Project-Team MOSTRARE Modeling Tree Structures, Machine Learning, and Information Extraction

The evolution of XML into the major document exchange format has reawaken a strong interest in modeling tree structures. The main objectives are to query for nodes in trees ( XPath ), to validity of documents, i.e., membership to a set of valid trees ( DTD ), and to transform trees into others ( XSLT ). Modelling approaches rely on tree automata, monadic second-order logics, modal logics, pattern languages, attribute grammars, or tree transducers. The main goal of the project is to design adaptive information extraction systems that fully exploit the tree structures of XML or HTML documents. In our approach we want to combine novel models of tree structures and machine learning techniques. Appropriate languages for modeling node queries in tree satisfy a number of properties required for adaptive information extraction. Possible trade-offs between expressiveness, learnability, and efﬁciency are to be understood. We thus propose new query languages in trees and investigate them from these perspectives. We consider tree automata for ranked and unranked trees, that may be ordered or unordered. We design efﬁcient algorithms for querying semi-structured data by automata. We investigate logical query languages such as monadic Datalog, monadic second-order logic, and modal logical languages. of is are mediator services, in information retrieval and in mining is wrappers are in Business Intelligence and Knowledge Management. we have proved decidable. Future works include a study of learnability of n-ary queries. In [17], we present node selecting tree transducer (NSTT) for representing monadic queries. NSTTs are the query formalism underlying our Squirrel tool. We have shown that deterministic NSTTs can be identiﬁed with monadic queries by unambiguous tree automata. In [18], we propose stepwise tree automata for querying unranked trees equally to binary trees. Stepwise tree automata are traditional tree automata. Stepwise tree automata simplify the often-needed query transfer ranked to unranked trees. This comes with their algebra nature. We have investigated related logics and constraints in ordered trees that are relevant to subtyping in programming languages [12][24] and for underspeciﬁed semantics of natural language [14]. In [15], we investigate an alternative modal logical querying formalism, the spatial logic TQL proposed by Cardelli and Ghelli in the context of the ambient calculus. We exhaustively study combined and data complexity of the model checking problem of TQL and its fragments, and equally investigate satisﬁability. We characterize fragments of TQL that have the same expressive power as MSO. In [27][26], we link TQL to languages with counting constraints such as Presburger monadic second-order logic (PMSO) introduced by Seidl and Schwentick or counting monadic second-order logic (CMSO) introduced by Courcelle in a uniform tree automata framework (see also [11]). sets C of path inclusions p (cid:22) u where p is a regular set of paths and u is a singleton path, which have a ﬁnite exact model. We present an effective way of computing such a model when it exists. (see also [19]). When considering heterogeneous HTML and XML documents as inputs of information extraction tasks – that may be either scattered into pieces or hidden in large parts of purely textual data – different tree wrappers need to be combined with textual wrappers. For classiﬁcation tasks, some machine learning algorithms realize such combinations. We have started to examine how such methods for information extraction. In a ﬁrst step, we reformulate information extraction as classiﬁcation tasks. Second we want to apply known techniques and combine them with structural wrapper induction. The CafeIn framework ( [31],[23]) improve previous approaches based on supervised classiﬁcation for information extraction ( [30], [29]). CafeIn is a single-slot wrapper induction framework for text data or semi-structured data. It combines an adaptive attribute-value representation of documents and a supervised classiﬁcation algorithm. For text data, the classiﬁcation task consists in deciding whether a tuple of two positions in a text are respectively the beginning and the end of data to extract. The document representation use only textual features. For semi-structured data, like XML or HTML trees, data to extract are contained in leaves. Thus the classiﬁcation task consists in deciding whether a leaf is to be extracted or not. In CafeIn, the attribute-value representation of the data is adaptive because it is based on different feature-sets, and it allows the integration of domain knowledge easily. Any supervised classiﬁcation algorithm can be used in the CafeIn framework.Asmentioned above, we have developed a CafeIn prototype and examined its performance with different learning algorithms for classiﬁcation (C4.5 [33], GloBoost [25]) and with different models of data representation. CafeIn is competitive with the others wrapper induction systems based on specialized learning algorithms. It will be continued with the combination of textual and structural informations, and with multi-slots wrappers induction.