Heuristic Ranking in Tightly Coupled Probabilistic Description Logics

The Semantic Web effort has steadily been gaining traction in the recent years. In particular,Web search companies are recently realizing that their products need to evolve towards having richer semantic search capabilities. Description logics (DLs) have been adopted as the formal underpinnings for Semantic Web languages used in describing ontologies. Reasoning under uncertainty has recently taken a leading role in this arena, given the nature of data found on theWeb. In this paper, we present a probabilistic extension of the DL EL++ (which underlies the OWL2 EL profile) using Markov logic networks (MLNs) as probabilistic semantics. This extension is tightly coupled, meaning that probabilistic annotations in formulas can refer to objects in the ontology. We show that, even though the tightly coupled nature of our language means that many basic operations are data-intractable, we can leverage a sublanguage of MLNs that allows to rank the atomic consequences of an ontology relative to their probability values (called ranking queries) even when these values are not fully computed. We present an anytime algorithm to answer ranking queries, and provide an upper bound on the error that it incurs, as well as a criterion to decide when results are guaranteed to be correct.Comment: Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012

Ranking Archived Documents for Structured Queries on Semantic Layers

Archived collections of documents (like newspaper and web archives) serve as important information sources in a variety of disciplines, including Digital Humanities, Historical Science, and Journalism. However, the absence of efficient and meaningful exploration methods still remains a major hurdle in the way of turning them into usable sources of information. A semantic layer is an RDF graph that describes metadata and semantic information about a collection of archived documents, which in turn can be queried through a semantic query language (SPARQL). This allows running advanced queries by combining metadata of the documents (like publication date) and content-based semantic information (like entities mentioned in the documents). However, the results returned by such structured queries can be numerous and moreover they all equally match the query. In this paper, we deal with this problem and formalize the task of "ranking archived documents for structured queries on semantic layers". Then, we propose two ranking models for the problem at hand which jointly consider: i) the relativeness of documents to entities, ii) the timeliness of documents, and iii) the temporal relations among the entities. The experimental results on a new evaluation dataset show the effectiveness of the proposed models and allow us to understand their limitation

Integrating and Ranking Uncertain Scientific Data

Mediator-based data integration systems resolve exploratory queries by joining data elements across sources. In the presence of uncertainties, such multiple expansions can quickly lead to spurious connections and incorrect results. The BioRank project investigates formalisms for modeling uncertainty during scientific data integration and for ranking uncertain query results. Our motivating application is protein function prediction. In this paper we show that: (i) explicit modeling of uncertainties as probabilities increases our ability to predict less-known or previously unknown functions (though it does not improve predicting the well-known). This suggests that probabilistic uncertainty models offer utility for scientific knowledge discovery; (ii) small perturbations in the input probabilities tend to produce only minor changes in the quality of our result rankings. This suggests that our methods are robust against slight variations in the way uncertainties are transformed into probabilities; and (iii) several techniques allow us to evaluate our probabilistic rankings efficiently. This suggests that probabilistic query evaluation is not as hard for real-world problems as theory indicates

Quasi-SLCA based Keyword Query Processing over Probabilistic XML Data

The probabilistic threshold query is one of the most common queries in uncertain databases, where a result satisfying the query must be also with probability meeting the threshold requirement. In this paper, we investigate probabilistic threshold keyword queries (PrTKQ) over XML data, which is not studied before. We first introduce the notion of quasi-SLCA and use it to represent results for a PrTKQ with the consideration of possible world semantics. Then we design a probabilistic inverted (PI) index that can be used to quickly return the qualified answers and filter out the unqualified ones based on our proposed lower/upper bounds. After that, we propose two efficient and comparable algorithms: Baseline Algorithm and PI index-based Algorithm. To accelerate the performance of algorithms, we also utilize probability density function. An empirical study using real and synthetic data sets has verified the effectiveness and the efficiency of our approaches

Combining link and content-based information in a Bayesian inference model for entity search

An architectural model of a Bayesian inference network to support entity search in semantic knowledge bases is presented. The model supports the explicit combination of primitive data type and object-level semantics under a single computational framework. A flexible query model is supported capable to reason with the availability of simple semantics in querie

Non-Compositional Term Dependence for Information Retrieval

Modelling term dependence in IR aims to identify co-occurring terms that are too heavily dependent on each other to be treated as a bag of words, and to adapt the indexing and ranking accordingly. Dependent terms are predominantly identified using lexical frequency statistics, assuming that (a) if terms co-occur often enough in some corpus, they are semantically dependent; (b) the more often they co-occur, the more semantically dependent they are. This assumption is not always correct: the frequency of co-occurring terms can be separate from the strength of their semantic dependence. E.g. "red tape" might be overall less frequent than "tape measure" in some corpus, but this does not mean that "red"+"tape" are less dependent than "tape"+"measure". This is especially the case for non-compositional phrases, i.e. phrases whose meaning cannot be composed from the individual meanings of their terms (such as the phrase "red tape" meaning bureaucracy). Motivated by this lack of distinction between the frequency and strength of term dependence in IR, we present a principled approach for handling term dependence in queries, using both lexical frequency and semantic evidence. We focus on non-compositional phrases, extending a recent unsupervised model for their detection [21] to IR. Our approach, integrated into ranking using Markov Random Fields [31], yields effectiveness gains over competitive TREC baselines, showing that there is still room for improvement in the very well-studied area of term dependence in IR