A proposal for an interchange abstract syntax for (parallel) Prolog

We propose an abstract syntax for Prolog that will help the manipulation of programs at compile-time, as well as the exchange of sources and information among the tools designed for this manipulation. This includes analysers, partial evaluators, and program transformation tools. We have chosen to concentrate on the information exchange format, rather than on the syntax of programs, for which we assume a simplified format. Our purpose is to provide a low-level meeting point for the tools which will allow them to read the same programs and understand the information about them. This report describes our first design in an informal way. We expect this design to evolve and concretize, along with the future development of the tools, during the project.

Federating queries in SPARQL 1.1: syntax, semantics and evaluation

Given the sustained growth that we are experiencing in the number of SPARQL endpoints available, the need to be able to send federated SPARQL queries across these has also grown. To address this use case, the W3C SPARQL working group is defining a federation extension for SPARQL 1.1 which allows for combining graph patterns that can be evaluated over several endpoints within a single query. In this paper, we describe the syntax of that extension and formalize its semantics. Additionally, we describe how a query evaluation system can be implemented for that federation extension, describing some static optimization techniques and reusing a query engine used for data-intensive science, so as to deal with large amounts of intermediate and final results. Finally we carry out a series of experiments that show that our optimizations speed up the federated query evaluation process.

Dealing with Semantic Heterogeneity Issues on the Web

The Semantic Web is an extension of the traditional Web in which meaning of information is well defined, thus allowing a better interaction between people and computers. To accomplish its goals, mechanisms are required to make explicit the semantics of Web resources, to be automatically processed by software agents (this semantics being described by means of online ontologies). Nevertheless, issues arise caused by the semantic heterogeneity that naturally happens on the Web, namely redundancy and ambiguity. For tackling these issues, we present an approach to discover and represent, in a non-redundant way, the intended meaning of words in Web applications, while taking into account the (often unstructured) context in which they appear. To that end, we have developed novel ontology matching, clustering, and disambiguation techniques. Our work is intended to help bridge the gap between syntax and semantics for the Semantic Web construction

Combining statistical and semantic approaches to the translation of ontologies and taxonomies

Ontologies and taxonomies are widely used to organize concepts providing the basis for activities such as indexing, and as background knowledge for NLP tasks. As such, translation of these resources would prove useful to adapt these systems to new languages. However, we show that the nature of these resources is significantly different from the "free-text" paradigm used to train most statistical machine translation systems. In particular, we see significant differences in the linguistic nature of these resources and such resources have rich additional semantics. We demonstrate that as a result of these linguistic differences, standard SMT methods, in particular evaluation metrics, can produce poor performance. We then look to the task of leveraging these semantics for translation, which we approach in three ways: by adapting the translation system to the domain of the resource; by examining if semantics can help to predict the syntactic structure used in translation; and by evaluating if we can use existing translated taxonomies to disambiguate translations. We present some early results from these experiments, which shed light on the degree of success we may have with each approach

OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web

OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web 1. INTRODUCTION. LINGUISTIC TOOLS AND ANNOTATIONS: THEIR LIGHTS AND SHADOWS Computational Linguistics is already a consolidated research area. It builds upon the results of other two major ones, namely Linguistics and Computer Science and Engineering, and it aims at developing computational models of human language (or natural language, as it is termed in this area). Possibly, its most well-known applications are the different tools developed so far for processing human language, such as machine translation systems and speech recognizers or dictation programs. These tools for processing human language are commonly referred to as linguistic tools. Apart from the examples mentioned above, there are also other types of linguistic tools that perhaps are not so well-known, but on which most of the other applications of Computational Linguistics are built. These other types of linguistic tools comprise POS taggers, natural language parsers and semantic taggers, amongst others. All of them can be termed linguistic annotation tools. Linguistic annotation tools are important assets. In fact, POS and semantic taggers (and, to a lesser extent, also natural language parsers) have become critical resources for the computer applications that process natural language. Hence, any computer application that has to analyse a text automatically and ‘intelligently’ will include at least a module for POS tagging. The more an application needs to ‘understand’ the meaning of the text it processes, the more linguistic tools and/or modules it will incorporate and integrate. However, linguistic annotation tools have still some limitations, which can be summarised as follows: 1. Normally, they perform annotations only at a certain linguistic level (that is, Morphology, Syntax, Semantics, etc.). 2. They usually introduce a certain rate of errors and ambiguities when tagging. This error rate ranges from 10 percent up to 50 percent of the units annotated for unrestricted, general texts. 3. Their annotations are most frequently formulated in terms of an annotation schema designed and implemented ad hoc. A priori, it seems that the interoperation and the integration of several linguistic tools into an appropriate software architecture could most likely solve the limitations stated in (1). Besides, integrating several linguistic annotation tools and making them interoperate could also minimise the limitation stated in (2). Nevertheless, in the latter case, all these tools should produce annotations for a common level, which would have to be combined in order to correct their corresponding errors and inaccuracies. Yet, the limitation stated in (3) prevents both types of integration and interoperation from being easily achieved. In addition, most high-level annotation tools rely on other lower-level annotation tools and their outputs to generate their own ones. For example, sense-tagging tools (operating at the semantic level) often use POS taggers (operating at a lower level, i.e., the morphosyntactic) to identify the grammatical category of the word or lexical unit they are annotating. Accordingly, if a faulty or inaccurate low-level annotation tool is to be used by other higher-level one in its process, the errors and inaccuracies of the former should be minimised in advance. Otherwise, these errors and inaccuracies would be transferred to (and even magnified in) the annotations of the high-level annotation tool. Therefore, it would be quite useful to find a way to (i) correct or, at least, reduce the errors and the inaccuracies of lower-level linguistic tools; (ii) unify the annotation schemas of different linguistic annotation tools or, more generally speaking, make these tools (as well as their annotations) interoperate. Clearly, solving (i) and (ii) should ease the automatic annotation of web pages by means of linguistic tools, and their transformation into Semantic Web pages (Berners-Lee, Hendler and Lassila, 2001). Yet, as stated above, (ii) is a type of interoperability problem. There again, ontologies (Gruber, 1993; Borst, 1997) have been successfully applied thus far to solve several interoperability problems. Hence, ontologies should help solve also the problems and limitations of linguistic annotation tools aforementioned. Thus, to summarise, the main aim of the present work was to combine somehow these separated approaches, mechanisms and tools for annotation from Linguistics and Ontological Engineering (and the Semantic Web) in a sort of hybrid (linguistic and ontological) annotation model, suitable for both areas. This hybrid (semantic) annotation model should (a) benefit from the advances, models, techniques, mechanisms and tools of these two areas; (b) minimise (and even solve, when possible) some of the problems found in each of them; and (c) be suitable for the Semantic Web. The concrete goals that helped attain this aim are presented in the following section. 2. GOALS OF THE PRESENT WORK As mentioned above, the main goal of this work was to specify a hybrid (that is, linguistically-motivated and ontology-based) model of annotation suitable for the Semantic Web (i.e. it had to produce a semantic annotation of web page contents). This entailed that the tags included in the annotations of the model had to (1) represent linguistic concepts (or linguistic categories, as they are termed in ISO/DCR (2008)), in order for this model to be linguistically-motivated; (2) be ontological terms (i.e., use an ontological vocabulary), in order for the model to be ontology-based; and (3) be structured (linked) as a collection of ontology-based triples, as in the usual Semantic Web languages (namely RDF(S) and OWL), in order for the model to be considered suitable for the Semantic Web. Besides, to be useful for the Semantic Web, this model should provide a way to automate the annotation of web pages. As for the present work, this requirement involved reusing the linguistic annotation tools purchased by the OEG research group (http://www.oeg-upm.net), but solving beforehand (or, at least, minimising) some of their limitations. Therefore, this model had to minimise these limitations by means of the integration of several linguistic annotation tools into a common architecture. Since this integration required the interoperation of tools and their annotations, ontologies were proposed as the main technological component to make them effectively interoperate. From the very beginning, it seemed that the formalisation of the elements and the knowledge underlying linguistic annotations within an appropriate set of ontologies would be a great step forward towards the formulation of such a model (henceforth referred to as OntoTag). Obviously, first, to combine the results of the linguistic annotation tools that operated at the same level, their annotation schemas had to be unified (or, preferably, standardised) in advance. This entailed the unification (id. standardisation) of their tags (both their representation and their meaning), and their format or syntax. Second, to merge the results of the linguistic annotation tools operating at different levels, their respective annotation schemas had to be (a) made interoperable and (b) integrated. And third, in order for the resulting annotations to suit the Semantic Web, they had to be specified by means of an ontology-based vocabulary, and structured by means of ontology-based triples, as hinted above. Therefore, a new annotation scheme had to be devised, based both on ontologies and on this type of triples, which allowed for the combination and the integration of the annotations of any set of linguistic annotation tools. This annotation scheme was considered a fundamental part of the model proposed here, and its development was, accordingly, another major objective of the present work. All these goals, aims and objectives could be re-stated more clearly as follows: Goal 1: Development of a set of ontologies for the formalisation of the linguistic knowledge relating linguistic annotation. Sub-goal 1.1: Ontological formalisation of the EAGLES (1996a; 1996b) de facto standards for morphosyntactic and syntactic annotation, in a way that helps respect the triple structure recommended for annotations in these works (which is isomorphic to the triple structures used in the context of the Semantic Web). Sub-goal 1.2: