Preferential Query Answering in the Semantic Web with Possibilistic Networks

In this paper, we explore how ontological knowledge expressed via existential rules can be combined with possibilistic networks (i) to represent qualitative preferences along with domain knowledge, and (ii) to realize preference-based answering of conjunctive queries (CQs). We call these combinations ontological possibilistic networks (OP-nets). We define skyline and k-rank answers to CQs under preferences and provide complexity (including data tractability) results for deciding consistency and CQ skyline membership for OP-nets. We show that our formalism has a lower complexity than a similar existing formalism

Multi modal multi-semantic image retrieval

PhDThe rapid growth in the volume of visual information, e.g. image, and video can overwhelm users’ ability to find and access the specific visual information of interest to them. In recent years, ontology knowledge-based (KB) image information retrieval techniques have been adopted into in order to attempt to extract knowledge from these images, enhancing the retrieval performance. A KB framework is presented to promote semi-automatic annotation and semantic image retrieval using multimodal cues (visual features and text captions). In addition, a hierarchical structure for the KB allows metadata to be shared that supports multi-semantics (polysemy) for concepts. The framework builds up an effective knowledge base pertaining to a domain specific image collection, e.g. sports, and is able to disambiguate and assign high level semantics to ‘unannotated’ images. Local feature analysis of visual content, namely using Scale Invariant Feature Transform (SIFT) descriptors, have been deployed in the ‘Bag of Visual Words’ model (BVW) as an effective method to represent visual content information and to enhance its classification and retrieval. Local features are more useful than global features, e.g. colour, shape or texture, as they are invariant to image scale, orientation and camera angle. An innovative approach is proposed for the representation, annotation and retrieval of visual content using a hybrid technique based upon the use of an unstructured visual word and upon a (structured) hierarchical ontology KB model. The structural model facilitates the disambiguation of unstructured visual words and a more effective classification of visual content, compared to a vector space model, through exploiting local conceptual structures and their relationships. The key contributions of this framework in using local features for image representation include: first, a method to generate visual words using the semantic local adaptive clustering (SLAC) algorithm which takes term weight and spatial locations of keypoints into account. Consequently, the semantic information is preserved. Second a technique is used to detect the domain specific ‘non-informative visual words’ which are ineffective at representing the content of visual data and degrade its categorisation ability. Third, a method to combine an ontology model with xi a visual word model to resolve synonym (visual heterogeneity) and polysemy problems, is proposed. The experimental results show that this approach can discover semantically meaningful visual content descriptions and recognise specific events, e.g., sports events, depicted in images efficiently. Since discovering the semantics of an image is an extremely challenging problem, one promising approach to enhance visual content interpretation is to use any associated textual information that accompanies an image, as a cue to predict the meaning of an image, by transforming this textual information into a structured annotation for an image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct types of information representation and modality, there are some strong, invariant, implicit, connections between images and any accompanying text information. Semantic analysis of image captions can be used by image retrieval systems to retrieve selected images more precisely. To do this, a Natural Language Processing (NLP) is exploited firstly in order to extract concepts from image captions. Next, an ontology-based knowledge model is deployed in order to resolve natural language ambiguities. To deal with the accompanying text information, two methods to extract knowledge from textual information have been proposed. First, metadata can be extracted automatically from text captions and restructured with respect to a semantic model. Second, the use of LSI in relation to a domain-specific ontology-based knowledge model enables the combined framework to tolerate ambiguities and variations (incompleteness) of metadata. The use of the ontology-based knowledge model allows the system to find indirectly relevant concepts in image captions and thus leverage these to represent the semantics of images at a higher level. Experimental results show that the proposed framework significantly enhances image retrieval and leads to narrowing of the semantic gap between lower level machinederived and higher level human-understandable conceptualisation

Multi-Attribute Decision Making using Weighted Description Logics

We introduce a framework based on Description Logics, which can be used to encode and solve decision problems in terms of combining inference services in DL and utility theory to represent preferences of the agent. The novelty of the approach is that we consider ABoxes as alternatives and weighted concept and role assertions as preferences in terms of possible outcomes. We discuss a relevant use case to show the benefits of the approach from the decision theory point of view

Query Answering in Ontologies under Preference Rankings

We present an ontological framework, based on preference rankings, that allows users to express their preferences between the knowledge explicitly available in the ontology. Using this formalism, the answers for a given query to an ontology can be ranked by preference, allowing users to retrieve the most preferred answers only. We provide a host of complexity results for the main computational tasks in this framework, for the general case, and for EL and DL-Litecore as underlying ontology languages

