Ontology driven Information Extraction
In this paper, we present an ontology driven information extraction system for pre analysis of military texts such as intelligence reports. We discuss the techniques used in this system, which are "Information Extraction" (IE) "Semantic Role Labeling" (SRL) and the application of different kinds of ontologies for knowledge representation. As a conclusion we show how a system that integrates all these techniques can be used in the process of automatic threat recognition. MOTIVATION Information advantage is a key success factor for all types of military operations. If the commander has comprehensive information about her own forces, about the current situation of the enemy and about other relevant conditions, she can draw the correct conclusions and make more specific decisions in shorter time. However, time is not always a critical factor. Especially terrorist threats develop within longer periods of time from planning to execution. Information about these kinds of hostile operations often is hidden in a huge amount of sensor data, SIGINT data, HUMINT reports, and open sources. This huge amount of heterogeneous data can no longer be processed solely by human reconnaissance specialists. It has to be pre processed by automatic means in order to reduce the number of items the human experts need to check [23] In the ideal case, systems proactively lead the user to those pieces of information that are relevant for his specific problem and support him in his manual search. The user then can analyze this promising information to find the solution for his problem. Computational linguistics offers approaches that, when combined, allow a potent and precise tool for automatic text analysis and threat recognition. In particular, text analysis systems can be augmented by adding ontology modules. The automated solution of sophisticated problems requires a certain "intelligence" of the systems. A necessary requirement for this is knowledge that is machine readable and processable. With the help of ontologies, it is possible to describe machine readable and processable knowledge at semantic level, i.e. to model the meaning of the represented facts. Furthermore, the use of ontologies has several advantages: It allows sharing a common understanding about the information structure and a clear common definition of the data to be processed. It also enables interoperability in disparate system environments as well as machine readability [25] The paper is structured as follows: First, we will provide a short overview on ontologies in general. Then, we will discuss the process of information extraction and its processing steps. The final step of information extraction is semantic role labeling. In our system, semantic role labeling relies on knowledge represented in ontologies. Thus, we have divided our discussion of information extraction in two sections, the processing steps up to semantic role labeling and semantic role labeling itself. Information extraction Ontology driven Information Extraction PAPER NBR 2 NATO RTO IST 097 calculates a formal representation of a test's content. In addition to presenting the IE process, we also will show how its results can be used in automated threat recognition. This application also relies on knowledge. Interestingly, the same ontologies that support information extraction can be used to enable automated threat recognition. ONTOLOGIES According to Gruber [9] an ontology is an "explicit specification of a conceptualization" where conceptualization is defined as "an abstract, simplified view of the world that we wish to represent for some purpose" Common knowledge often is taken granted by humans but nevertheless has to be represented so that a system can use it. In order to stress this point, Borst [6] modified the definition of Gruber, by adding the modifier "shared" to "conceptualization" and by substituting "explicit" by "formal" so that Borst proposes the definition: "An ontology is a formal specification of a shared conceptualization." In contrast to philosophy where ontology is the science of being as such [19] the definitions by Gruber and Borst refer to "ontology" as used in information science. Here, ontologies usually consist of a defined basic vocabulary structured as taxonomy. This means that the terms refer to concepts and are put into a tree structure for which the structuring relation is the hyponymy relation "ISA" is a) This relation is chosen because it allows inheritance. From the implementation point of view, the concepts are classes. For example, there might be the class "car" in the ontology as subclass of "vehicle." These classes might have instances denoting specific objects of the real world, specific cars in our case. Often, the instances are stored in a data base. In addition to the taxonomic structure, the classes are augmented with attributes as well as relations that hold among them. For example, the class "vehicle" might have attributes like "color" or "max speed" which are inherited to "vehicle's" subclass "car" An instance of class "car" then has a value for the attribute "color" which has to be a color denotation, e.g. "white" Another example might be the relation "owner" by which for example instances of "vehicle" can be linked to instances of "organization" or "person" In the following, we will discuss the process of information extraction by which texts, written in natural languages like English, can be analyzed automatically. The final step of this process is semantic role labeling. We will show how ontologies support the step of semantic role labeling. Before that, however, we will take a look at those steps that precede semantic role labeling. INFORMATION EXTRACTION For automatic information extraction (IE) we developed an application called MIETER (Military Information Extraction from Texts and their Electronic Representation) [10] We use this application with the open source tool GATE (A General Architecture for Text Engineering) provided by the University of Sheffield [2, 7] GATE offers a toolbox to build IE processing pipelines. These pipelines can easily be customized according to one's needs which is what we have done developing MIETER. An IE processing pipeline consists at least of the following processing modules: tokenizer, gazetteer, sentence splitter, part of speech tagger, named entity recognizer, and a chunker [3] The tokenizer determines individual tokens of the text, i.e. single words, numbers, abbreviations, and punctuation marks. The gazetteer then compares the tokens to elements of several lists which contain names of various types such as person and organization names, names for countries, cities, rivers and the like. Tokens matching an element in one of the lists will be annotated with the respective type, e.g. the token "Kabul" might be tagged as type location, subtype city. Once the tokenization is done, sentences need to be determined by the sentence splitter. Next, the part of speech tagger (POS tagger) Ontology driven Information Extraction NATO RTO IST 097 PAPER NBR 3 assigns the word tokens their respective syntactic categories (e.g. for "the patrol stops" "the" has to be tagged as determiner, "patrol" as noun and "stops" as verb) On the basis of the annotations produced, the recognizer for named entities and the chunker identify the constituents within the sentences. The recognizer for named entities combines elements annotated by the gazetteer. For example, for the sequence "Dr. Mohammed el Baradei" the recognizer will annotate the whole sequence with the tags person and noun phrase according to its rules. The chunker operates on the tags provided by the POS tagger and by the recognizer for named entities. For example, in "The patrol moves towards the airport" "the" was previously labeled determiner and "patrol" as well as "airport" were labeled noun. The chunker recognizes a sequence of a determiner and a noun as noun phrase. So "the patrol" and "the airport" are annotated noun phrase. In addition, "towards" is labeled preposition. A preposition followed by a noun phrase, like "towards the airport" constitutes a prepositional phrase. SEMANTIC ROLE LABELING The module for semantic role labeling (SRL) is attached at the end of the IE pipeline to assign semantic roles to the constituents calculated in the preceding processing steps. In our example, SRL will assign theme to "the patrol" and direction to "towards the airport" SRL starts by identifying the verb group within each sentence and the main verb within the verb group. For example, in the verb group "had been sold" the main verb is "sell" That verb is stored and looked up in a specific verb ontology we developed for this process. This ontology provides information about verbs, among it their semantic frames. Semantic frames, originating from Fillmore's case grammar [8] tell us which semantic roles come with the verb in question and which of these roles are mandatory, optional or forbidden. For example, a verb that denotes an action that involves a movement, such as "move" "advance" or "withdraw" is compatible with the spatial semantic roles origin, path and destination or direction (e.g. The company advanced from Dowdall's Tavern via the Orange Turnpike towards Chancellorsville" Thus origin, path, direction and destination are in the frames of "move" "advance" and "withdraw" The verb ontology is a specific module of our system. Often, ontologies refer to object terms only in order to represent knowledge. For example, we explained the structure of an ontology by referring to the concepts of "car" and "vehicle" above. However, not only object terms can be put in an ontology but also action and event terms which are verbs. Although it is not as for object terms, verbs also can be taxonomized. For example, "buy" and "steal" denote actions that can be subsumed under "procure" actions. We created our verb ontology based on FrameNet [1, 17] a