We address the problem of semantic querying of relational databases (RDB) modulo knowledge bases using very expressive knowledge representation formalisms, such as full first-order logic or its various fragments. We propose to use a first-order logic (FOL) reasoner for computing schematic answers to deductive queries, with the subsequent instantiation of these schematic answers using a conventional relational DBMS. In this research note, we outline the main idea of this technique -- using abstractions of databases and constrained clauses for deriving schematic answers. The proposed method can be directly used with regular RDB, including legacy databases. Moreover, we propose it as a potential basis for an efficient Web-scale semantic search technology.
The work presented here was originally inspired by the XSTONE project @cite_2 . In XSTONE, a resolution-based theorem prover (a reimplementation of Gandalf, which is, in particular, optimised for taxonomic reasoning) is integrated with an RDBMS by loading rows from a database as ground facts into the reasoner and using them to answer queries with resolution. The system is highly scalable in terms of expressiveness: it accepts full FOL with some useful extensions, and also has parsers for RDF, RDFS and OWL. We believe that our approach has better data scalability and can cope with very large databases which are beyond the reach of XSTONE, mostly because our approach obtains answers in bulk, and also due to the way we use highly-optimised RDBMS.
@cite_2
1870380066 1756261662 2138204945 2045495924
Triple stores implementing the RL profile of OWL 2 are becoming increasingly popular. In contrast to unrestricted OWL 2, the RL profile is known to enjoy favourable computational properties for query answering, and state-of-the-art RL reasoners such as OWLim and Oracle's native inference engine of Oracle Spatial and Graph have proved extremely successful in industry-scale applications. The expressive restrictions imposed by OWL 2 RL may, however, be problematical for some applications. In this paper, we propose novel techniques that allow us (in many cases) to compute exact query answers using an off-the-shelf RL reasoner, even when the ontology is outside the RL profile. Furthermore, in the cases where exact query answers cannot be computed, we can still compute both lower and upper bounds on the exact answers. These bounds allow us to estimate the degree of incompleteness of the RL reasoner on the given query, and to optimise the computation of exact answers using a fully-fledged OWL 2 reasoner. A preliminary evaluation using the RDF Semantic Graph feature in Oracle Database has shown very promising results with respect to both scalability and tightness of the bounds. The QL profile of OWL 2 has been designed so that it is possible to use database technology for query answering via query rewriting. We present a comparison of our resolution based rewriting algorithm with the standard algorithm proposed by , implementing both and conducting an empirical evaluation using ontologies and queries derived from realistic applications. The results indicate that our algorithm produces significantly smaller rewritings in most cases, which could be important for practicality in realistic applications. A number of text mining and information extraction projects such as Text Runner and NELL seek to automatically build knowledge bases from the rapidly growing amount of information on the web. In order to scale to the size of the web, these projects often employ ad hoc heuristics to reason about uncertain and contradictory information rather than reasoning jointly about all candidate facts. In this paper, we present a Markov logic-based system for cleaning an extracted knowledge base. This allows a scalable system such as NELL to take advantage of joint probabilistic inference, or, conversely, allows Markov logic to be applied to a web scale problem. Our system uses only the ontological constraints and confidence values of the original system, along with human-labeled data if available. The labeled data can be used to calibrate the confidence scores from the original system or learn the effectiveness of individual extraction patterns. To achieve scalability, we introduce a neighborhood grounding method that only instantiates the part of the network most relevant to the given query. This allows us to partition the knowledge cleaning task into tractable pieces that can be solved individually. In experiments on NELL's knowledge base, we evaluate several variants of our approach and find that they improve both F1 and area under the precision-recall curve. Harvesting relational facts from Web sources has received great attention for automatically constructing large knowledge bases. Stateof-the-art approaches combine pattern-based gathering of fact candidates with constraint-based reasoning. However, they still face major challenges regarding the trade-offs between precision, recall, and scalability. Techniques that scale well are susceptible to noisy patterns that degrade precision, while techniques that employ deep reasoning for high precision cannot cope with Web-scale data. This paper presents a scalable system, called PROSPERA, for high-quality knowledge harvesting. We propose a new notion of ngram-itemsets for richer patterns, and use MaxSat-based constraint reasoning on both the quality of patterns and the validity of fact candidates.We compute pattern-occurrence statistics for two benefits: they serve to prune the hypotheses space and to derive informative weights of clauses for the reasoner. The paper shows how to incorporate these building blocks into a scalable architecture that can parallelize all phases on a Hadoop-based distributed platform. Our experiments with the ClueWeb09 corpus include comparisons to the recent ReadTheWeb experiment. We substantially outperform these prior results in terms of recall, with the same precision, while having low run-times.