Incorporation into this preliminary ontological formalisation of other existing standards and standard proposals relating the levels mentioned above, such as those currently under development within ISO/TC 37 (the ISO Technical Committee dealing with Terminology, which deals also with linguistic resources and annotations). Sub-goal 1.3: Generalisation and extension of the recommendations in EAGLES (1996a; 1996b) and ISO/TC 37 to the semantic level, for which no ISO/TC 37 standards have been developed yet. Sub-goal 1.4: Ontological formalisation of the generalisations and/or extensions obtained in the previous sub-goal as generalisations and/or extensions of the corresponding ontology (or ontologies). Sub-goal 1.5: Ontological formalisation of the knowledge required to link, combine and unite the knowledge represented in the previously developed ontology (or ontologies). Goal 2: Development of OntoTag’s annotation scheme, a standard-based abstract scheme for the hybrid (linguistically-motivated and ontological-based) annotation of texts. Sub-goal 2.1: Development of the standard-based morphosyntactic annotation level of OntoTag’s scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996a) and also the recommendations included in the ISO/MAF (2008) standard draft. Sub-goal 2.2: Development of the standard-based syntactic annotation level of the hybrid abstract scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996b) and the ISO/SynAF (2010) standard draft. Sub-goal 2.3: Development of the standard-based semantic annotation level of OntoTag’s (abstract) scheme. Sub-goal 2.4: Development of the mechanisms for a convenient integration of the three annotation levels already mentioned. These mechanisms should take into account the recommendations included in the ISO/LAF (2009) standard draft. Goal 3: Design of OntoTag’s (abstract) annotation architecture, an abstract architecture for the hybrid (semantic) annotation of texts (i) that facilitates the integration and interoperation of different linguistic annotation tools, and (ii) whose results comply with OntoTag’s annotation scheme. Sub-goal 3.1: Specification of the decanting processes that allow for the classification and separation, according to their corresponding levels, of the results of the linguistic tools annotating at several different levels. Sub-goal 3.2: Specification of the standardisation processes that allow (a) complying with the standardisation requirements of OntoTag’s annotation scheme, as well as (b) combining the results of those linguistic tools that share some level of annotation. Sub-goal 3.3: Specification of the merging processes that allow for the combination of the output annotations and the interoperation of those linguistic tools that share some level of annotation. Sub-goal 3.4: Specification of the merge processes that allow for the integration of the results and the interoperation of those tools performing their annotations at different levels. Goal 4: Generation of OntoTagger’s schema, a concrete instance of OntoTag’s abstract scheme for a concrete set of linguistic annotations. These linguistic annotations result from the tools and the resources available in the research group, namely • Bitext’s DataLexica (http://www.bitext.com/EN/datalexica.asp), • LACELL’s (POS) tagger (http://www.um.es/grupos/grupo-lacell/quees.php), • Connexor’s FDG (http://www.connexor.eu/technology/machinese/glossary/fdg/), and • EuroWordNet (Vossen et al., 1998). This schema should help evaluate OntoTag’s underlying hypotheses, stated below. Consequently, it should implement, at least, those levels of the abstract scheme dealing with the annotations of the set of tools considered in this implementation. This includes the morphosyntactic, the syntactic and the semantic levels. Goal 5: Implementation of OntoTagger’s configuration, a concrete instance of OntoTag’s abstract architecture for this set of linguistic tools and annotations. This configuration (1) had to use the schema generated in the previous goal; and (2) should help support or refute the hypotheses of this work as well (see the next section). Sub-goal 5.1: Implementation of the decanting processes that facilitate the classification and separation of the results of those linguistic resources that provide annotations at several different levels (on the one hand, LACELL’s tagger operates at the morphosyntactic level and, minimally, also at the semantic level; on the other hand, FDG operates at the morphosyntactic and the syntactic levels and, minimally, at the semantic level as well). Sub-goal 5.2: Implementation of the standardisation processes that allow (i) specifying the results of those linguistic tools that share some level of annotation according to the requirements of OntoTagger’s schema, as well as (ii) combining these shared level results. In particular, all the tools selected perform morphosyntactic annotations and they had to be conveniently combined by means of these processes. Sub-goal 5.3: Implementation of the merging processes that allow for the combination (and possibly the improvement) of the annotations and the interoperation of the tools that share some level of annotation (in particular, those relating the morphosyntactic level, as in the previous sub-goal). Sub-goal 5.4: Implementation of the merging processes that allow for the integration of the different standardised and combined annotations aforementioned, relating all the levels considered. Sub-goal 5.5: Improvement of the semantic level of this configuration by adding a named entity recognition, (sub-)classification and annotation subsystem, which also uses the named entities annotated to populate a domain ontology, in order to provide a concrete application of the present work in the two areas involved (the Semantic Web and Corpus Linguistics). 3. MAIN RESULTS: ASSESSMENT OF ONTOTAG’S UNDERLYING HYPOTHESES The model developed in the present thesis tries to shed some light on (i) whether linguistic annotation tools can effectively interoperate; (ii) whether their results can be combined and integrated; and, if they can, (iii) how they can, respectively, interoperate and be combined and integrated. Accordingly, several hypotheses had to be supported (or rejected) by the development of the OntoTag model and OntoTagger (its implementation). The hypotheses underlying OntoTag are surveyed below. Only one of the hypotheses (H.6) was rejected; the other five could be confirmed. H.1 The annotations of different levels (or layers) can be integrated into a sort of overall, comprehensive, multilayer and multilevel annotation, so that their elements can complement and refer to each other. • CONFIRMED by the development of: o OntoTag’s annotation scheme, o OntoTag’s annotation architecture, o OntoTagger’s (XML, RDF, OWL) annotation schemas, o OntoTagger’s configuration. H.2 Tool-dependent annotations can be mapped onto a sort of tool-independent annotations and, thus, can be standardised. • CONFIRMED by means of the standardisation phase incorporated into OntoTag and OntoTagger for the annotations yielded by the tools. H.3 Standardisation should ease: H.3.1: The interoperation of linguistic tools. H.3.2: The comparison, combination (at the same level and layer) and integration (at different levels or layers) of annotations. • H.3 was CONFIRMED by means of the development of OntoTagger’s ontology-based configuration: o Interoperation, comparison, combination and integration of the annotations of three different linguistic tools (Connexor’s FDG, Bitext’s DataLexica and LACELL’s tagger); o Integration of EuroWordNet-based, domain-ontology-based and named entity annotations at the semantic level. o Integration of morphosyntactic, syntactic and semantic annotations. H.4 Ontologies and Semantic Web technologies (can) play a crucial role in the standardisation of linguistic annotations, by providing consensual vocabularies and standardised formats for annotation (e.g., RDF triples). • CONFIRMED by means of the development of OntoTagger’s RDF-triple-based annotation schemas. H.5 The rate of errors introduced by a linguistic tool at a given level, when annotating, can be reduced automatically by contrasting and combining its results with the ones coming from other tools, operating at the same level. However, these other tools might be built following a different technological (stochastic vs. rule-based, for example) or theoretical (dependency vs. HPS-grammar-based, for instance) approach. • CONFIRMED by the results yielded by the evaluation of OntoTagger. H.6 Each linguistic level can be managed and annotated independently. • REJECTED: OntoTagger’s experiments and the dependencies observed among the morphosyntactic annotations, and between them and the syntactic annotations. In fact, Hypothesis H.6 was already rejected when OntoTag’s ontologies were developed. We observed then that several linguistic units stand on an interface between levels, belonging thereby to both of them (such as morphosyntactic units, which belong to both the morphological level and the syntactic level). Therefore, the annotations of these levels overlap and cannot be handled independently when merged into a unique multileveled annotation. 