4137211
An agent-based infrastructure for enterprise integration
Some of the features that make Jackal (Java-based Applications Communication using KQML Agent communication Language) extremely valuable to agent development are its conversation management facilities, its flexible, blackboard-style interface and its ease of integration. Jackal has been developed in support of an investigation of the use of agents in enterprise-wide integration of planning and execution for manufacturing. This paper describes Jackal at a surface level and at the design level, and demonstrates its use in a multi-agent system that supports intelligent integration of enterprise planning and execution.
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.
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.
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.
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.
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.
17089555
Counting Small Cliques in 3-uniform Hypergraphs
Many applications of Szemeredi's Regularity Lemma for graphs are based on the following counting result. If ${\mathcal G}$ is an $s$-partite graph with partition $V({\mathcal G}) =\bigcup_{i=1}^{s} V_i$, $\vert V_i\vert =m$ for all $i\in [s]$, and all pairs $(V_i, V_j)$, $1\leq i < j\leq s$, are $\epsilon$-regular of density $d$, then $\mathcal{G}$ contains $(1\pm f(\epsilon))d^{({s\atop 2})}m^s$ cliques $K_{s}$, provided $\epsilon<\epsilon(d)$, where $f(\epsilon)$ tends to 0 as $\epsilon$ tends to 0.Guided by the regularity lemma for 3-uniform hypergraphs established earlier by Frankl and Rodl, Nagle and Rodl proved a corresponding counting lemma. Their proof is rather technical, mostly due to the fact that the ‘quasi-random’ hypergraph arising after application of Frankl and Rodl's regularity lemma is ‘sparse’, and consequently difficult to handle.When the ‘quasi-random’ hypergraph is ‘dense’ Kohayakawa, Rodl and Skokan (J. Combin. Theory Ser. A 97 307–352) found a simpler proof of the counting lemma. Their result applies even to $k$-uniform hypergraphs for arbitrary $k$. While the Frankl–Rodl regularity lemma will not render the dense case, in this paper, for $k=3$, we are nevertheless able to reduce the harder, sparse case to the dense case.Namely, we prove that a ‘dense substructure’ randomly chosen from the ‘sparse $\delta$-regular structure’ is $\delta$-regular as well. This allows us to count the number of cliques (and other subhypergraphs) using the Kohayakawa–Rodl–Skokan result, and provides an alternative proof of the counting lemma in the sparse case. Since the counting lemma in the dense case applies to $k$-uniform hypergraphs for arbitrary $k$, there is a possibility that the approach of this paper can be adopted to the general case as well.
42292049
Fitting semiparametric cure models
Survival data with a sizable cure fraction are commonly encountered in some cancer clinical researches and the semiparametric proportional hazards cure model has been recently investigated to analyze such data. However the estimation method of the model requires a special C program. The restrictive assumptions of the model also limit its application in broader settings. We present a new computational method for the cure model in this article. The method combines the computational methods for logistic regression and the Cox proportional hazards models and is easy to implement in many statistical packages. We also show how this method allows a number of useful extensions of the model to relax the restrictive assumptions. An illustrative example with survival times of lymphoma patients is provided.
20234262
Chromatic equivalence classes of certain generalized polygon trees
Let P(G) denote the chromatic polynomial of a graph G. Two graphs G and H are chromatically equivalent, written G ~ H, if P(G) = P(H). Let ¢ denote the family of all generalized polygon trees with three interior regions. Xu (1994) showed that ¢ is a union of chromatic equivalence classes under the equivalence relation ',-/. In this paper, we determine infinitely many chromatic equivalence classes in ~ under ',-~'. As a byproduct, we obtain a family of chromatically unique graphs established by Peng (1995).
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.
11039476
On the chromatic uniqueness of certain bipartite graphs
Let K(p, q), p ⩽ q, denote the complete bipartite graph in which the two partite sets consist of p and q vertices, respectively. We denote by K-r(p, q), the family of all graphs obtained by deleting any r distinct edges from K(p, q). Teo and Koh showed that K-1(p, q) is chromatically unique (in short χ-unique) for all p, q such that 3 ⩽ p ⩽ q. In this paper, we obtain a sufficient condition for a graph in K-2(p, q) to be χ-unique. Using this result, we then prove that each graph in K-2(p, p + d) is χ-unique for p ⩾ 4and 0 ⩽ d ⩽ 3. For d ⩾ 4, the graphs in K-2(p, q + d) are χ-unique if p > (A + √B)/4d2, where A and B are polynomials in d. We also show that each graph (≇K(4, 4) – K(1, 3)) in K-3(p, p + d) is χ-unique, for p ⩾ 4 and d=0, 1; and all graphs in K-3(p, p + 2) are χ-unique if and only if all graphs in K-4(p + 1, p + 1) are χ-unique, where p ⩾ 4. Finally we prove that every graph in K-4(p, p + 1) is χ-unique for p ⩾ 5.