Hermann Helbig

An Integrated Approach to Semantic Evaluation and Content-Based Retrieval of Multimedia Documents

We present an overview of a large combined querying and retrieval system that performs content-based on-line searches in a large database of multimedia documents (currently text, tables and colour images). Queries are submitted as sentences in natural language and are transformed into the language of the target database. The documents are analyzed semantically for their information content; in a data fusion step the individual pieces of information extracted from these documents are aggregated into cognitively adequate result documents. There is no pre-indexing necessary when new documents are stored into the system. This retains a high degree of flexibility with respect to the questions that may be asked. It implies, however, that both huge amounts of data must be evaluated rapidly and that intelligent caching strategies must be employed. It is therefore mandatory that the system be equipped with dedicated high-speed hardware processors. The complete system is currently available as a prototype; the paper outlines its architecture and gives examples of some real sample queries in the knowledge domain of weather data documents.

Semantic interpretation of prepositions for NLP applications

The proper interpretation of prepositions is an important issue for automatic natural language understanding. We present an approach towards PP interpretation as part of a natural language understanding system which has been successfully employed in various NLP tasks for information retrieval and question answering. Our approach is based on the so-called Multi-Net paradigm, a knowledge representation formalism especially designed for the representation of natural language semantics. The paper describes how the information about the semantic interpretation of PPs is represented in the lexicon and in PP interpretation rules and how this information is used during semantic analysis. Moreover, we report on experiments that evaluate the impact of using this information about PP interpretation on the CLEF question answering task.

Logic-Based Question Answering

Question answering systems aim to provide concise and correct responses to arbitrary questions, communicating with the user in a natural language. This way they help making the knowledge of large textual sources accessible in an intuitive manner which goes beyond the capabilities of conventional search engines. In the LogAnswer project the universities of Hagen and Koblenz cooperate to build a German language question answering system which combines computational linguistics and automated reasoning to deduce answers from a knowledge base derived from Wikipedia.

Multilayered Extended Semantic Networks as a Language for Meaning Representation in NLP Systems

Multilayered Extended Semantic Networks (abbreviated: MultiNet) are one of the few knowledge representation paradigms along the line of Semantic Networks (abbreviated: SN) with a comprehensive, systematic, and publicly available documentation. In contrast to logically oriented meaning representation systems with their extensional interpretation, MultiNet is based on a use-theoretic operational semantics. MultiNet is distinguished from the afore-mentioned systems by fulfilling the criteria of homogeneity and cognitive adequacy. The paper describes the main features of MultiNet and the standard repertoire of representational means provided by this system. Besides of the structural information, which is manifested in the relational and functional connections between nodes of the semantic network, the conceptual representatives of MultiNet are characterized by embedding the nodes of the network into a multidimensional space of layer attributes. To warrant cognitive adequacy and universality of the knowledge representation system, every node of the SN uniquely represents a concept, while the relations between them have to be expressed by a predefined set of about 110 semantic primitive relations and functions. The knowledge representation language MultiNet has been used as an interface in several natural language processing systems. It is also suitable as an interlingua for machine translation systems.

LogAnswer - A Deduction-Based Question Answering System (System Description)

LogAnswer is an open domain question answering system which employs an automated theorem prover to infer correct replies to natural language questions. For this purpose LogAnswer operates on a large axiom set in first-order logic, representing a formalized semantic network acquired from extensive textual knowledge bases. The logicbased approach allows the formalization of semantics and background knowledge, which play a vital role in deriving answers. We present the functional LogAnswer prototype, which consists of automated theorem provers for logical answer derivation as well as an environment for deep linguistic processing.

Validating Meronymy Hypotheses with Support Vector Machines and Graph Kernels

There is a substantial body of work on the extraction of relations from texts, most of which is based on pattern matching or on applying tree kernel functions to syntactic structures. Whereas pattern application is usually more efficient, tree kernels can be superior when assessed by the F-measure. In this paper, we introduce a hybrid approach to extracting meronymy relations, which is based on both patterns and kernel functions. In a first step, meronymy relation hypotheses are extracted from a text corpus by applying patterns. In a second step these relation hypotheses are validated by using several shallow features and a graph kernel approach. In contrast to other meronymy extraction and validation methods which are based on surface or syntactic representations we use a purely semantic approach based on semantic networks. This involves analyzing each sentence of the Wikipedia corpus by a deep syntactico-semantic parser and converting it into a semantic network. Meronymy relation hypotheses are extracted from the semantic networks by means of an automated theorem prover, which employs a set of logical axioms and patterns in the form of semantic networks. The meronymy candidates are then validated by means of a graph kernel approach based on common walks. The evaluation shows that this method achieves considerably higher accuracy, recall, and F-measure than a method using purely shallow validation.

