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

Lightweight Tag-Aware Personalized Recommendation on the Social Web Using Ontological Similarity

With the rapid growth of social tagging systems, many research efforts are being put intopersonalized search and recommendation using social tags (i.e., folksonomies). As users can freely choosetheir own vocabulary, social tags can be very ambiguous (for instance, due to the use of homonymsor synonyms). Machine learning techniques (such as clustering and deep neural networks) are usuallyapplied to overcome this tag ambiguity problem. However, the machine-learning-based solutions alwaysneed very powerful computing facilities to train recommendation models from a large amount of data,so they are inappropriate to be used in lightweight recommender systems. In this work, we propose anontological similarity to tackle the tag ambiguity problem without the need of model training by usingcontextual information. The novelty of this ontological similarity is that it first leverages external domainontologies to disambiguate tag information, and then semantically quantifies the relevance between userand item profiles according to the semantic similarity of the matching concepts of tags in the respectiveprofiles. Our experiments show that the proposed ontological similarity is semantically more accurate thanthe state-of-the-art similarity metrics, and can thus be applied to improve the performance of content-based tag-aware personalized recommendation on the Social Web. Consequently, as a model-training-freesolution, ontological similarity is a good disambiguation choice for lightweight recommender systems anda complement to machine-learning-based recommendation solutions.Fil: Xu, Zhenghua. University of Oxford; Reino UnidoFil: Tifrea-Marciuska, Oana. Bloomberg; Reino UnidoFil: Lukasiewicz, Thomas. University of Oxford; Reino UnidoFil: Martinez, Maria Vanina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Simari, Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Chen, Cheng. China Academy of Electronics and Information Technology; Chin

Personalised search for the Social Semantic Web

Recently, the Web has been changing more and more to what is called the Social Semantic Web. As a consequence, the ranking of search results no longer depends solely on the structure of the interconnections among Web pages. In my research, I argue that such ranking can be based on user preferences from the Social Web, and on ontological background knowledge from the Semantic Web. Therefore, I combine preference representation languages with Semantic Web technologies. There is some related research in database community that had dedicated some time to integrate preferences in database queries. However, one cannot directly use the ideas from databases, as we additionally have ontological knowledge, which may introduce unknown values, so-called nulls. Therefore, I need to define the exact semantics and check their feasibility for this context. In my thesis, as a first step towards closing the gap between the Semantic Web, databases, and preferences, I introduce families of expressive extensions of Datalog&plusmn; with preferences as new paradigms for query answering over ontologies. I first define the syntax and semantic of the proposed frameworks, then propose top-k query answering algorithms under user preferences in semantic data for different types of queries and preference models. Each of the proposed frameworks comes with advantages and disadvantages; therefore, I provide formal properties of my algorithms and empirical experiments on the performance and quality of my results. Furthermore, I explore the combination of my framework with uncertainty and the generalisation to the preferences of a group of users, where I analyse properties of my algorithms related with social choice theory.</p

Personalised search for the Social Semantic Web

Recently, the Web has been changing more and more to what is called the Social Semantic Web. As a consequence, the ranking of search results no longer depends solely on the structure of the interconnections among Web pages. In my research, I argue that such ranking can be based on user preferences from the Social Web, and on ontological background knowledge from the Semantic Web. Therefore, I combine preference representation languages with Semantic Web technologies. There is some related research in database community that had dedicated some time to integrate preferences in database queries. However, one cannot directly use the ideas from databases, as we additionally have ontological knowledge, which may introduce unknown values, so-called nulls. Therefore, I need to define the exact semantics and check their feasibility for this context. In my thesis, as a first step towards closing the gap between the Semantic Web, databases, and preferences, I introduce families of expressive extensions of Datalog± with preferences as new paradigms for query answering over ontologies. I first define the syntax and semantic of the proposed frameworks, then propose top-k query answering algorithms under user preferences in semantic data for different types of queries and preference models. Each of the proposed frameworks comes with advantages and disadvantages; therefore, I provide formal properties of my algorithms and empirical experiments on the performance and quality of my results. Furthermore, I explore the combination of my framework with uncertainty and the generalisation to the preferences of a group of users, where I analyse properties of my algorithms related with social choice theory.</p

