The KB paradigm and its application to interactive configuration

The knowledge base paradigm aims to express domain knowledge in a rich formal language, and to use this domain knowledge as a knowledge base to solve various problems and tasks that arise in the domain by applying multiple forms of inference. As such, the paradigm applies a strict separation of concerns between information and problem solving. In this paper, we analyze the principles and feasibility of the knowledge base paradigm in the context of an important class of applications: interactive configuration problems. In interactive configuration problems, a configuration of interrelated objects under constraints is searched, where the system assists the user in reaching an intended configuration. It is widely recognized in industry that good software solutions for these problems are very difficult to develop. We investigate such problems from the perspective of the KB paradigm. We show that multiple functionalities in this domain can be achieved by applying different forms of logical inferences on a formal specification of the configuration domain. We report on a proof of concept of this approach in a real-life application with a banking company. To appear in Theory and Practice of Logic Programming (TPLP).Comment: To appear in Theory and Practice of Logic Programming (TPLP

Context Sensitive Search String Composition Algorithm using User Intention to Handle Ambiguous Keywords

Finding the required URL among the first few result pages of a search engine is still a challenging task. This may require number of reformulations of the search string thus adversely affecting user's search time. Query ambiguity and polysemy are major reasons for not obtaining relevant results in the top few result pages. Efficient query composition and data organization are necessary for getting effective results. Context of the information need and the user intent may improve the autocomplete feature of existing search engines. This research proposes a Funnel Mesh-5 algorithm (FM5) to construct a search string taking into account context of information need and user intention with three main steps 1) Predict user intention with user profiles and the past searches via weighted mesh structure 2) Resolve ambiguity and polysemy of search strings with context and user intention 3) Generate a personalized disambiguated search string by query expansion encompassing user intention and predicted query. Experimental results for the proposed approach and a comparison with direct use of search engine are presented. A comparison of FM5 algorithm with K Nearest Neighbor algorithm for user intention identification is also presented. The proposed system provides better precision for search results for ambiguous search strings with improved identification of the user intention. Results are presented for English language dataset as well as Marathi (an Indian language) dataset of ambiguous search strings.

Efficient index structures for and applications of the CompleteSearch engine

Traditional search engines, such as Google, offer response times well under one second, even for a corpus with more than a billion documents. They achieve this by making use of a (parallelized) inverted index. However, the inverted index is primarily designed to efficiently process simple key word queries, which is why search engines rarely offer support for queries which cannot be (re-)formulated in this manner, possibly using "special key words';. We have contrived data structures for the CompleteSearch engine, a search engine, developed at the Max-Planck Institute for Computer Science, which supports a far greater set of query types, without sacrificing the efficiency. It is built on top of a context-sensitive prefix search and completion mechanism. This mechanism is, on the one hand, simple enough to be efficiently realized by appropriate algorithms, and, on the other hand, powerful enough to be employed to support additional query types. We present two new data structures, which can be used to solve the underlying prefix search and completion problem. The first one, called AutoTree, has the theoretically desirable property that, for non-degenerate corpora and queries, its running time is proportional to the sum of the sizes of the input and output. The second one, called HYB, focuses on compressibility of the data and is optimized for scenarios, where the index does not fit in main memory but resides on disk. Both beat the baseline algorithm, using an inverted index, by a factor of 4-10 in terms of average processing time. A direct head-to-head comparison shows that, in a general setting, HYB outperforms AutoTree. Thanks to the HYB data structure, the CompleteSearch engine efficiently supports features such as faceted search for categorical information, completion to synonyms, support for basic database style queries on relational tables and the efficient search of ontologies. For each of these features, we demonstrate the viability of our approach through experiments. Finally, we also prove the practical relevance of our work through a small user study with employees of the helpdesk of our institute.Typische Suchmaschinen, wie z.B. Google, erreichen Antwortzeiten deutlich unter einer Sekunde, selbst für einen Korpus mit mehr als einer Milliarde Dokumenten. Sie schaffen dies durch die Nutzung eines (parallelisierten) invertierten Index. Da der invertierte Index jedoch hauptsächlich für die Bearbeitung von einfachen Schlagwortsuchen konzipiert ist, bieten Suchmaschinen nur selten die Möglichkeit, komplexere Anfragen zu beantworten, die sich nicht in solch eine Schlagwortsuche umformulieren lassen, u.U. mit der Zurhilfenahme von speziellen Kunstworten. Wir haben für die CompleteSearch Suchmaschine, konzipiert und implementiert am Max-Planck-Institut für Informatik, spezielle Datenstrukturen entwickelt, die ein deutlich größeres Spektrum an Anfragetypen unterstützen, ohne dabei die Effizienz zu opfern. Die CompleteSearch Suchmaschine baut auf einem kontext-sensitiven Präfixsuch- und Vervollständigungsmechanismus auf. Dieser Mechanismus ist einerseits einfach genug, um eine effiziente Implementierung zu erlauben, andererseits hinreichend mächtig, um die Bearbeitung zusätzlicher Anfragetypen zu erlauben. Wir stellen zwei neue Datenstrukturen vor, die eingesetzt werden können, um das zu Grunde liegende Präfixsuch und Vervollstängigungsproblem zu lösen. Die erste der beiden, AutoTree genannt, hat die theoretisch wünschenswerte Eigenschaft, dass sie für nicht entartete Korpora eine Bearbeitungszeit linear in der aufsummierten Größe der Ein- und Ausgabe zulässt. Die zweite, HYB genannt, ist auf die Komprimierbarkeit der Daten ausgelegt und ist für Szenarien optimiert, in denen der Index nicht in den Hauptspeicher passt, sondern auf der Festplatte ruht. Beide schlagen den Referenzalgorithmus, der den invertierten Index benutzt, um einen Faktor von 4-10 hinsichtlich der durchschnittlichen Bearbeitungszeit. Ein direkter Vergleich zeigt, dass im Allgemeinen HYB schneller ist als AutoTree. Dank der HYB Datenstruktur kann die CompleteSearch Suchmaschine auch anspruchsvollere Anfragetypen, wie Facettensuche für Kategorieninformation, Vervollständigung zu Synonymen, Anfragen im Stile von elementaren, relationalen Datenbankanfragen und die Suche auf Ontologien, effizient bearbeiten. Für jede dieser Fähigkeiten beweisen wir die Realisierbarkeit unseres Ansatzes durch Experimente. Schließlich demonstrieren wir durch eine kleine Nutzerstudie mit Mitarbeitern des Helpdesks unseres Institutes auch den praktischen Nutzen unserer Arbeit