4. OTHER MAIN RESULTS AND CONTRIBUTIONS First, interoperability is a hot topic for both the linguistic annotation community and the whole Computer Science field. The specification (and implementation) of OntoTag’s architecture for the combination and integration of linguistic (annotation) tools and annotations by means of ontologies shows a way to make these different linguistic annotation tools and annotations interoperate in practice. Second, as mentioned above, the elements involved in linguistic annotation were formalised in a set (or network) of ontologies (OntoTag’s linguistic ontologies). • On the one hand, OntoTag’s network of ontologies consists of − The Linguistic Unit Ontology (LUO), which includes a mostly hierarchical formalisation of the different types of linguistic elements (i.e., units) identifiable in a written text; − The Linguistic Attribute Ontology (LAO), which includes also a mostly hierarchical formalisation of the different types of features that characterise the linguistic units included in the LUO; − The Linguistic Value Ontology (LVO), which includes the corresponding formalisation of the different values that the attributes in the LAO can take; − The OIO (OntoTag’s Integration Ontology), which  Includes the knowledge required to link, combine and unite the knowledge represented in the LUO, the LAO and the LVO;  Can be viewed as a knowledge representation ontology that describes the most elementary vocabulary used in the area of annotation. • On the other hand, OntoTag’s ontologies incorporate the knowledge included in the different standards and recommendations for linguistic annotation released so far, such as those developed within the EAGLES and the SIMPLE European projects or by the ISO/TC 37 committee: − As far as morphosyntactic annotations are concerned, OntoTag’s ontologies formalise the terms in the EAGLES (1996a) recommendations and their corresponding terms within the ISO Morphosyntactic Annotation Framework (ISO/MAF, 2008) standard; − As for syntactic annotations, OntoTag’s ontologies incorporate the terms in the EAGLES (1996b) recommendations and their corresponding terms within the ISO Syntactic Annotation Framework (ISO/SynAF, 2010) standard draft; − Regarding semantic annotations, OntoTag’s ontologies generalise and extend the recommendations in EAGLES (1996a; 1996b) and, since no stable standards or standard drafts have been released for semantic annotation by ISO/TC 37 yet, they incorporate the terms in SIMPLE (2000) instead; − The terms coming from all these recommendations and standards were supplemented by those within the ISO Data Category Registry (ISO/DCR, 2008) and also of the ISO Linguistic Annotation Framework (ISO/LAF, 2009) standard draft when developing OntoTag’s ontologies. Third, we showed that the combination of the results of tools annotating at the same level can yield better results (both in precision and in recall) than each tool separately. In particular, 1. OntoTagger clearly outperformed two of the tools integrated into its configuration, namely DataLexica and FDG in all the combination sub-phases in which they overlapped (i.e. POS tagging, lemma annotation and morphological feature annotation). As far as the remaining tool is concerned, i.e. LACELL’s tagger, it was also outperformed by OntoTagger in POS tagging and lemma annotation, and it did not behave better than OntoTagger in the morphological feature annotation layer. 2. As an immediate result, this implies that a) This type of combination architecture configurations can be applied in order to improve significantly the accuracy of linguistic annotations; and b) Concerning the morphosyntactic level, this could be regarded as a way of constructing more robust and more accurate POS tagging systems. Fourth, Semantic Web annotations are usually performed by humans or else by machine learning systems. Both of them leave much to be desired: the former, with respect to their annotation rate; the latter, with respect to their (average) precision and recall. In this work, we showed how linguistic tools can be wrapped in order to annotate automatically Semantic Web pages using ontologies. This entails their fast, robust and accurate semantic annotation. As a way of example, as mentioned in Sub-goal 5.5, we developed a particular OntoTagger module for the recognition, classification and labelling of named entities, according to the MUC and ACE tagsets (Chinchor, 1997; Doddington et al., 2004). These tagsets were further specified by means of a domain ontology, namely the Cinema Named Entities Ontology (CNEO). This module was applied to the automatic annotation of ten different web pages containing cinema reviews (that is, around 5000 words). In addition, the named entities annotated with this module were also labelled as instances (or individuals) of the classes included in the CNEO and, then, were used to populate this domain ontology. • The statistical results obtained from the evaluation of this particular module of OntoTagger can be summarised as follows. On the one hand, as far as recall (R) is concerned, (R.1) the lowest value was 76,40% (for file 7); (R.2) the highest value was 97, 50% (for file 3); and (R.3) the average value was 88,73%. On the other hand, as far as the precision rate (P) is concerned, (P.1) its minimum was 93,75% (for file 4); (R.2) its maximum was 100% (for files 1, 5, 7, 8, 9, and 10); and (R.3) its average value was 98,99%. • These results, which apply to the tasks of named entity annotation and ontology population, are extraordinary good for both of them. They can be explained on the basis of the high accuracy of the annotations provided by OntoTagger at the lower levels (mainly at the morphosyntactic level). However, they should be conveniently qualified, since they might be too domain- and/or language-dependent. It should be further experimented how our approach works in a different domain or a different language, such as French, English, or German. • In any case, the results of this application of Human Language Technologies to Ontology Population (and, accordingly, to Ontological Engineering) seem very promising and encouraging in order for these two areas to collaborate and complement each other in the area of semantic annotation. Fifth, as shown in the State of the Art of this work, there are different approaches and models for the semantic annotation of texts, but all of them focus on a particular view of the semantic level. Clearly, all these approaches and models should be integrated in order to bear a coherent and joint semantic annotation level. OntoTag shows how (i) these semantic annotation layers could be integrated together; and (ii) they could be integrated with the annotations associated to other annotation levels. Sixth, we identified some recommendations, best practices and lessons learned for annotation standardisation, interoperation and merge. They show how standardisation (via ontologies, in this case) enables the combination, integration and interoperation of different linguistic tools and their annotations into a multilayered (or multileveled) linguistic annotation, which is one of the hot topics in the area of Linguistic Annotation. And last but not least, OntoTag’s annotation scheme and OntoTagger’s annotation schemas show a way to formalise and annotate coherently and uniformly the different units and features associated to the different levels and layers of linguistic annotation. This is a great scientific step ahead towards the global standardisation of this area, which is the aim of ISO/TC 37 (in particular, Subcommittee 4, dealing with the standardisation of linguistic annotations and resources).

