The MUSEEC (MUltilingual SEntence Extraction and Compression) summarization tool implements several extractive summarization techniques-at the level of complete and compressed sentences-that can be applied, with some minor adaptations, to documents in multiple languages. The current version of MUSEEC provides the following summarization methods: (1) MUSE-a supervised summarizer, based on a genetic algorithm (GA), that ranks document sentences and extracts top-ranking sentences into a summary, (2) POLY-an unsupervised summarizer, based on linear programming (LP), that selects the best extract of document sentences, and (3) WECOM-an unsupervised extension of POLY that compiles a document summary from compressed sentences. In this paper, we provide an overview of MUSEEC methods and its architecture in general.

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Searching for RNA sequence-structure patterns is becoming an essential tool for RNA practitioners. Novel discoveries of regulatory non-coding RNAs in targeted organisms and the motivation to find them across a wide range of organisms have prompted the use of computational RNA pattern matching as an enhancement to sequence similarity. State-of-the-art programs differ by the flexibility of patterns allowed as queries and by their simplicity of use. In particular—no existing method is available as a user-friendly web server. A general program that searches for RNA sequence-structure patterns is RNA Structator. However, it is not available as a web server and does not provide the option to allow flexible gap pattern representation with an upper bound of the gap length being specified at any position in the sequence. Here, we introduce RNAPattMatch, a web-based application that is user friendly and makes sequence/structure RNA queries accessible to practitioners of various background and proficiency. It also extends RNA Structator and allows a more flexible variable gaps representation, in addition to analysis of results using energy minimization methods. RNAPattMatch service is available at http://www.cs.bgu.ac.il/rnapattmatch. A standalone version of the search tool is also available to download at the site.

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A qualitative logic program (QLP) is a logic program where a dominance is associated with each rule in the program. The intuition is that rules with higher dominance are more plausible or more reliable. Literals in the answer sets of QLPs are also annotated with weights, with the intuition that a literal with a higher weight is more likely to be true. We present three different applications of QLPs: Ontology Matching, Ranking of search results and Inheritance Networks. We also address the problem of computing answer sets of QLPs. We define a property of QLPs called "monotonicity" and show that answer sets of monotonic QLPs can be computed in an "anytime" fashion, such that the literals are produced by a descending order of their dominance. We then present an application of QLP called LPmatch, which is a tool for ontology matching. LPmatch is a simple matcher composed of only nineteen logic programming rules. The most significant advantage of LPmatch is its flexibility as it can handle new domains for ontology matching instantly just by adding new rules. Comparisons with other existing tools show that LPmatch is a top performer. on the semantic web (see for example [10]). Ontology matching has emerged as a crucial step when information sources are being integrated, such as when companies are being merged and their corresponding knowledge bases are to be united. However, with the rapid growth of information sources, manual ontology matching has become tedious and time-consuming. To address this issue, various automated ontology matching systems have been developed and an Ontology Alignment Evaluation Initiative (OAEI), which organizes evaluation campaigns aiming at evaluating ontology matching technologies, was formed (see http://oaei.ontologymatching.org). Among the different approaches, we would like to mention the probabilistic approach (see [2,18]) on the one hand, and the logic and rule-based approach [25] on the other hand.In order to demonstrate the potential of QLPs, we have developed LPmatch, a tool for ontology matching that is based on Qualitative Logic Programs (QLPs). LPmatch generally works in three phases. The first phase uses a first-line matcher and produces an initial Rule 19: Properties P1 and P2 are matched if class C1 defines restriction of type T on property P1, class C2 defines restriction of same type T on property P2 and classes C1, C2 are matched.