Sourish Dasgupta

Taxonomic Clustering and Query Matching for Efficient Service Discovery

Service discovery is one of the key problems that have been widely researched in the area of Service Oriented Architecture (SOA) based systems. Web Service clustering is a technique for efficiently facilitating service discovery. Most Web Service clustering approaches are based on suitable semantic similarity distance measure and a threshold. Threshold selection is essentially difficult and often leads to unsatisfactory accuracy. In this paper, we have proposed a self-organizing based clustering algorithm called Taxonomic clustering for taxonomically organizing semantic Web Service advertisements. We have tested the algorithm on both simulation based randomly generated test data and the standard OWL-S TC test data set. We have observed promising results both in terms of accuracy and performance.

AskNow: A Framework for Natural Language Query Formalization in SPARQL

Natural Language Query Formalization involves semantically parsing queries in natural language and translating them into their corresponding formal representations. It is a key component for developing question-answering QA systems on RDF data. The chosen formal representation language in this case is often SPARQL. In this paper, we propose a framework, called AskNow, where users can pose queries in English to a target RDF knowledge base e.g.i?źDBpedia, which are first normalized into an intermediary canonical syntactic form, called Normalized Query Structure NQS, and then translated into SPARQL queries. NQS facilitates the identification of the desire or expected output information and the user-provided input information, and establishing their mutual semantic relationship. At the same time, it is sufficiently adaptive to query paraphrasing. We have empirically evaluated the framework with respect to the syntactic robustness of NQS and semantic accuracy of the SPARQL translator on standard benchmark datasets.

Taxonomic clustering of web service for efficient discovery

The World Wide Web (WWW) has become a major platform for hosting, discovering, and composing web services. Web service clustering is a technique for efficiently facilitating web service discovery. Most web service clustering approaches are based on suitable semantic similarity distance measure and a threshold. Threshold selection is essentially difficult and often leads to unsatisfactory accuracy. In this paper we propose a taxonomic clustering algorithm for grouping functionally similar web services. We have tested the algorithm on both simulation based randomly generated test data and the standard OWL-S TC test data set. We have observed promising results both in terms of accuracy and performance.

SGPS: a semantic scheme for web service similarity

Todays Web becomes a platform for services to be dynamically interconnected to produce a desired outcome. It is important to formalize the semantics of the contextual elements of web services. In this paper, we propose a novel technique called Semantic Genome Propagation Scheme (SGPS) for measuring similarity between semantic concepts. We show how SGPS is used to compute a multi-dimensional similarity between two services. We evaluate the SGPS similarity measurement in terms of the similarity performance and scalability.

Event driven service composition for pervasive computing

Pervasive Computing plays an integral role in our daily life. As pervasive services become increasingly important to be able to capture context, it is reasonable to speculate that dynamic representations for composition of services is very much needed. Such representations may typically include activities or functional modules that are not only temporal but also responsive to each other. Such actions may be associated with actors who execute them, spatial and temporal information, information-flow and background objects. Consequently, one may model an activity network that depicts causal relations between activities and has the capability of embedding the semantic relations of these activities with other contextual entities. In this paper we discuss a new way of representing such dynamic workflows with a novel data structure called Activity Logic Graphs (ALGs). We introduce a novel design of an architecture that grasps the semantics of activities (or events) in dynamic pervasive environments, maps that to a collaboration of services (as ALGs) in order to recognize specific situations (known as Situation Boundaries) that has risen out of the events, and implements the recognition process using an efficient indexing technique. We test the model with a set of ALGs and report experimental results on the queries that are essential for service composition.

s-Birds Avengers: A Dynamic Heuristic Engine-Based Agent for the Angry Birds Problem

Angry Birds is a popular video game in which a set of birds has to perform sling shots (bird shots) so as to kill pigs that are protected by a structure composed of different building blocks. The fewer birds we use and the more blocks we destroy, the higher the score we achieve. AIBirds competition is an AI challenge where an intelligent bot has to be developed that plays the game without human intervention. In this paper, we describe the approach implemented in the bot, s-birds Avengers, that participated in ECAI AIBirds 2014. Heuristic techniques were designed to analyze unseen structures using various structural parameters and then to discover their vulnerable points using prior parameter learning training algorithm. The bot then uses this to decide where to hit the structure with the birds.

SimDoc: Topic Sequence Alignment based Document Similarity Framework

Document similarity is the problem of estimating the degree to which a given pair of documents has similar semantic content. An accurate document similarity measure can improve several enterprise relevant tasks such as document clustering, text mining, and question-answering. In this paper, we show that a documents thematic flow, which is often disregarded by bag-of-word techniques, is pivotal in estimating their similarity. To this end, we propose a novel semantic document similarity framework, called SimDoc. We model documents as topic-sequences, where topics represent latent generative clusters of related words. Then, we use a sequence alignment algorithm to estimate their semantic similarity. We further conceptualize a novel mechanism to compute topic-topic similarity to fine tune our system. In our experiments, we show that SimDoc outperforms many contemporary bag-of-words techniques in accurately computing document similarity, and on practical applications such as document clustering.

A logic-based formalism for pervasive workflow

In order to deal with dynamic events occurring in the system, we designed three basic operations over this ALNet: (i) search for end services that can trigger the target event, (ii) search for source nodes that have reachability to at least one of the end nodes, and (iii) select the source node that gives the best composition cost. However, with the addition of more and more services, the ALNet becomes larger and complex and then performing these three tasks might be computationally costly. In order to solve the problem, we proposed an abstraction method that improves the performance significantly and demonstrated that this can be an effective platform for realizing event handling in pervasive systems. As an evaluation of the ALNet, an experimental setup has been designed and the abstraction runtime performance has been tested over different sizes service network. We have found out that the abstraction runtime performance is improved (compared to one without the abstraction) and has shown a steady increase even with the increasing size of the ALNet network.

CAOFES: an ontological framework for web service retrieval

Semantic web search involves retrieval of user-specific web artifacts by utilizing their semantic descriptions. A very specific web artifact that has evolved in recent times is web services. Web services can be semantically described in languages like OWL-S. However, such languages are limited with respect to their expressivity of context. They also lack a formal ontological framework where efficient web service retrieval can be conducted. In this paper we model a service request scenario within the web as an event-driven system. Services as well as user requests are modeled as events. We propose an ontological framework called Context-Aware Ontology Framework for Events and Services (CAOFES) where such event-driven web service retrieval can be efficiently executed by a novel reasoning technique.