A Language to Formalize and to Operationalize Problem-solving Strategies of Structured Knowledge Models

This paper describes a proposal of a language called Link which has been designed to formalize and operationalize problem solving strategies. This language is used within a software environment called KSM (Knowledge Structure Manager) which helps developers in formulating and operationalizing structured knowledge models. The paper presents both its syntax and dynamics, and gives examples of well-known problem-solving strategies of reasoning formulated using this language.

A flexible (C)LP-based approach to the analysis of object-oriented programs

Static analyses of object-oriented programs usually rely on intermediate representations that respect the original semantics while having a more uniform and basic syntax. Most of the work involving object-oriented languages and abstract interpretation usually omits the description of that language or just refers to the Control Flow Graph(CFG) it represents. However, this lack of formalization on one hand results in an absence of assurances regarding the correctness of the transformation and on the other it typically strongly couples the analysis to the source language. In this work we present a framework for analysis of object-oriented languages in which in a first phase we transform the input program into a representation based on Horn clauses. This allows on one hand proving the transformation correct attending to a simple condition and on the other being able to apply an existing analyzer for (constraint) logic programming to automatically derive a safe approximation of the semantics of the original program. The approach is flexible in the sense that the first phase decouples the analyzer from most languagedependent features, and correct because the set of Horn clauses returned by the transformation phase safely approximates the standard semantics of the input program. The resulting analysis is also reasonably scalable due to the use of mature, modular (C)LP-based analyzers. The overall approach allows us to report results for medium-sized programs.

A generic persistence model for CLP systems (and two useful implementations)

This paper describes a model of persistence in (C)LP languages and two different and practically very useful ways to implement this model in current systems. The fundamental idea is that persistence is a characteristic of certain dynamic predicates (Le., those which encapsulate state). The main effect of declaring a predicate persistent is that the dynamic changes made to such predicates persist from one execution to the next one. After proposing a syntax for declaring persistent predicates, a simple, file-based implementation of the concept is presented and some examples shown. An additional implementation is presented which stores persistent predicates in an external datábase. The abstraction of the concept of persistence from its implementation allows developing applications which can store their persistent predicates alternatively in files or databases with only a few simple changes to a declaration stating the location and modality used for persistent storage. The paper presents the model, the implementation approach in both the cases of using files and relational databases, a number of optimizations of the process (using information obtained from static global analysis and goal clustering), and performance results from an implementation of these ideas.