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

Ontology−Based Query Answering with Group Preferences

The Web has recently been evolving into a system that is in many ways centered on social interactions and is now more and more becoming what is called the Social Semantic Web. One of the many implications of such an evolution is that the ranking of search results no longer depends solely on the structure of the interconnections among Web pages — instead, the social components must also come into play. In this paper, we argue that such rankings can be based on ontological background knowledge and on user preferences. Another aspect that has become increasingly important in recent times is that of uncertainty management, since uncertainty can arise due to many uncontrollable factors. To combine these two aspects, we propose extensions of the Datalog+/– family of ontology languages that both allow for the management of partially ordered preferences of groups of users as well as uncertainty, which is represented via a probabilistic model. We focus on answering k-rank queries in this context, presenting different strategies to compute group preferences as an aggregation of the preferences of a collection of single users. We also study merging operators that are useful for combining the preferences of the users with those induced by the values obtained from the probabilistic model. We then provide algorithms to answer k-rank queries for DAQs (disjunctions of atomic queries) under these group preferences and uncertainty that generalizes top-k queries based on the iterative computation of classical skyline answers. We show that such DAQ answering in Datalog+/– can be done in polynomial time in the data complexity, under certain reasonable conditions, as long as query answering can also be done in polynomial time (in the data complexity) in the underlying classical ontology. Finally, we present a prototype implementation of the query answering system, as well as experimental results (on the running time of our algorithms and the quality of their results) obtained from real-world ontological data and preference models, derived from information gathered from real users, showing in particular that our approach is feasible in practice

Preference-Based Query Answering in Probabilistic Datalog+/– Ontologies

The incorporation of preferences into information systems, such as databases, has recently seen a surge in interest, mainly fueled by the revolution in Web data availability. Modeling the preferences of a user on the Web has also increasingly become appealing to many companies since the explosion of popularity of social media. The other surge in interest is in modeling uncertainty in these domains, since uncertainty can arise due to many uncontrollable factors. In this paper, we propose an extension of the Datalog+/- family of ontology languages with two models: one representing user preferences and one representing the (probabilistic) uncertainty with which inferences are made. Assuming that more probable answers are in general more preferable, one asks how to rank answers to a user´s queries, since the preference model may be in conflict with the preferences induced by the probabilistic model, the need thus arises for preference combination operators. We propose four specific operators and study their semantic and computational properties. We also provide an algorithm for ranking answers based on the iteration of the well-known skyline answers to a query and show that, under certain conditions, it runs in polynomial time in the data complexity. Furthermore, we report on an implementation and experimental results.Fil: Lukasiewicz, Thomas. University of Oxford; Reino UnidoFil: Martinez, Maria Vanina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. University of Oxford; Reino UnidoFil: Simari, Gerardo I.. University of Oxford; Reino UnidoFil: Tifrea Marciuska, Oana. University of Oxford; Reino Unid

Group Preferences for Query Answering in Datalog+⁄− Ontologies

In the recent years, the Web has been changing more and more towards the so-called Social Semantic Web. Rather than being based on the link structure between Web pages, the ranking of search results in the Social Semantic Web needs to be based on something new. We believe that it can be based on ontological background knowledge and on user preferences. In this paper, we thus propose an extension of the Datalog+⁄− ontology language that allows for dealing with partially ordered preferences of groups of users. We focus on answering k-rank queries in this context. In detail, we present different strategies to compute group preferences as an aggregation of the preferences of a collection of single users. We then provide algorithms to answer k-rank queries for DAQs (disjunctions of atomic queries) under these group preferences. We show that such DAQ answering in Datalog+⁄− can be done in polynomial time in the data complexity, as long as query answering can also be done in polynomial time (in the data complexity) in the underlying classical ontology.</p

Query Answering in Probabilistic Datalog+⁄− Ontologies under Group Preferences

In the recent years, the Web has been changing more and more towards the so-called Social Semantic Web. Rather than being based on the link structure between Web pages, the ranking of search results in the Social Semantic Web needs to be based on something new - we believe that it can be based on user preferences and underlying ontological knowledge. Modeling uncertainty is also playing an increasingly important role in these domains, since uncertainty can arise due to many uncontrollable factors. In this paper, we propose an extension of the Datalog+⁄− ontology language with a model for representing preferences of groups of users and a model for representing the (probabilistic) uncertainty in the domain. Assuming that more probable answers are more preferable, this raises the question of how to rank query results, since the preferences of single users may be in conflict both with the probability-based preferences as well as with each other. To this end, we propose preference merging and aggregation operators, respectively, and study their semantic and computational properties. Based on these operators, we provide algorithms for answering k-rank queries for DAQs (disjunctions of atomic queries), which generalize top-k queries based on the iterative computation of classical skyline answers, and show that, under certain reasonable conditions, they run in polynomial time in the data complexity