The Generation and Use of Layer Information in Multilayered Extended Semantic Networks

The paradigm of Multilayered Extended Semantic Networks (MultiNet) is one of the most thoroughly described knowledge representantion systems along the line of semantic networks. The conceptual representation of MultiNet is characterized by embedding its nodes into a multidimensional space of layer attributes. These layer attributes play an important part during the syntactico-semantic analysis of natural language texts and during the inferential answer finding in question answering systems. The paper demonstrates the automatic generation of complex layer information for conceptual nodes and their use in the assimilation of knowledge pieces into a larger knowledge base.

Automatic Semantic Analysis for NLP Applications

Abstract In this article, we describe a long-term enterprise at the FernUniversität in Hagen to develop systems for the automatic semantic analysis of natural language. We introduce the underlying semantic framework and give an overview of several recent activities and projects covering natural language interfaces to information providers on the web, automatic knowledge acquisition, and textual inference.

Vergleich zwischen MultiNet und anderen Wissensrepräsentationsmodellen

Keine umfassendere Arbeit uber Wissensreprasentation bzw. Wissensverarbeitung sollte ohne einen Vergleich mit anderen existierenden Systemen bleiben. Das ist bei der Fulle der vorhandenen Wissensreprasentations-Systeme (WRS) nicht ganz einfach, da einerseits die Auswahl reprasentativer Paradigmen nicht leicht fallt, und andererseits ein systematischer Vergleich der verschiedensten WRS bisher von keiner Seite durchgefuhrt wurde. Auserdem fehlt ein einheitlicher, allgemein akzeptierter Kriterienkatalog der einem solchen Vergleich zugrundegelegt werden konnte, wobei noch erschwerend hinzukommt, das viele Arbeiten auf diesem Gebiet ganz unterschiedliche Zielrichtungen verfolgen. Trotzdem soll hier ein (zugegebenermasen bruchstuckhafter) Vergleich gewagt werden, wobei fur drei wichtige Wissensreprasentationsparadigmen jeweils einige typische Arbeiten zum Vergleich herangezogen werden. Als Paradigmen wurden die folgenden ausgewahlt: (Andere) Netzwerk-orientierte WRS Logik-orientierte WRS Frame-orientierte WRS Naturlich haftet einer solchen Einteilung immer etwas Willkurliches an; trotzdem charakterisieren die gewahlten Benennungen bestimmte Grundzuge der jeweiligen WRS. Damit erhebt sich auch die Frage nach der Fairness des angestrebten Vergleichs, denn wie will man eine starker theoretisch orientierte Arbeit einer anwendungsorientierten angemessen gegenuberstellen, oder die — wie wir meinen — bessere kognitive und linguistische Fundierung von MultiNet gegen die tiefere logische Durchdringung von Logik-orientierten Systemen abwagen, wobei letztere wiederum eine nicht so breite Abdeckung der sprachlichen Erscheinungen aufweisen.

Automatic Generation of Large Knowledge Bases using Deep Semantic and Linguistically Founded Methods

Automatic knowledge acquisition from texts is one of the challenges of the information society that can only be mastered by technical means. While the syntactic analysis of isolated sentences is relatively well understood, the problem of automatically parsing on all linguistic levels, starting from the morphological level through to the semantic level, i.e. real understanding of texts,is far from being solved. This paper explains the approach taken in this direction by the MultiNet technology in bridging the gap between the syntactic-semantic analysis of single sentences and the creation of knowledge bases representing the content of whole texts. In particular, it is shown how linguistic text phenomena like inclusion or bridging references can be dealt with by logical means using the axiomatic apparatus of the MultiNet formalism. The NLP techniques described are practically applied in transforming large textual corpora like Wikipedia into a knowledge base and using the latter in meaning-oriented search engines.