A generic persistence model for CLP systems

This paper describes a model of persistence in (C)LP languages and two different and practically very useful ways to implement this model in current systems. The fundamental idea is that persistence is a characteristic of certain dynamic predicates (i.e., those which encapsulate state). The main effect of declaring a predicate persistent is that the dynamic changes made to such predicates persist from one execution to the next one. After proposing a syntax for declaring persistent predicates, a simple, file-based implementation of the concept is presented and some examples shown. An additional implementation is presented which stores persistent predicates in an external database. The abstraction of the concept of persistence from its implementation allows developing applications which can store their persistent predicates alternatively in files or databases with only a few simple changes to a declaration stating the location and modality used for persistent storage. The paper presents the model, the implementation approach in both the cases of using files and relational databases, a number of optimizations of the process (using information obtained from static global analysis and goal clustering), and performance results from an implementation of these ideas.

A new module system for prolog

It is now widely accepted that separating programs into modules is useful in program development and maintenance. While many Prolog implementations include useful module systems, we argüe that these systems can be improved in a number of ways, such as, for example, being more amenable to effective global analysis and transformation and allowing sepárate compilation or sensible creation of standalone executables. We discuss a number of issues related to the design of such an improved module system for Prolog and propose some novel solutions. Based on this, we present the choices made in the Ciao module system, which has been designed to meet a number of objectives: allowing sepárate compilation, extensibility in features and in syntax, amenability to modular global analysis and transformation, enhanced error detection, support for meta-programming and higher-order, compatibility to the extent possible with official and de-facto standards, etc.

Semantic Heterogeneity Issues on the Web

The Semantic Web is an extension of the traditional Web in which meaning of information is well defined, thus allowing a better interaction between people and computers. To accomplish its goals, mechanisms are required to make explicit the semantics of Web resources, to be automatically processed by software agents (this semantics being described by means of online ontologies). Nevertheless, issues arise caused by the semantic heterogeneity that naturally happens on the Web, namely redundancy and ambiguity. For tackling these issues, we present an approach to discover and represent, in a non-redundant way, the intended meaning of words in Web applications, while taking into account the (often unstructured) context in which they appear. To that end, we have developed novel ontology matching, clustering, and disambiguation techniques. Our work is intended to help bridge the gap between syntax and semantics for the Semantic Web construction.

A new module system for prolog

It is now widely accepted that separating programs into modules has proven very useful in program development and maintenance. While many Prolog implementations include useful module systems, we feel that these systems can be improved in a number of ways, such as, for example, being more amenable to effective global analysis and allowing sepárate compilation or sensible creation of standalone executables. We discuss a number of issues related to the design of such an improved module system for Prolog. Based on this, we present the choices made in the Ciao module system, which has been designed to meet a number of objectives: allowing sepárate compilation, extensibility in features and in syntax, amenability to modular global analysis, etc.

O'CIAO an object oriented programming model using CIAO Prolog

There have been several previous proposals for the integration of Object Oriented Programming features into Logic Programming, resulting in much support theory and several language proposals. However, none of these proposals seem to have made it into the mainstream. Perhaps one of the reasons for these is that the resulting languages depart too much from the standard logic programming languages to entice the average Prolog programmer. Another reason may be that most of what can be done with object-oriented programming can already be done in Prolog through the meta- and higher-order programming facilities that the language includes, albeit sometimes in a more cumbersome way. In light of this, in this paper we propose an alternative solution which is driven by two main objectives. The first one is to include only those characteristics of object-oriented programming which are cumbersome to implement in standard Prolog systems. The second one is to do this in such a way that there is minimum impact on the syntax and complexity of the language, i.e., to introduce the minimum number of new constructs, declarations, and concepts to be learned. Finally, we would like the implementation to be as straightforward as possible, ideally based on simple source to source expansions.

Implicaciones de la accesibilidad configuracional en la movilidad peatonal. El caso de Madrid = The implications of configurational accessibility in pedestrian mobility. The case of Madrid

- Resumen La hipótesis que anima esta tesis doctoral es que algunas de las características del entorno urbano, en particular las que describen la accesibilidad de su red de espacio público, podrían estar relacionadas con la proporción de viajes a pie o reparto modal, que tiene cada zona o barrio de Madrid. Uno de los puntos de partida de dicha hipótesis que el entorno urbano tiene una mayor influencia sobre los viaje a pie que en sobre otros modos de transporte, por ejemplo que en los viajes de bicicleta o en transporte público; y es que parece razonable suponer que estos últimos van a estar más condicionadas por ejemplo por la disponibilidad de vías ciclistas, en el primer caso, o por la existencia de un servicio fiable y de calidad, en el segundo. Otra de las motivaciones del trabajo es que la investigación en este campo de la accesibilidad del espacio público, en concreto la denominada “Space Syntax”, ha probado en repetidas ocasiones la influencia de la red de espacio público en cómo se distribuye la intensidad del tráfico peatonal por la trama urbana, pero no se han encontrado referencias de la influencia de dicho elemento sobre el reparto modal. De acuerdo con la hipótesis y con otros trabajos anteriores se propone una metodología basada en el análisis empírico y cuantitativo. Su objetivo es comprobar si la red de espacio público, independientemente de otras variables como los usos del suelo, incluso de las variables de ajenas entorno no construido, como las socioeconómicas, está o no relacionada estadísticamente con la proporción de peatones viajes en las zonas urbanas. Las técnicas estadísticas se utilizan para comprobar sistemáticamente la asociación de las variables del entorno urbano, denominadas variables independientes, con el porcentaje de viajes a pie, la variable dependiente. En términos generales, la metodología es similar a la usada en otros trabajos en este campo como los de CERVERÓ y KOCKLEMAN (1997), CERVERÓ y DUNCAN (2003), o para los que se utilizan principalmente en la revisión general de TRB (2005) o, más recientemente, en ZEGRAS (2006) o CHATMAN (2009). Otras opciones metodológicas, como los métodos de preferencias declaradas (ver LOUVIERE, HENSHER y SWAIT, 2000) o el análisis basado en agentes (PENN & TURNER, 2004) fueron descartados, debido a una serie de razones, demasiado extensas para ser descritas aquí. El caso de estudio utilizado es la zona metropolitana de Madrid, abarcándola hasta la M-50, es decir en su mayor parte, con un tamaño aproximado de 31x34 Km y una población de 4.132.820 habitantes (aproximadamente el 80% de la población de la región). Las principales fuentes de datos son la Encuesta Domiciliaria de Movilidad de 2004 (EDM04), del Consorcio Regional de Transportes de Madrid que es la última disponible (muestra: > 35.000 familias,> 95.000 personas), y un modelo espacial del área metropolitana, integrando el modelo para calcular los índices de Space Syntax y un Sistema de Información Geográfica (SIG). La unidad de análisis, en este caso las unidades espaciales, son las zonas de transporte (con una población media de 7.063 personas) y los barrios (con una población media de 26.466 personas). Las variables del entorno urbano son claramente el centro del estudio. Un total de 20 índices (de 21) se seleccionan de entre los más relevantes encontrados en la revisión de la producción científica en este campo siendo que, al mismo tiempo, fueran accesibles. Nueve de ellos se utilizan para describir las características de los usos del suelo, mientras que otros once se usan para describir la red de espacios públicos. Estos últimos incluyen las variables de accesibilidad configuracional, que son, como se desprende de su título, el centro del estudio propuesto. La accesibilidad configuracional es un tipo especial de accesibilidad que se basa en la configuración de la trama urbana, según esta fue definida por HILLIER (1996), el autor de referencia dentro de esta línea de investigación de Space Syntax. Además se incluyen otras variables de la red de espacio público más habituales en los estudios de movilidad, y que aquí se denominan características geométricas de los elementos de la red, tales como su longitud, tipo de intersección, conectividad, etc. Por último se incluye además una variable socioeconómica, es decir ajena al entorno urbano, para evaluar la influencia de los factores externos, pues son varios los que pueden tener un impacto en la decisión de caminar (edad, género, nivel de estudios, ingresos, tasa de motorización, etc.). La asociación entre las variables se han establecido usando análisis de correlación (bivariante) y modelos de análisis multivariante. Las primeras se calculan entre por pares entre cada una de las 21 variables independientes y la dependiente, el porcentaje de viajes a pie. En cuanto a los segundos, se han realizado tres tipos de estudios: modelo multivariante general lineal, modelo multivariante general curvilíneo y análisis discriminante. Todos ellos son capaces de generar modelos de asociación entre diversas variables, pudiéndose de esta manera evaluar con bastante precisión en qué medida cada modelo reproduce el comportamiento de la variable dependiente, y además, el peso o influencia de cada variable en el modelo respecto a las otras. Los resultados fundamentales del estudio se expresan en dos modelos finales alternativos, que demuestran tener una significativa asociación con el porcentaje de viajes a pie (R2 = 0,6789, p <0,0001), al explicar las dos terceras partes de su variabilidad. En ellos, y en general en todo el estudio realizado, se da una influencia constante de tres índices en particular, que quedan como los principales. Dos de ellos, de acuerdo con muchos de los estudios previos, corresponden a la densidad y la mezcla de usos del suelo. Pero lo más novedoso de los resultados obtenidos es que el tercero es una medida de la accesibilidad de la red de espacio público, algo de lo que no había referencias hasta ahora. Pero, ¿cuál es la definición precisa y el peso relativo de cada uno en el modelo, es decir, en la variable independiente? El de mayor peso en la mayor parte de los análisis realizados es el índice de densidad total (n º residentes + n º puestos de trabajo + n º alumnos / Ha). Es decir, una densidad no sólo de población, sino que incluye algunas de las actividades más importantes que pueden darse una zona para generar movilidad a pie. El segundo que mayor peso adquiere, llegando a ser el primero en alguno de los análisis estadísticos efecturados, es el índice de accesibuilidad configuracional denominado integración de radio 5. Se trata de una medida de la accesibilidad de la zona, de su centralidad, a la escala de, más un menor, un distrito o comarca. En cuanto al tercero, obtiene una importancia