contains a large, relatively dense $\epsilon$-regular pair. We mainly focus on a related concept of an $(\epsilon,\sigma)$-dense pair, for which our bound is, up to a constant, best possible.
17328688
Improved Dataset Characterisation for Meta-learning
This paper presents new measures, based on the induced decision tree, to characterise datasets for meta-learning in order to select appropriate learning algorithms. The main idea is to capture the characteristics of dataset from the structural shape and size of decision tree induced from the dataset. Totally 15 measures are proposed to describe the structure of a decision tree. Their effectiveness is illustrated through extensive experiments, by comparing to the results obtained by the existing data characteristics techniques, including data characteristics tool (DCT) that is the most wide used technique in meta-learning, and Landmarking that is the most recently developed method.