Abstract Solving the Arithmetic Word Problems (AWPs) using AI techniques has attracted much attention in recent years. We feel that the current AWP solvers are under-utilizing the relevant domain knowledge. We present a knowledge- and learning-based system that effectively solves AWPs of a specific type—those that involve transfer of objects from one agent to another (Transfer Cases (TC)). We represent the knowledge relevant to these problems as TC Ontology. The sentences in TC-AWPs contain information of essentially four types: before-transfer, transfer, after-transfer, and query. Our system (KLAUS-Tr) uses statistical classifier to recognize the types of sentences. The sentence types guide the information extraction process used to identify the agents, quantities, units, types of objects, and the direction of transfer from the AWP text. The extracted information is represented as an RDF graph that utilizes the TC Ontology terminology. To solve the given AWP, we utilize semantic web rule language (SWRL) rules that capture the knowledge about how object transfer affects the RDF graph of the AWP. Using the TC ontology, we also analyze if the given problem is consistent or otherwise. The different ways in which TC-AWPs can be inconsistent are encoded as SWRL rules. Thus, KLAUS-Tr can identify if the given AWP is invalid and accordingly notify the user. Since the existing datasets do not have inconsistent AWPs, we create AWPs of this type and augment the datasets. We have implemented KLAUS-Tr and tested it on TC-type AWPs drawn from the All-Arith and other datasets. We find that TC-AWPs constitute about 40% of the AWPs in a typical dataset like All-Arith. Our system achieves an impressive accuracy of 92%, thus improving the state-of-the-art significantly. We plan to extend the system to handle AWPs that contain multiple transfers of objects and also offer explanations of the solutions.

Summary The syntactic information of a program can be represented as resource description framework (RDF) triples called program triples. We propose an extensible static analysis framework, called OPAL—Ontology‐based Program AnaLysis. The framework enables formal representation of external knowledge, such as usage knowledge of libraries and domain knowledge. Utilizing this knowledge and the program triples, we compute the semantic information, called static trace of the program. It is generated through path‐sensitive intraprocedural traversal of the program. We approximate information in case of loops by unrolling them a fixed number of times. The main contribution of the framework is to store the static trace as RDF triples called semantic triples. They are described using the Program Analysis ontology proposed in this article. The program triples and the semantic triples are together called consolidated program triples. These triples are stored and used to accelerate the execution of client‐analyses specified by the end user. In the framework, a client‐analysis is specified by a set of conjunctive expressions that use SPARQL (W3C RDF query language) queries. The framework is effective for the client‐analyses that warrant sound and approximate information. The effectiveness is assessed first, using two source‐code‐analyses that require only the program triples, and then 10 intraprocedural path‐sensitive analyses that require the consolidated program triples. Using NPB and SPEC 2006 benchmarks, we achieve an improvement in the conciseness of analysis specifications. Also, the execution time using OPAL is competitive to LLVM's clang for individual analysis and outperforms clang over a series of analyses because of the reuse of consolidated program triples.

In this paper, we propose a new CNL called tedei (TExtual DEscription Identifier) whose grammar is inspired by the different ways in which OWL-DL constructs are expressed in English. We built a system that transforms Tedei sentences into corresponding OWL-DL axioms. Now, ambiguity due to different possible lexicalizations of sentences and semantic ambiguity present in sentences are challenges in this context. We find that the best way to handle these challenges is to construct axioms corresponding to alternative formalizations of the sentence so that the end-user can make an appropriate choice. The output is compared against human-authored axioms and in substantial number of cases, human-authored axiom is indeed one of the alternatives given by the system. The proposed system substantially enhances the types of sentence structures that can be used for ontology authoring.

The existing query-log based on-line index maintenance approaches rely on frequency distribution of terms in the static query-log. Though these approaches are proved to be efficient, but in real world, the frequency distribution of the terms changes over a period of time. This negatively affects the efficiency of the static query-log based approaches. To overcome this problem, we propose an index tuning strategy for reorganizing the indexes according to the latest frequency distribution of the terms captured from query-logs.Experimental results show that the proposed tuning strategy improves the performance of static query-log based approaches.

In this paper, we propose a new merge-based index maintenance strategy for Information Retrieval systems. The new model is based on partitioning of the inverted index across the terms in it. We exploit the query log to partition the on-disk inverted index into two types of sub-indexes. Inverted lists of the terms contained in the queries that are frequently posed to the Information Retrieval systems are kept in one partition, called frequent-term index and the other inverted lists form another partition, called infrequent-term index. We use a lazy-merge strategy for maintaining infrequent-term sub-indexes, and an active merge strategy for maintaining frequent-term sub-indexes. The sub-indexes are also similarly split into frequent and in-frequent parts. Experimental results show that the proposed method improves both index maintenance performance and query performance compared to the existing merge-based strategies.

With the advent of XML as the new standard for information representation and exchange, indexing and querying of XML data is of major concern. In this paper, we propose a method for representing an XML document as a sequence based on a variation of Prüfer sequences. We incorporate new components in the node encodings such as level, number of a certain kind of descendants and develop methods for holistic processing of tree pattern queries. The query processing involves converting the query also into a sequence and performing subsequence matching on the document sequence. We establish certain interesting properties of the proposed method of sequencing that give rise to a new efficient pattern matching algorithm. The sequence data is stored in a two level B+-trees to support query processing. We also propose an optimization for parent-child axis to speed up the query processing. Our approach does not require any post-processing and guarantees results that are free of false positives and duplicates. Experimental results show that our system performs significantly better than previous systems in a large number of cases.

Wavelength routed optical networks have emerged as a technology that can effectively utilize the enormous bandwidth of the optical fiber. Wavelength converters play an important role in enhancing the fiber utilization and reducing the overall call blocking probability of the network. As the distortion of the optical signal increases with the increase in the range of wavelength conversion in optical wavelength converters, limited range wavelength conversion assumes importance. Placement of wavelength converters is a NP complete problem in an arbitrary mesh network. In this paper, we investigate heuristics for placing limited range wavelength converters in arbitrary mesh wavelength routed optical networks. The objective is to achieve near optimal placement of limited range wavelength converters resulting in reduced blocking probabilities and low distortion of the optical signal. The proposed heuristic is to place limited range wavelength converters at the most congested nodes, nodes which lie on the long lightpaths and nodes where conversion of the optical signals is significantly high. We observe that limited range converters at a few nodes can provide almost the entire improvement in the blocking probability as the full range wavelength converters placed at all the nodes. Congestion control in the network is brought about by dynamically adjusting the weights of the channels in the link thereby balancing the load and reducing the average delay of the traffic in the entire network. Simulations have been carried out on a 12-node ring network, 14-node NSFNET, 19-node European Optical Network (EON), 30-node INET network and the results agree with the analysis.

This paper attempts to study the impact of wavelength converters in WDM wavelength routed all-optical networks. A new heuristic approach for placement of wavelength converters to reduce blocking probabilities is explored. Multihop virtual topology is designed to minimize the number and overall cost of the converters. Blocking probabilities for static lightpath establishment (SLE) and dynamic lightpath establishment (DLE) are analyzed. In the case of SLE, arranging lightpaths in ascending order of their path length reduces blocking probability. Wavelength converters placed at nodes with high nodal degree further reduces the blocking probabilities. Simulation studies performed on a 28-node USA long haul network and a 20-node arbitrary mesh network, validate the above observations.