bastante menor que los anteriores, y es que representa la mezcla de usos. En concreto es una medida del equilibrio entre los comercios especializados de venta al por menor y el número de residentes (n º de tiendas especializadas en alimentación, bebidas y tabaco / n º de habitantes). Por lo tanto, estos resultados confirman buena parte de los de estudios anteriores, especialmente los relativas a los usos del suelo, pero al mismo tiempo, apuntan a que la red de espacio público podría tener una influir mayor de la comprobada hasta ahora en la proporción de peatones sobre el resto de modos de transportes. Las razones de por qué esto puede ser así, se discuten ampliamente en las conclusiones. Finalmente se puede precisar que dicha conclusión principal se refiere a viajes de una sola etapa (no multimodales) que se dan en los barrios y zonas del área metropolitana de Madrid. Por supuesto, esta conclusión tiene en la actualidad, una validez limitada, ya que es el resultado de un solo caso — Abstract The research hypothesis for this Ph.D. Thesis is that some characteristics of the built environment, particularly those describing the accessibility of the public space network, could be associated with the proportion of pedestrians in all trips (modal split), found in the different parts of a city. The underlying idea is that walking trips are more sensitive to built environment than those by other transport modes, such as for example those by bicycle or by public transport, which could be more conditioned by, e.g. infrastructure availability or service frequency and quality. On the other hand, it has to be noted that the previously research on this field, in particular within Space Syntax’s where this study can be referred, have tested similar hypothesis using pedestrian volumes as the dependent variable, but never against modal split. According to such hypothesis, research methodology is based primarily on empirical quantitative analysis, and it is meant to be able to assess whether public space network, no matter other built environment and non-built environment variables, could have a relationship with the proportion of pedestrian trips in urban areas. Statistical techniques are used to check the association of independent variables with the percentage of walking in all trips, the dependent one. Broadly speaking this methodology is similar to that of previous studies in the field such as CERVERO&KOCKLEMAN (1997), CERVERO & DUNCAN (2003), or to those used mainly in the general review of T.R.B. (2005) or, more recently in ZEGRAS (2006) or CHATMAN (2009). Other methodological options such as stated choice methods (see LOUVIERE, HENSHER & SWAIT, 2000) or agent based analysis (PENN & TURNER, 2004), were discarded, due to a number of reasons, too long to be described here. The case study is not the entire Madrid’s metropolitan area, but almost (4.132.820 inhabitants, about 80% of region´s population). Main data sources are the Regional Mobility Home Based Survey 2004 (EDM04), which is the last available (sample: >35.000 families, > 95.000 individuals), and a spatial model of the metropolitan area, developed using Space Syntax and G.I.S. techniques. The analysis unit, in this case spatial units, are both transport zones (mean population = 7.063) and neighborhoods (mean population = 26.466). The variables of the built environment are clearly the core of the study. A total of 20 (out of 21) are selected from among those found in the literature while, at the same time, being accessible. Nine out of them are used to describe land use characteristics while another eleven describe the network of public spaces. Latter ones include configurational accessibility or Space Syntax variables. This is a particular sort of accessibility related with the concept of configuration, by HILLIER (1996), one of the main authors of Space Syntax, But it also include more customary variables used in mobility research to describe the urban design or spatial structure (here public space network), which here are called geometric characteristics of the such as its length, type of intersection, conectivity, density, etc. Finally a single socioeconomic variable was included in order to assess the influence non built environment factors that also may have an impact on walking (age, income, motorization rate, etc.). The association among variables is worked out using bi-variate correlation analysis and multivariate-analysis. Correlations are calculated among the 21 independent variables and the dependent one, the percentage of walking trips. Then, three types of multi-variate studies are run: general linear, curvilinear and discriminant multi-variate analysis. The latter are fully capable of generating complex association models among several variables, assessing quite precisely to what extent each model reproduces the behavior of the dependent variable, and also the weight or influence of each variable in the model. This study’s results show a consistent influence of three particular indexes in the two final alternative models of the multi-variate study (best, R2=0,6789, p<0,0000). Not surprisingly, two of them correspond to density and mix of land uses. But perhaps more interesting is that the third one is a measure of the accessibility of the public space network, a variable less important in the literature up to now. Additional precisions about them and their relative weight could also be of some interest. The density index is not only about population but includes most important activities in an area (nº residents + nº jobs+ nº students/Ha). The configurational index (radius 5 integration) is a measure of the accessibility of the area, i.e. centrality, at the scale of, more a less, a district. Regarding the mix of land uses index, this one is a measure of the balance between retail, in fact local basic retail, and the number of residents (nº of convenience shops / nº of residents). Referring to their weights, configurational index (radius 5 integration) gets the higher standardized coefficient of the final equation. However, in the final equations, there are a higher number of indexes coming from the density or land use mix categories than from public space network enter. Therefore, these findings seem to support part of the field’s knowledge, especially those concerning land uses, but at the same time they seem to bring in the idea that the configuration of the urban grid could have an influence in the proportion of walkers (as a part of total trips on any transport mode) that do single journey trips in the neighborhoods of Madrid, Spain. Of course this conclusion has, at present, a limited validity since it’s the result of a single case. The reasons of why this can be so, are discussed in the last part of the thesis.