A multi-layered Bayesian network model for structured document retrieval

New standards in document representation, like for example SGML, XML, and MPEG-7, compel Information Retrieval to design and implement models and tools to index, retrieve and present documents according to the given document structure. The paper presents the design of an Information Retrieval system for multimedia structured documents, like for example journal articles, e-books, and MPEG-7 videos. The system is based on Bayesian Networks, since this class of mathematical models enable to represent and quantify the relations between the structural components of the document. Some preliminary results on the system implementation are also presented

A multi-layered Bayesian network model for structured document retrieval

New standards in document representation, like for example SGML, XML, and MPEG-7, compel Information Retrieval to design and implement models and tools to index, retrieve and present documents according to the given document structure. The paper presents the design of an Information Retrieval system for multimedia structured documents, like for example journal articles, e-books, and MPEG-7 videos. The system is based on Bayesian Networks, since this class of mathematical models enable to represent and quantify the relations between the structural components of the document. Some preliminary results on the system implementation are also presented

Personalized content retrieval in context using ontological knowledge

Personalized content retrieval aims at improving the retrieval process by taking into account the particular interests of individual users. However, not all user preferences are relevant in all situations. It is well known that human preferences are complex, multiple, heterogeneous, changing, even contradictory, and should be understood in context with the user goals and tasks at hand. In this paper, we propose a method to build a dynamic representation of the semantic context of ongoing retrieval tasks, which is used to activate different subsets of user interests at runtime, in a way that out-of-context preferences are discarded. Our approach is based on an ontology-driven representation of the domain of discourse, providing enriched descriptions of the semantics involved in retrieval actions and preferences, and enabling the definition of effective means to relate preferences and context

Expressive probabilistic description logics

AbstractThe work in this paper is directed towards sophisticated formalisms for reasoning under probabilistic uncertainty in ontologies in the Semantic Web. Ontologies play a central role in the development of the Semantic Web, since they provide a precise definition of shared terms in web resources. They are expressed in the standardized web ontology language OWL, which consists of the three increasingly expressive sublanguages OWL Lite, OWL DL, and OWL Full. The sublanguages OWL Lite and OWL DL have a formal semantics and a reasoning support through a mapping to the expressive description logics SHIF(D) and SHOIN(D), respectively. In this paper, we present the expressive probabilistic description logics P-SHIF(D) and P-SHOIN(D), which are probabilistic extensions of these description logics. They allow for expressing rich terminological probabilistic knowledge about concepts and roles as well as assertional probabilistic knowledge about instances of concepts and roles. They are semantically based on the notion of probabilistic lexicographic entailment from probabilistic default reasoning, which naturally interprets this terminological and assertional probabilistic knowledge as knowledge about random and concrete instances, respectively. As an important additional feature, they also allow for expressing terminological default knowledge, which is semantically interpreted as in Lehmann's lexicographic entailment in default reasoning from conditional knowledge bases. Another important feature of this extension of SHIF(D) and SHOIN(D) by probabilistic uncertainty is that it can be applied to other classical description logics as well. We then present sound and complete algorithms for the main reasoning problems in the new probabilistic description logics, which are based on reductions to reasoning in their classical counterparts, and to solving linear optimization problems. In particular, this shows the important result that reasoning in the new probabilistic description logics is decidable/computable. Furthermore, we also analyze the computational complexity of the main reasoning problems in the new probabilistic description logics in the general as well as restricted cases

