A neural network for semantic labelling of structured information

Intelligent systems rely on rich sources of information to make informed decisions. Using information from external sources requires establishing correspondences between the information and known information classes. This can be achieved with semantic labelling, which assigns known labels to structured information by classifying it according to computed features. The existing proposals have explored different sets of features, without focusing on what classification techniques are used. In this paper we present three contributions: first, insights on architectural issues that arise when using neural networks for semantic labelling; second, a novel implementation of semantic labelling that uses a state-of-the-art neural network classifier which achieves significantly better results than other four traditional classifiers; third, a comparison of the results obtained by the former network when using different subsets of features, comparing textual features to structural ones, and domain-dependent features to domain-independent ones. The experiments were carried away with datasets from three real world sources. Our results show that there is a need to develop more semantic labelling proposals with sophisticated classification techniques and large features catalogues.Ministerio de Economía y Competitividad TIN2016-75394-

TAPON: a two-phase machine learning approach for semantic labelling

Through semantic labelling we enrich structured information from sources such as HTML pages, tables, or JSON files, with labels to integrate it into a local ontology. This process involves measuring some features of the information and then nding the classes that best describe it. The problem with current techniques is that they do not model relationships between classes. Their features fall short when some classes have very similar structures or textual formats. In order to deal with this problem, we have devised TAPON: a new semantic labelling technique that computes novel features that take into account the relationships. TAPON computes these features by means of a two-phase approach. In the first phase, we compute simple features and obtain a preliminary set of labels (hints). In the second phase, we inject our novel features and obtain a refined set of labels. Our experimental results show that our technique, thanks to our rich feature catalogue and novel modelling, achieves higher accuracy than other state-of-the-art techniques.Ministerio de Economía y Competitividad TIN2016-75394-

AYNEC: All you need for evaluating completion techniques in knowledge graphs

The popularity of knowledge graphs has led to the development of techniques to refine them and increase their quality. One of the main refinement tasks is completion (also known as link prediction for knowledge graphs), which seeks to add missing triples to the graph, usually by classifying potential ones as true or false. While there is a wide variety of graph completion techniques, there is no standard evaluation setup, so each proposal is evaluated using different datasets and metrics. In this paper we present AYNEC, a suite for the evaluation of knowledge graph completion techniques that covers the entire evaluation workflow. It includes a customisable tool for the generation of datasets with multiple variation points related to the preprocessing of graphs, the splitting into training and testing examples, and the generation of negative examples. AYNEC also provides a visual summary of the graph and the optional exportation of the datasets in an open format for their visualisation. We use AYNEC to generate a library of datasets ready to use for evaluation purposes based on several popular knowledge graphs. Finally, it includes a tool that computes relevant metrics and uses significance tests to compare each pair of techniques. These open source tools, along with the datasets, are freely available to the research community and will be maintained.Ministerio de Economía y Competitividad TIN2016-75394-

Improving Semantic Web Services Discovery Using SPARQL-Based Repository Filtering

Semantic Web Services discovery is commonly a heavyweight task, which has scalability issues when the number of services or the ontology complexity increase, because most approaches are based on Description Logics reasoning. As a higher number of services becomes available, there is a need for solutions that improve discovery performance. Our proposal tackles this scalability problem by adding a preprocessing stage based on two SPARQL queries that filter service repositories, discarding service descriptions that do not refer to any functionality or non-functional aspect requested by the user before the actual discovery takes place. This approach fairly reduces the search space for discovery mechanisms, consequently improving the overall performance of this task. Furthermore, this particular solution does not provide yet another discovery mechanism, but it is easily applicable to any of the existing ones, as our prototype evaluation shows. Moreover, proposed queries are automatically generated from service requests, transparently to the user. In order to validate our proposal, this article showcases an application to the OWL-S ontology, in addition to a comprehensive performance analysis that we carried out in order to test and compare the results obtained from proposed filters and current discovery approaches, discussing the benefits of our proposal

SOA4 All Integrated Ranking: a Preference-Based, Holistic Implementation

There exist many available service ranking implementations, each one providing ad hoc preference models that offer different levels of expressiveness. Consequently, applying a single implementation to a particular scenario constrains the user to define preferences based on the underlying formalisms. Furthermore, preferences from different ranking implementation’s model cannot be combined in general, due to interoperability issues. in this article we present an integrated ranking implementation that enables the combination of three different ranking implementations developed within the EU FP7 SOA4All project. Our solution has been developed using PURI, a Preference-based Universal Ranking Integration framework that is based on a common, holistic preference model that allows to exploit synergies from the integrated ranking implementations, offering a single user interface to define preferences that acts as a façade to the integrated ranking implementation

On User Preferences and Utility Functions in Selection: A Semantic Approach

Discovery tasks in the context of Semantic Web Services are generally performed using Description Logics. However, this formalism is not suited when non-functional, numerical parameters are involved in the discovery process. Furthermore, in selection tasks, where an optimization algorithm is needed, DLs are not capable of computing the optimum. Although there are DLs extensions that can handle numerical parameters, they bring decidability problems. Other solutions, as hybrid approaches which use DLs in functional discovery and other formalisms in non-functional selection, do not provide a semantic framework to describe user preferences based on non-functional properties. In this work, we propose to semantically describe user preferences, so they can be used to perform selection within a hybrid solution. By using semantically described utility functions in order to define user preferences, our proposal enables interoperability between service offers and demands, while providing a high level of expressiveness in these preferences and including them within SWS descriptions.Comisión Interministerial de Ciencia y Tecnología TIN2006-0047

A Service Ranker Based on Logic Rules Evaluation and Constraint Programming

Ranking of Semantic Web Services is usually performed based on user preferences descriptions. These descriptions are expressed in terms of an underlying logical formalism, which limits their expressiveness. Thus, there are some kind of descriptions, such as utility functions, that cannot be handled by reasoners currently being used to perform Semantic Web Services tasks, though utility functions provide a higher level of expressiveness. in this work, we present a hybrid solution to allow the introduction of utility functions in user preferences descriptions, using both Logic Programming rules evaluation and Constraint Programming to perform the ranking process. This proposal is based on the Web Service Modeling Ontology, extending it with a highly expressive framework to specify user preferences, and enabling the integration of different engines to perform the ranking process

Implementing Multiparty Interactions on a Network Computer

Classical client/server interaction primitives such as remote procedure call or rendez–vous are not adequate when we need to describe the behaviour of three or more processes that need to collaborate simultaneously in order to solve a problem. Multiparty interactions are the key to describe these problems, and there are several languages that use them for the description of reactive systems. In this paper, we show and compare two different fair implementations of this mechanism and also outline the research we are carrying out in an effort to improve them

QoS-Aware Semantic Service Selection: An Optimization Problem

In order to select the best suited service among a set of discovered services, with respect to QOS parameters, a user have to state his or her preferences, so services can be ranked according to these QOS parameters. Current Se- mantic Web Services ontologies do not support the defini- tion of QOS-aware user preferences, though there are some proposals that extend those ontologies to allow selection based on those preferences. However, their selection algo- rithms are very coupled with user preferences descriptions, which are defined without semantics or at a different seman- tic level than service functionality. In this work, we present a service selection framework that transforms user prefer- ences into an optimization problem where the best service is selected. This framework is based on an ontology that conceptualizes these user preferences. Thus, we use a very expressive solution decoupled with the concrete selection technique by using XSL transformations, while describing QOS-aware user preferences at the same semantic level of functional preferences.Comisión Interministerial de Ciencia y Tecnología TIN2006-00472Junta de Andalucía TIC-253