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

Explaining differences between unaligned table snapshots

We study the problem of explaining differences between two snapshots of the same database table including record insertions, deletions and in particular record updates. Unlike existing alternatives, our solution induces transformation functions and does not require knowledge of the correct alignment between the record sets. This allows profiling snapshots of tables with unspecified or modified primary keys. In such a problem setting, there are always multiple explanations for the differences. Our goal is to find the simplest explanation. We propose to measure the complexity of explanations on the basis of minimum description length in order to formulate the task as an optimization problem. We show that the problem is NP-hard and propose a heuristic search algorithm to solve practical problem instances. We implement a prototype called Affidavit to assess the explanatory qualities of our approach in experiments based on different real-world data sets. We show that it can scale to both a large number of records and attributes and is able to reliably provide correct explanations under practical levels of modifications

Alignment Incoherence in Ontology Matching

Ontology matching is the process of generating alignments between ontologies. An alignment is a set of correspondences. Each correspondence links concepts and properties from one ontology to concepts and properties from another ontology. Obviously, alignments are the key component to enable integration of knowledge bases described by different ontologies. For several reasons, alignments contain often erroneous correspondences. Some of these errors can result in logical conflicts with other correspondences. In such a case the alignment is referred to as an incoherent alignment. The relevance of alignment incoherence and strategies to resolve alignment incoherence are in the center of this thesis. After an introduction to syntax and semantics of ontologies and alignments, the importance of alignment coherence is discussed from different perspectives. On the one hand, it is argued that alignment incoherence always coincides with the incorrectness of correspondences. On the other hand, it is demonstrated that the use of incoherent alignments results in severe problems for different types of applications. The main part of this thesis is concerned with techniques for resolving alignment incoherence, i.e., how to find a coherent subset of an incoherent alignment that has to be preferred over other coherent subsets. The underlying theory is the theory of diagnosis. In particular, two specific types of diagnoses, referred to as local optimal and global optimal diagnosis, are proposed. Computing a diagnosis is for two reasons a challenge. First, it is required to use different types of reasoning techniques to determine that an alignment is incoherent and to find subsets (conflict sets) that cause the incoherence. Second, given a set of conflict sets it is a hard problem to compute a global optimal diagnosis. In this thesis several algorithms are suggested to solve these problems in an efficient way. In the last part of this thesis, the previously developed algorithms are applied to the scenarios of - evaluating alignments by computing their degree of incoherence; - repairing incoherent alignments by computing different types of diagnoses; - selecting a coherent alignment from a rich set of matching hypotheses; - supporting the manual revision of an incoherent alignment. In the course of discussing the experimental results, it becomes clear that it is possible to create a coherent alignment without negative impact on the alignments quality. Moreover, results show that taking alignment incoherence into account has a positive impact on the precision of the alignment and that the proposed approach can help a human to save effort in the revision process

A Probabilistic Approach for Integrating Heterogeneous Knowledge Sources

Open Information Extraction (OIE) systems like Nell and ReVerb have achieved impressive results by harvesting massive amounts of machine-readable knowledge with minimal supervision. However, the knowledge bases they produce still lack a clean, explicit semantic data model. This, on the other hand, could be provided by full-fledged semantic networks like DBpedia or Yago, which, in turn, could benefit from the additional coverage provided by Web-scale IE. In this paper, we bring these two strains of research together, and present a method to align terms from Nell with instances in DBpedia. Our approach is unsupervised in nature and relies on two key components. First, we automatically acquire probabilistic type information for Nell terms given a set of matching hypotheses. Second, we view the mapping task as the statistical inference problem of finding the most likely coherent mapping – i.e., the maximum a posteriori (MAP) mapping – based on the outcome of the first component used as soft constraint. These two steps are highly intertwined: accordingly, we propose an approach that iteratively refines type acquisition based on the output of the mapping generator, and vice versa. Experimental results on gold-standard data indicate that our approach outperforms a strong baseline, and is able to produce ever-improving mappings consistently across iterations

On the Aggregation of Rules for Knowledge Graph Completion

Rule learning approaches for knowledge graph completion are efficient, interpretable and competitive to purely neural models. The rule aggregation problem is concerned with finding one plausibility score for a candidate fact which was simultaneously predicted by multiple rules. Although the problem is ubiquitous, as data-driven rule learning can result in noisy and large rulesets, it is underrepresented in the literature and its theoretical foundations have not been studied before in this context. In this work, we demonstrate that existing aggregation approaches can be expressed as marginal inference operations over the predicting rules. In particular, we show that the common Max-aggregation strategy, which scores candidates based on the rule with the highest confidence, has a probabilistic interpretation. Finally, we propose an efficient and overlooked baseline which combines the previous strategies and is competitive to computationally more expensive approaches.Comment: KLR Workshop@ICML202

Automating OAEI campaigns (first report)

trojahn2010cInternational audienceThis paper reports the first effort into integrating OAEI and SEALS evaluation campaigns. The SEALS project aims at providing standardized resources (software components, data sets, etc.) for automatically executing evaluations of typical semantic web tools, including ontology matching tools. A first version of the software infrastructure is based on the use of a web service interface wrapping the functionality of a matching tool to be evaluated. In this setting, the evaluation results can visualized and manipulated immediately in a direct feedback cycle. We describe how parts of the OAEI 2010 evaluation campaign have been integrated into this software infrastructure. In particular, we discuss technical and organizational aspects related to the use of the new technology for both participants and organizers of the OAEI

uDecide: A protégé plugin for multiattribute decision making

This paper introduces the Protege plugin uDecide. With the help of uDecide it is possible to solve multi-attribute decision making problems encoded in a straight forward extension of standard Description Logics. The formalism allows to specify background knowledge in terms of an ontology, while each attribute is represented as a weighted class expression. On top of such an approach one can compute the best choice (or the best k-choices) taking background knowledge into account in the appropriate way. We show how to implement the approach on top of existing semantic web technologies and demonstrate its benefits with the help of an interesting use case that illustrates how to convert an existing web resource into an expert system with the help of uDecide