Scalable statistical learning for relation prediction on structured data

Relation prediction seeks to predict unknown but potentially true relations by revealing missing relations in available data, by predicting future events based on historical data, and by making predicted relations retrievable by query. The approach developed in this thesis can be used for a wide variety of purposes, including to predict likely new friends on social networks, attractive points of interest for an individual visiting an unfamiliar city, and associations between genes and particular diseases. In recent years, relation prediction has attracted significant interest in both research and application domains, partially due to the increasing volume of published structured data and background knowledge. In the Linked Open Data initiative of the Semantic Web, for instance, entities are uniquely identified such that the published information can be integrated into applications and services, and the rapid increase in the availability of such structured data creates excellent opportunities as well as challenges for relation prediction. This thesis focuses on the prediction of potential relations by exploiting regularities in data using statistical relational learning algorithms and applying these methods to relational knowledge bases, in particular in Linked Open Data in particular. We review representative statistical relational learning approaches, e.g., Inductive Logic Programming and Probabilistic Relational Models. While logic-based reasoning can infer and include new relations via deduction by using ontologies, machine learning can be exploited to predict new relations (with some degree of certainty) via induction, purely based on the data. Because the application of machine learning approaches to relation prediction usually requires handling large datasets, we also discuss the scalability of machine learning as a solution to relation prediction, as well as the significant challenge posed by incomplete relational data (such as social network data, which is often much more extensive for some users than others). The main contribution of this thesis is to develop a learning framework called the Statistical Unit Node Set (SUNS) and to propose a multivariate prediction approach used in the framework. We argue that multivariate prediction approaches are most suitable for dealing with large, sparse data matrices. According to the characteristics and intended application of the data, the approach can be extended in different ways. We discuss and test two extensions of the approach--kernelization and a probabilistic method of handling complex n-ary relationships--in empirical studies based on real-world data sets. Additionally, this thesis contributes to the field of relation prediction by applying the SUNS framework to various domains. We focus on three applications: 1. In social network analysis, we present a combined approach of inductive and deductive reasoning for recommending movies to users. 2. In the life sciences, we address the disease gene prioritization problem. 3. In the recommendation system, we describe and investigate the back-end of a mobile app called BOTTARI, which provides personalized location-based recommendations of restaurants.Die Beziehungsvorhersage strebt an, unbekannte aber potenziell wahre Beziehungen vorherzusagen, indem fehlende Relationen in verfügbaren Daten aufgedeckt, zukünftige Ereignisse auf der Grundlage historischer Daten prognostiziert und vorhergesagte Relationen durch Anfragen abrufbar gemacht werden. Der in dieser Arbeit entwickelte Ansatz lässt sich für eine Vielzahl von Zwecken einschließlich der Vorhersage wahrscheinlicher neuer Freunde in sozialen Netzen, der Empfehlung attraktiver Sehenswürdigkeiten für Touristen in fremden Städten und der Priorisierung möglicher Assoziationen zwischen Genen und bestimmten Krankheiten, verwenden. In den letzten Jahren hat die Beziehungsvorhersage sowohl in Forschungs- als auch in Anwendungsbereichen eine enorme Aufmerksamkeit erregt, aufgrund des Zuwachses veröffentlichter strukturierter Daten und von Hintergrundwissen. In der Linked Open Data-Initiative des Semantischen Web werden beispielsweise Entitäten eindeutig identifiziert, sodass die veröffentlichten Informationen in Anwendungen und Dienste integriert werden können. Diese rapide Erhöhung der Verfügbarkeit strukturierter Daten bietet hervorragende Gelegenheiten sowie Herausforderungen für die Beziehungsvorhersage. Diese Arbeit fokussiert sich auf die Vorhersage potenzieller Beziehungen durch Ausnutzung von Regelmäßigkeiten in Daten unter der Verwendung statistischer relationaler Lernalgorithmen und durch Einsatz dieser Methoden in relationale Wissensbasen, insbesondere in den Linked Open Daten. Wir geben einen Überblick über repräsentative statistische relationale Lernansätze, z.B. die Induktive Logikprogrammierung und Probabilistische Relationale Modelle. Während das logikbasierte Reasoning neue Beziehungen unter der Nutzung von Ontologien ableiten und diese einbeziehen kann, kann maschinelles Lernen neue Beziehungen (mit gewisser Wahrscheinlichkeit) durch Induktion ausschließlich auf der Basis der vorliegenden Daten vorhersagen. Da die Verarbeitung von massiven Datenmengen in der Regel erforderlich ist, wenn maschinelle Lernmethoden in die Beziehungsvorhersage eingesetzt werden, diskutieren wir auch die Skalierbarkeit des maschinellen Lernens sowie die erhebliche Herausforderung, die sich aus unvollständigen relationalen Daten ergibt (z. B. Daten aus sozialen Netzen, die oft für manche Benutzer wesentlich umfangreicher sind als für Anderen). Der Hauptbeitrag der vorliegenden Arbeit besteht darin, ein Lernframework namens Statistical Unit Node Set (SUNS) zu entwickeln und einen im Framework angewendeten multivariaten Prädiktionsansatz einzubringen. Wir argumentieren, dass multivariate Vorhersageansätze am besten für die Bearbeitung von großen und dünnbesetzten Datenmatrizen geeignet sind. Je nach den Eigenschaften und der beabsichtigten Anwendung der Daten kann der Ansatz auf verschiedene Weise erweitert werden. In empirischen Studien werden zwei Erweiterungen des Ansatzes--ein kernelisierter Ansatz sowie ein probabilistischer Ansatz zur Behandlung komplexer n-stelliger Beziehungen-- diskutiert und auf realen Datensätzen untersucht. Ein weiterer Beitrag dieser Arbeit ist die Anwendung des SUNS Frameworks auf verschiedene Bereiche. Wir konzentrieren uns auf drei Anwendungen: 1. In der Analyse sozialer Netze stellen wir einen kombinierten Ansatz von induktivem und deduktivem Reasoning vor, um Benutzern Filme zu empfehlen. 2. In den Biowissenschaften befassen wir uns mit dem Problem der Priorisierung von Krankheitsgenen. 3. In den Empfehlungssystemen beschreiben und untersuchen wir das Backend einer mobilen App "BOTTARI", das personalisierte ortsbezogene Empfehlungen von Restaurants bietet

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports